From 0161ae4a65431845f39aa7a0eeb67d2949361ed6 Mon Sep 17 00:00:00 2001
From: johnxie <john@taskade.com>
Date: Tue, 24 Feb 2026 07:56:16 -0800
Subject: [PATCH 1/2] docs: deepen tutorial chapters batch 2 (copilot-cli
 through llama-factory)

---
 .../01-getting-started.md                     | 601 +++++++++++++++++
 .../02-authentication-and-access-policies.md  | 610 +++++++++++++++++
 ...interactive-workflow-and-approval-model.md | 610 +++++++++++++++++
 ...els-experimental-features-and-autopilot.md | 598 +++++++++++++++++
 .../05-mcp-and-lsp-extension-points.md        | 610 +++++++++++++++++
 .../06-github-native-context-workflows.md     | 610 +++++++++++++++++
 .../07-installation-and-update-channels.md    | 598 +++++++++++++++++
 ...-production-governance-and-team-rollout.md | 609 +++++++++++++++++
 .../copilotkit-tutorial/01-getting-started.md |  52 +-
 .../copilotkit-tutorial/02-app-context.md     |  53 +-
 .../copilotkit-tutorial/03-copilot-actions.md |  53 +-
 .../copilotkit-tutorial/04-chat-components.md |  53 +-
 .../copilotkit-tutorial/05-generative-ui.md   |  53 +-
 tutorials/copilotkit-tutorial/06-coagents.md  |  53 +-
 .../copilotkit-tutorial/07-human-in-loop.md   |  53 +-
 .../copilotkit-tutorial/08-production.md      |  50 ++
 ...etting-started-and-scaffolding-workflow.md | 601 +++++++++++++++++
 ...rated-project-structure-and-conventions.md | 598 +++++++++++++++++
 ...rchitecture-resources-prompts-and-tools.md | 610 +++++++++++++++++
 ...4-runtime-dependencies-and-uv-packaging.md | 610 +++++++++++++++++
 ...ntegration-claude-desktop-and-inspector.md | 598 +++++++++++++++++
 ...06-customization-and-extension-patterns.md | 598 +++++++++++++++++
 ...ity-security-and-contribution-workflows.md | 610 +++++++++++++++++
 ...atus-migration-and-long-term-operations.md | 597 +++++++++++++++++
 ...01-getting-started-and-scaffolding-flow.md | 604 +++++++++++++++++
 ...-generated-structure-and-build-pipeline.md | 613 +++++++++++++++++
 ...-mcp-primitives-resources-tools-prompts.md | 613 +++++++++++++++++
 ...04-configuration-metadata-and-packaging.md | 613 +++++++++++++++++
 ...elopment-workflows-build-watch-and-link.md | 601 +++++++++++++++++
 .../06-debugging-and-local-integration.md     | 613 +++++++++++++++++
 ...ity-security-and-contribution-practices.md | 613 +++++++++++++++++
 ...status-migration-and-long-term-strategy.md | 600 +++++++++++++++++
 .../crewai-tutorial/01-getting-started.md     |  45 ++
 tutorials/crewai-tutorial/02-agent-roles.md   |  49 ++
 tutorials/crewai-tutorial/03-task-planning.md |  49 ++
 .../crewai-tutorial/04-tool-integration.md    |  49 ++
 .../crewai-tutorial/05-crew-communication.md  |  49 ++
 .../crewai-tutorial/06-process-management.md  |  49 ++
 .../crewai-tutorial/07-advanced-patterns.md   |  49 ++
 .../08-production-deployment.md               |  48 ++
 .../crush-tutorial/01-getting-started.md      | 577 ++++++++++++++++
 .../02-architecture-and-session-model.md      | 586 +++++++++++++++++
 .../03-providers-and-model-configuration.md   | 574 ++++++++++++++++
 .../04-permissions-and-tool-controls.md       | 598 +++++++++++++++++
 .../05-lsp-and-mcp-integration.md             | 578 ++++++++++++++++
 ...lls-commands-and-workflow-customization.md | 586 +++++++++++++++++
 .../07-logs-debugging-and-operations.md       | 598 +++++++++++++++++
 .../08-production-governance-and-rollout.md   | 585 +++++++++++++++++
 .../daytona-tutorial/01-getting-started.md    | 606 +++++++++++++++++
 ...sandbox-lifecycle-resources-and-regions.md | 607 +++++++++++++++++
 .../03-process-and-code-execution-patterns.md | 607 +++++++++++++++++
 .../04-file-git-and-preview-workflows.md      | 607 +++++++++++++++++
 .../05-mcp-agent-integration-and-tooling.md   | 607 +++++++++++++++++
 ...configuration-api-and-deployment-models.md | 607 +++++++++++++++++
 ...07-limits-network-controls-and-security.md | 607 +++++++++++++++++
 ...-production-operations-and-contribution.md | 606 +++++++++++++++++
 .../deer-flow-tutorial/01-getting-started.md  |  55 ++
 .../deer-flow-tutorial/02-workflow-basics.md  |  59 ++
 .../deer-flow-tutorial/03-task-management.md  |  59 ++
 .../deer-flow-tutorial/04-dependencies.md     |  59 ++
 .../deer-flow-tutorial/05-error-handling.md   |  59 ++
 tutorials/deer-flow-tutorial/06-scaling.md    |  59 ++
 tutorials/deer-flow-tutorial/07-monitoring.md |  59 ++
 .../08-advanced-patterns.md                   |  58 ++
 .../01-system-overview.md                     |  50 +-
 .../02-core-architecture.md                   |  51 +-
 .../03-workflow-engine.md                     |  51 +-
 .../04-rag-implementation.md                  |  51 +-
 .../05-agent-framework.md                     |  51 +-
 .../06-custom-nodes.md                        |  51 +-
 .../07-production-deployment.md               |  51 +-
 .../08-operations-playbook.md                 |  48 ++
 tutorials/dspy-tutorial/01-getting-started.md |  52 +-
 tutorials/dspy-tutorial/02-signatures.md      |  53 +-
 tutorials/dspy-tutorial/03-modules.md         |  53 +-
 tutorials/dspy-tutorial/04-rag.md             |  53 +-
 tutorials/dspy-tutorial/05-optimization.md    |  53 +-
 .../dspy-tutorial/06-advanced-patterns.md     |  53 +-
 tutorials/dspy-tutorial/07-evaluation.md      |  53 +-
 tutorials/dspy-tutorial/08-production.md      |  52 +-
 tutorials/dyad-tutorial/01-getting-started.md | 496 ++++++++++++++
 .../02-natural-language-building.md           | 440 +++++++++++++
 .../dyad-tutorial/03-component-integration.md | 488 ++++++++++++++
 tutorials/dyad-tutorial/04-data-management.md | 512 +++++++++++++++
 tutorials/dyad-tutorial/05-api-integration.md |  53 ++
 .../dyad-tutorial/06-customization-styling.md |  53 ++
 .../dyad-tutorial/07-testing-validation.md    |  53 ++
 .../dyad-tutorial/08-deployment-sharing.md    |  52 ++
 .../elizaos-tutorial/01-getting-started.md    |  48 ++
 .../elizaos-tutorial/02-agent-runtime.md      |  49 ++
 .../elizaos-tutorial/03-character-system.md   |  49 ++
 .../04-plugin-architecture.md                 |  49 ++
 tutorials/elizaos-tutorial/05-memory-rag.md   |  49 ++
 .../06-platform-connectors.md                 |  49 ++
 tutorials/elizaos-tutorial/07-multi-agent.md  |  49 ++
 .../08-production-deployment.md               |  48 ++
 .../01-getting-started.md                     | 589 +++++++++++++++++
 .../02-architecture-and-component-topology.md | 598 +++++++++++++++++
 ...3-installation-modes-and-rules-strategy.md | 598 +++++++++++++++++
 ...agents-skills-and-command-orchestration.md | 598 +++++++++++++++++
 ...hooks-mcp-and-continuous-learning-loops.md | 598 +++++++++++++++++
 ...-platform-workflows-cursor-and-opencode.md | 598 +++++++++++++++++
 ...esting-verification-and-troubleshooting.md | 598 +++++++++++++++++
 ...08-contribution-workflow-and-governance.md | 585 +++++++++++++++++
 .../fabric-tutorial/01-getting-started.md     |  51 ++
 .../fabric-tutorial/02-pattern-system.md      |  55 ++
 tutorials/fabric-tutorial/03-basic-usage.md   |  55 ++
 .../fabric-tutorial/04-advanced-patterns.md   |  55 ++
 .../fabric-tutorial/05-stitch-composition.md  |  55 ++
 .../fabric-tutorial/06-custom-patterns.md     |  55 ++
 .../fabric-tutorial/07-integration-api.md     |  55 ++
 .../08-enterprise-deployment.md               |  54 ++
 .../fastmcp-tutorial/01-getting-started.md    | 606 +++++++++++++++++
 ...actions-components-providers-transforms.md | 607 +++++++++++++++++
 .../03-server-runtime-and-transports.md       | 607 +++++++++++++++++
 ...ent-architecture-and-transport-patterns.md | 607 +++++++++++++++++
 ...grations-claude-code-cursor-and-tooling.md | 607 +++++++++++++++++
 .../06-configuration-auth-and-deployment.md   | 607 +++++++++++++++++
 ...sting-contributing-and-upgrade-strategy.md | 607 +++++++++++++++++
 ...08-production-operations-and-governance.md | 606 +++++++++++++++++
 .../01-getting-started.md                     | 598 +++++++++++++++++
 ...02-architecture-and-context-translation.md | 611 +++++++++++++++++
 .../03-frame-targeting-and-context-scope.md   | 607 +++++++++++++++++
 ...patterns-for-one-shot-ui-implementation.md | 607 +++++++++++++++++
 .../05-mcp-client-integrations.md             | 619 ++++++++++++++++++
 .../06-performance-and-token-optimization.md  | 607 +++++++++++++++++
 ...07-team-workflows-and-design-governance.md | 619 ++++++++++++++++++
 .../08-production-security-and-operations.md  | 606 +++++++++++++++++
 .../01-getting-started-and-core-setup.md      | 598 +++++++++++++++++
 ...-architecture-transports-and-versioning.md | 595 +++++++++++++++++
 ...lection-scrape-map-crawl-search-extract.md | 595 +++++++++++++++++
 ...egrations-cursor-claude-windsurf-vscode.md | 607 +++++++++++++++++
 ...iguration-retries-and-credit-monitoring.md | 595 +++++++++++++++++
 ...rkflows-deep-research-and-api-evolution.md | 607 +++++++++++++++++
 ...lity-observability-and-failure-handling.md | 607 +++++++++++++++++
 ...ty-governance-and-contribution-workflow.md | 594 +++++++++++++++++
 .../firecrawl-tutorial/01-getting-started.md  |  45 ++
 .../firecrawl-tutorial/02-basic-scraping.md   |  49 ++
 .../03-advanced-extraction.md                 |  49 ++
 .../04-javascript-handling.md                 |  49 ++
 .../firecrawl-tutorial/05-content-cleaning.md |  49 ++
 .../firecrawl-tutorial/06-rag-integration.md  |  49 ++
 .../07-scaling-performance.md                 |  49 ++
 .../08-production-deployment.md               |  48 ++
 .../fireproof-tutorial/01-getting-started.md  | 589 +++++++++++++++++
 ...2-core-document-api-and-query-lifecycle.md | 598 +++++++++++++++++
 .../03-react-hooks-and-live-local-first-ux.md | 598 +++++++++++++++++
 .../04-ledger-crdt-and-causal-consistency.md  | 610 +++++++++++++++++
 .../05-storage-gateways-and-sync-topology.md  | 598 +++++++++++++++++
 ...6-files-attachments-and-rich-data-flows.md | 610 +++++++++++++++++
 ...ime-coverage-browser-node-deno-and-edge.md | 598 +++++++++++++++++
 ...ction-operations-security-and-debugging.md | 597 +++++++++++++++++
 .../01-system-overview.md                     |  50 +-
 .../02-workflow-engine.md                     |  51 +-
 .../03-node-development.md                    |  51 +-
 .../04-advanced-integrations.md               |  51 +-
 .../05-production-deployment.md               |  51 +-
 .../06-security-governance.md                 |  49 ++
 .../07-observability.md                       |  49 ++
 .../08-extension-ecosystem.md                 |  48 ++
 .../gemini-cli-tutorial/01-getting-started.md | 577 ++++++++++++++++
 .../02-architecture-tools-and-agent-loop.md   | 590 +++++++++++++++++
 ...uthentication-and-model-access-strategy.md | 578 ++++++++++++++++
 ...04-settings-context-and-custom-commands.md | 586 +++++++++++++++++
 .../05-mcp-extensions-and-skills.md           | 586 +++++++++++++++++
 .../06-headless-mode-and-ci-automation.md     | 590 +++++++++++++++++
 ...sandboxing-security-and-troubleshooting.md | 586 +++++++++++++++++
 ...tion-workflow-and-enterprise-operations.md | 597 +++++++++++++++++
 .../01-getting-started.md                     | 606 +++++++++++++++++
 .../02-architecture-and-control-plane.md      | 607 +++++++++++++++++
 ...ols-yaml-sources-tools-toolsets-prompts.md | 607 +++++++++++++++++
 ...mcp-connectivity-and-client-integration.md | 607 +++++++++++++++++
 ...ebuilt-connectors-and-database-patterns.md | 607 +++++++++++++++++
 ...6-deployment-and-observability-patterns.md | 607 +++++++++++++++++
 ...07-cli-testing-and-development-workflow.md | 607 +++++++++++++++++
 ...duction-governance-and-release-strategy.md | 606 +++++++++++++++++
 .../01-getting-started.md                     | 597 +++++++++++++++++
 .../02-remote-vs-local-architecture.md        | 598 +++++++++++++++++
 .../03-authentication-and-token-strategy.md   | 598 +++++++++++++++++
 ...04-toolsets-tools-and-dynamic-discovery.md | 598 +++++++++++++++++
 .../05-host-integration-patterns.md           | 598 +++++++++++++++++
 ...rity-governance-and-enterprise-controls.md | 598 +++++++++++++++++
 ...oting-read-only-and-lockdown-operations.md | 598 +++++++++++++++++
 .../08-contribution-and-upgrade-workflow.md   | 597 +++++++++++++++++
 .../goose-tutorial/01-getting-started.md      | 586 +++++++++++++++++
 .../02-architecture-and-agent-loop.md         | 583 +++++++++++++++++
 .../03-providers-and-model-routing.md         | 583 +++++++++++++++++
 .../04-permissions-and-tool-governance.md     | 595 +++++++++++++++++
 .../05-sessions-and-context-management.md     | 583 +++++++++++++++++
 .../06-extensions-and-mcp-integration.md      | 587 +++++++++++++++++
 .../07-cli-workflows-and-automation.md        | 595 +++++++++++++++++
 .../08-production-operations-and-security.md  | 594 +++++++++++++++++
 .../gpt-oss-tutorial/01-getting-started.md    |  58 ++
 .../02-transformer-architecture.md            |  59 ++
 .../03-tokenization-embeddings.md             |  59 ++
 .../gpt-oss-tutorial/04-training-pipeline.md  |  59 ++
 .../05-attention-mechanisms.md                |  59 ++
 .../06-scaling-distributed-training.md        |  59 ++
 .../07-fine-tuning-alignment.md               |  59 ++
 .../08-production-inference.md                |  58 ++
 .../gptme-tutorial/01-getting-started.md      | 601 +++++++++++++++++
 ...2-core-cli-workflow-and-prompt-patterns.md | 610 +++++++++++++++++
 ...-tooling-and-local-execution-boundaries.md | 610 +++++++++++++++++
 ...uration-layers-and-environment-strategy.md | 610 +++++++++++++++++
 ...ext-lessons-and-conversation-management.md | 610 +++++++++++++++++
 .../06-mcp-acp-and-plugin-extensibility.md    | 610 +++++++++++++++++
 ...omation-server-mode-and-agent-templates.md | 610 +++++++++++++++++
 .../08-production-operations-and-security.md  | 609 +++++++++++++++++
 tutorials/hapi-tutorial/01-getting-started.md |  52 ++
 .../hapi-tutorial/02-system-architecture.md   |  53 ++
 .../03-session-lifecycle-and-handoff.md       |  53 ++
 .../04-remote-access-and-networking.md        |  49 ++
 .../05-permissions-and-approval-workflow.md   |  49 ++
 .../06-pwa-telegram-and-extensions.md         |  49 ++
 .../07-configuration-and-security.md          |  49 ++
 .../hapi-tutorial/08-production-operations.md |  48 ++
 .../haystack-tutorial/01-getting-started.md   |  45 ++
 .../haystack-tutorial/02-document-stores.md   |  49 ++
 .../haystack-tutorial/03-retrievers-search.md |  51 +-
 .../haystack-tutorial/04-generators-llms.md   |  51 +-
 .../05-pipelines-workflows.md                 |  51 +-
 .../06-evaluation-optimization.md             |  51 +-
 .../haystack-tutorial/07-custom-components.md |  51 +-
 .../08-production-deployment.md               |  50 +-
 .../01-getting-started.md                     |  45 ++
 .../02-text-classification.md                 |  49 ++
 .../03-text-generation.md                     |  51 +-
 .../04-question-answering.md                  |  51 +-
 .../05-named-entity-recognition.md            |  51 +-
 .../06-translation-multilingual.md            |  51 +-
 .../huggingface-tutorial/07-fine-tuning.md    |  51 +-
 .../08-production-deployment.md               |  50 +-
 .../humanlayer-tutorial/01-getting-started.md | 606 +++++++++++++++++
 .../02-architecture-and-monorepo-layout.md    | 607 +++++++++++++++++
 .../03-context-engineering-workflows.md       | 607 +++++++++++++++++
 .../04-parallel-agent-orchestration.md        | 607 +++++++++++++++++
 ...-human-approval-and-high-stakes-actions.md | 607 +++++++++++++++++
 .../06-ide-and-cli-integration-patterns.md    | 619 ++++++++++++++++++
 .../07-telemetry-cost-and-team-governance.md  | 607 +++++++++++++++++
 .../08-production-rollout-and-adoption.md     | 606 +++++++++++++++++
 .../instructor-tutorial/01-getting-started.md |  50 ++
 .../instructor-tutorial/02-pydantic-models.md |  51 ++
 .../instructor-tutorial/03-validation.md      |  51 ++
 tutorials/instructor-tutorial/04-complex.md   |  51 ++
 tutorials/instructor-tutorial/05-streaming.md |  51 ++
 tutorials/instructor-tutorial/06-providers.md |  51 ++
 tutorials/instructor-tutorial/07-advanced.md  |  51 ++
 .../instructor-tutorial/08-production.md      |  50 ++
 tutorials/khoj-tutorial/01-getting-started.md |  48 ++
 .../khoj-tutorial/02-architecture-overview.md |  49 ++
 tutorials/khoj-tutorial/03-data-connectors.md |  49 ++
 .../khoj-tutorial/04-search-and-retrieval.md  |  49 ++
 tutorials/khoj-tutorial/05-chat-interface.md  |  49 ++
 .../khoj-tutorial/06-automation-and-agents.md |  49 ++
 .../07-customization-and-plugins.md           |  49 ++
 .../khoj-tutorial/08-production-deployment.md |  48 ++
 .../kilocode-tutorial/01-getting-started.md   | 609 +++++++++++++++++
 .../02-agent-loop-and-state-model.md          | 610 +++++++++++++++++
 .../03-modes-prompts-and-approval-workflow.md | 610 +++++++++++++++++
 .../04-authentication-and-provider-routing.md | 610 +++++++++++++++++
 ...session-history-and-context-persistence.md | 610 +++++++++++++++++
 .../06-extensions-mcp-and-custom-modes.md     | 610 +++++++++++++++++
 ...7-cli-tui-architecture-for-contributors.md | 610 +++++++++++++++++
 ...08-production-operations-and-governance.md | 609 +++++++++++++++++
 .../kimi-cli-tutorial/01-getting-started.md   | 601 +++++++++++++++++
 ...02-command-surface-and-session-controls.md | 598 +++++++++++++++++
 .../03-agents-subagents-and-skills.md         | 598 +++++++++++++++++
 .../04-mcp-tooling-and-security-model.md      | 602 +++++++++++++++++
 .../05-acp-and-ide-integrations.md            | 602 +++++++++++++++++
 .../06-shell-mode-print-mode-and-wire-mode.md | 602 +++++++++++++++++
 .../07-loop-control-retries-and-long-tasks.md | 598 +++++++++++++++++
 ...08-production-operations-and-governance.md | 609 +++++++++++++++++
 .../01-getting-started.md                     |  50 +-
 .../02-custom-resources.md                    |  51 +-
 .../03-reconciliation-loop.md                 |  51 +-
 .../04-owned-resources.md                     |  51 +-
 .../05-status-conditions.md                   |  51 +-
 .../06-testing.md                             |  51 +-
 .../07-observability.md                       |  51 +-
 .../08-production-deployment.md               |  50 +-
 .../lancedb-tutorial/01-getting-started.md    |  48 ++
 .../lancedb-tutorial/02-data-modeling.md      |  49 ++
 .../lancedb-tutorial/03-vector-operations.md  |  49 ++
 .../lancedb-tutorial/04-hybrid-search.md      |  49 ++
 tutorials/lancedb-tutorial/05-integrations.md |  49 ++
 tutorials/lancedb-tutorial/06-performance.md  |  49 ++
 tutorials/lancedb-tutorial/07-production.md   |  49 ++
 .../lancedb-tutorial/08-advanced-patterns.md  |  48 ++
 .../01-getting-started.md                     |  48 ++
 .../02-runnable-interface.md                  |  49 ++
 .../03-chat-model-architecture.md             |  49 ++
 .../04-chain-composition.md                   |  49 ++
 .../05-document-loading-splitting.md          |  49 ++
 .../06-vector-store-abstraction.md            |  49 ++
 .../07-agent-architecture.md                  |  49 ++
 .../08-production-patterns.md                 |  48 ++
 .../langchain-tutorial/01-getting-started.md  |  45 ++
 .../langchain-tutorial/02-prompt-templates.md |  49 ++
 .../langchain-tutorial/03-memory-systems.md   |  46 ++
 .../04-document-processing.md                 |  46 ++
 .../langchain-tutorial/05-vector-stores.md    |  46 ++
 .../langchain-tutorial/06-agents-tools.md     |  46 ++
 .../langchain-tutorial/07-advanced-chains.md  |  46 ++
 .../08-production-deployment.md               |  46 ++
 .../09-evaluation-monitoring.md               |  50 +-
 .../langflow-tutorial/01-getting-started.md   | 598 +++++++++++++++++
 .../02-platform-architecture.md               | 599 +++++++++++++++++
 .../03-visual-flow-builder.md                 | 607 +++++++++++++++++
 .../04-agent-workflows-and-orchestration.md   | 607 +++++++++++++++++
 .../05-api-and-mcp-deployment.md              | 607 +++++++++++++++++
 .../06-observability-and-security.md          | 595 +++++++++++++++++
 .../07-custom-components-and-extensions.md    | 595 +++++++++++++++++
 .../08-production-operations.md               | 606 +++++++++++++++++
 .../langfuse-tutorial/01-getting-started.md   | 319 +++++++++
 tutorials/langfuse-tutorial/02-tracing.md     | 152 +++++
 tutorials/langfuse-tutorial/03-prompts.md     | 176 +++++
 tutorials/langfuse-tutorial/04-evaluation.md  |  53 ++
 tutorials/langfuse-tutorial/05-analytics.md   | 152 +++++
 tutorials/langfuse-tutorial/06-datasets.md    | 152 +++++
 .../langfuse-tutorial/07-integrations.md      |  53 ++
 tutorials/langfuse-tutorial/08-production.md  | 223 +++++++
 .../langgraph-tutorial/01-getting-started.md  |  45 ++
 .../langgraph-tutorial/02-state-management.md |  49 ++
 .../langgraph-tutorial/03-nodes-edges.md      |  46 ++
 .../04-conditional-logic.md                   |  46 ++
 .../05-multi-agent-systems.md                 |  46 ++
 .../langgraph-tutorial/06-tool-integration.md |  51 +-
 .../07-persistence-checkpoints.md             |  35 +
 .../08-production-deployment.md               |  51 +-
 .../letta-tutorial/01-getting-started.md      |  52 +-
 tutorials/letta-tutorial/02-memory.md         |  53 +-
 tutorials/letta-tutorial/03-configuration.md  |  53 +-
 tutorials/letta-tutorial/04-tools.md          |  53 +-
 tutorials/letta-tutorial/05-conversations.md  |  53 +-
 tutorials/letta-tutorial/06-multi-agent.md    |  53 +-
 tutorials/letta-tutorial/07-api.md            |  53 +-
 tutorials/letta-tutorial/08-production.md     |  52 +-
 .../litellm-tutorial/01-getting-started.md    | 321 ++++++++-
 tutorials/litellm-tutorial/02-providers.md    | 154 ++++-
 tutorials/litellm-tutorial/03-completion.md   | 154 ++++-
 tutorials/litellm-tutorial/04-streaming.md    | 154 ++++-
 tutorials/litellm-tutorial/05-fallbacks.md    | 154 ++++-
 .../litellm-tutorial/06-cost-tracking.md      |  55 +-
 tutorials/litellm-tutorial/07-proxy.md        |  55 +-
 tutorials/litellm-tutorial/08-production.md   |  54 +-
 .../liveblocks-tutorial/01-getting-started.md |  52 ++
 .../02-presence-awareness.md                  |  53 ++
 .../03-storage-conflict-resolution.md         |  53 ++
 .../04-comments-threads.md                    |  53 ++
 .../liveblocks-tutorial/05-notifications.md   |  53 ++
 .../06-react-integration.md                   |  53 ++
 .../07-advanced-patterns.md                   |  53 ++
 .../08-production-deployment.md               |  52 ++
 .../llama-cpp-tutorial/01-getting-started.md  |  52 +-
 .../llama-cpp-tutorial/02-model-formats.md    |  53 +-
 tutorials/llama-cpp-tutorial/03-cli-usage.md  |  53 +-
 tutorials/llama-cpp-tutorial/04-server.md     |  53 +-
 tutorials/llama-cpp-tutorial/05-gpu.md        |  53 +-
 .../llama-cpp-tutorial/06-quantization.md     |  53 +-
 tutorials/llama-cpp-tutorial/07-advanced.md   |  53 +-
 .../llama-cpp-tutorial/08-integration.md      |  52 +-
 .../01-getting-started.md                     |  45 ++
 .../02-data-preparation.md                    |  46 ++
 .../03-model-configuration.md                 |  46 ++
 .../04-training-pipeline.md                   |  46 ++
 .../05-model-evaluation.md                    |  46 ++
 .../llama-factory-tutorial/06-deployment.md   |  46 ++
 .../07-advanced-techniques.md                 |  46 ++
 .../08-production-case-studies.md             |  45 ++
 369 files changed, 105658 insertions(+), 85 deletions(-)

diff --git a/tutorials/copilot-cli-tutorial/01-getting-started.md b/tutorials/copilot-cli-tutorial/01-getting-started.md
index 8071cc52..fe390999 100644
--- a/tutorials/copilot-cli-tutorial/01-getting-started.md
+++ b/tutorials/copilot-cli-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: GitHub Copilot CLI Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets Copilot CLI installed and launched for your first terminal agent session.
 
 ## Installation Paths
@@ -42,3 +45,601 @@ On first run, use `/login` if not already authenticated.
 You now have Copilot CLI running in terminal with a first interactive session.
 
 Next: [Chapter 2: Authentication and Access Policies](02-authentication-and-access-policies.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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- tutorial slug: **copilot-cli-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Copilot Cli 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
+
+- [Copilot CLI Repository](https://github.com/github/copilot-cli)
+- [Copilot CLI README](https://github.com/github/copilot-cli/blob/main/README.md)
+- [Copilot CLI Changelog](https://github.com/github/copilot-cli/blob/main/changelog.md)
+- [Official Copilot CLI docs](https://docs.github.com/copilot/concepts/agents/about-copilot-cli)
+- [Install script](https://github.com/github/copilot-cli/blob/main/install.sh)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `install`, `copilot`, `macOS` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Linux`, `brew`, `github` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `install`.
+2. **Input normalization**: shape incoming data so `copilot` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `macOS`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Copilot CLI Repository](https://github.com/github/copilot-cli)
+  Why it matters: authoritative reference on `Copilot CLI Repository` (github.com).
+- [Copilot CLI README](https://github.com/github/copilot-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Copilot CLI README` (github.com).
+- [Copilot CLI Changelog](https://github.com/github/copilot-cli/blob/main/changelog.md)
+  Why it matters: authoritative reference on `Copilot CLI Changelog` (github.com).
+- [Official Copilot CLI docs](https://docs.github.com/copilot/concepts/agents/about-copilot-cli)
+  Why it matters: authoritative reference on `Official Copilot CLI docs` (docs.github.com).
+- [Install script](https://github.com/github/copilot-cli/blob/main/install.sh)
+  Why it matters: authoritative reference on `Install script` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `install` and `copilot` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Authentication and Access Policies](02-authentication-and-access-policies.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/copilot-cli-tutorial/02-authentication-and-access-policies.md b/tutorials/copilot-cli-tutorial/02-authentication-and-access-policies.md
index bcd66295..e8b009a2 100644
--- a/tutorials/copilot-cli-tutorial/02-authentication-and-access-policies.md
+++ b/tutorials/copilot-cli-tutorial/02-authentication-and-access-policies.md
@@ -7,6 +7,9 @@ parent: GitHub Copilot CLI Tutorial
 
 # Chapter 2: Authentication and Access Policies
 
+Welcome to **Chapter 2: Authentication and Access Policies**. In this part of **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Copilot CLI access depends on both local authentication and organization-level Copilot policy settings.
 
 ## Authentication Options
@@ -32,3 +35,610 @@ Copilot CLI access depends on both local authentication and organization-level C
 You now know how to secure Copilot CLI access and avoid common policy blockers.
 
 Next: [Chapter 3: Interactive Workflow and Approval Model](03-interactive-workflow-and-approval-model.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- tutorial slug: **copilot-cli-tutorial**
+- chapter focus: **Chapter 2: Authentication and Access Policies**
+- system context: **Copilot Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Authentication and Access Policies`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Copilot CLI Repository](https://github.com/github/copilot-cli)
+- [Copilot CLI README](https://github.com/github/copilot-cli/blob/main/README.md)
+- [Copilot CLI Changelog](https://github.com/github/copilot-cli/blob/main/changelog.md)
+- [Official Copilot CLI docs](https://docs.github.com/copilot/concepts/agents/about-copilot-cli)
+- [Install script](https://github.com/github/copilot-cli/blob/main/install.sh)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Authentication and Access Policies`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 2: Authentication and Access Policies
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Authentication and Access Policies` as an operating subsystem inside **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Authentication and Access Policies` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Copilot CLI Repository](https://github.com/github/copilot-cli)
+  Why it matters: authoritative reference on `Copilot CLI Repository` (github.com).
+- [Copilot CLI README](https://github.com/github/copilot-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Copilot CLI README` (github.com).
+- [Copilot CLI Changelog](https://github.com/github/copilot-cli/blob/main/changelog.md)
+  Why it matters: authoritative reference on `Copilot CLI Changelog` (github.com).
+- [Official Copilot CLI docs](https://docs.github.com/copilot/concepts/agents/about-copilot-cli)
+  Why it matters: authoritative reference on `Official Copilot CLI docs` (docs.github.com).
+- [Install script](https://github.com/github/copilot-cli/blob/main/install.sh)
+  Why it matters: authoritative reference on `Install script` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Interactive Workflow and Approval Model](03-interactive-workflow-and-approval-model.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/copilot-cli-tutorial/03-interactive-workflow-and-approval-model.md b/tutorials/copilot-cli-tutorial/03-interactive-workflow-and-approval-model.md
index b58f3667..69d424b5 100644
--- a/tutorials/copilot-cli-tutorial/03-interactive-workflow-and-approval-model.md
+++ b/tutorials/copilot-cli-tutorial/03-interactive-workflow-and-approval-model.md
@@ -7,6 +7,9 @@ parent: GitHub Copilot CLI Tutorial
 
 # Chapter 3: Interactive Workflow and Approval Model
 
+Welcome to **Chapter 3: Interactive Workflow and Approval Model**. In this part of **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Copilot CLI emphasizes user-in-the-loop execution: proposals are shown before actions are performed.
 
 ## Interaction Pattern
@@ -31,3 +34,610 @@ Copilot CLI emphasizes user-in-the-loop execution: proposals are shown before ac
 You now understand the approval-first interaction style that governs Copilot CLI execution.
 
 Next: [Chapter 4: Models, Experimental Features, and Autopilot](04-models-experimental-features-and-autopilot.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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- tutorial slug: **copilot-cli-tutorial**
+- chapter focus: **Chapter 3: Interactive Workflow and Approval Model**
+- system context: **Copilot Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Interactive Workflow and Approval 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
+
+- [Copilot CLI Repository](https://github.com/github/copilot-cli)
+- [Copilot CLI README](https://github.com/github/copilot-cli/blob/main/README.md)
+- [Copilot CLI Changelog](https://github.com/github/copilot-cli/blob/main/changelog.md)
+- [Official Copilot CLI docs](https://docs.github.com/copilot/concepts/agents/about-copilot-cli)
+- [Install script](https://github.com/github/copilot-cli/blob/main/install.sh)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Interactive Workflow and Approval Model`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 3: Interactive Workflow and Approval Model
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Interactive Workflow and Approval Model` as an operating subsystem inside **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Interactive Workflow and Approval Model` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Copilot CLI Repository](https://github.com/github/copilot-cli)
+  Why it matters: authoritative reference on `Copilot CLI Repository` (github.com).
+- [Copilot CLI README](https://github.com/github/copilot-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Copilot CLI README` (github.com).
+- [Copilot CLI Changelog](https://github.com/github/copilot-cli/blob/main/changelog.md)
+  Why it matters: authoritative reference on `Copilot CLI Changelog` (github.com).
+- [Official Copilot CLI docs](https://docs.github.com/copilot/concepts/agents/about-copilot-cli)
+  Why it matters: authoritative reference on `Official Copilot CLI docs` (docs.github.com).
+- [Install script](https://github.com/github/copilot-cli/blob/main/install.sh)
+  Why it matters: authoritative reference on `Install script` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Authentication and Access Policies](02-authentication-and-access-policies.md)
+- [Next Chapter: Chapter 4: Models, Experimental Features, and Autopilot](04-models-experimental-features-and-autopilot.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/copilot-cli-tutorial/04-models-experimental-features-and-autopilot.md b/tutorials/copilot-cli-tutorial/04-models-experimental-features-and-autopilot.md
index 778202aa..3d64792f 100644
--- a/tutorials/copilot-cli-tutorial/04-models-experimental-features-and-autopilot.md
+++ b/tutorials/copilot-cli-tutorial/04-models-experimental-features-and-autopilot.md
@@ -7,6 +7,9 @@ parent: GitHub Copilot CLI Tutorial
 
 # Chapter 4: Models, Experimental Features, and Autopilot
 
+Welcome to **Chapter 4: Models, Experimental Features, and Autopilot**. In this part of **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Copilot CLI supports model switching and opt-in experimental capabilities for advanced workflows.
 
 ## Controls and Features
@@ -33,3 +36,598 @@ Copilot CLI supports model switching and opt-in experimental capabilities for ad
 You can now tune Copilot CLI behavior for speed or control based on task risk.
 
 Next: [Chapter 5: MCP and LSP Extension Points](05-mcp-and-lsp-extension-points.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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- tutorial slug: **copilot-cli-tutorial**
+- chapter focus: **Chapter 4: Models, Experimental Features, and Autopilot**
+- system context: **Copilot Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Models, Experimental Features, and Autopilot`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Copilot CLI Repository](https://github.com/github/copilot-cli)
+- [Copilot CLI README](https://github.com/github/copilot-cli/blob/main/README.md)
+- [Copilot CLI Changelog](https://github.com/github/copilot-cli/blob/main/changelog.md)
+- [Official Copilot CLI docs](https://docs.github.com/copilot/concepts/agents/about-copilot-cli)
+- [Install script](https://github.com/github/copilot-cli/blob/main/install.sh)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Models, Experimental Features, and Autopilot`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Models, Experimental Features, and Autopilot
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Models, Experimental Features, and Autopilot` as an operating subsystem inside **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Models, Experimental Features, and Autopilot` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Copilot CLI Repository](https://github.com/github/copilot-cli)
+  Why it matters: authoritative reference on `Copilot CLI Repository` (github.com).
+- [Copilot CLI README](https://github.com/github/copilot-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Copilot CLI README` (github.com).
+- [Copilot CLI Changelog](https://github.com/github/copilot-cli/blob/main/changelog.md)
+  Why it matters: authoritative reference on `Copilot CLI Changelog` (github.com).
+- [Official Copilot CLI docs](https://docs.github.com/copilot/concepts/agents/about-copilot-cli)
+  Why it matters: authoritative reference on `Official Copilot CLI docs` (docs.github.com).
+- [Install script](https://github.com/github/copilot-cli/blob/main/install.sh)
+  Why it matters: authoritative reference on `Install script` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Interactive Workflow and Approval Model](03-interactive-workflow-and-approval-model.md)
+- [Next Chapter: Chapter 5: MCP and LSP Extension Points](05-mcp-and-lsp-extension-points.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/copilot-cli-tutorial/05-mcp-and-lsp-extension-points.md b/tutorials/copilot-cli-tutorial/05-mcp-and-lsp-extension-points.md
index 1f9d75ca..be882735 100644
--- a/tutorials/copilot-cli-tutorial/05-mcp-and-lsp-extension-points.md
+++ b/tutorials/copilot-cli-tutorial/05-mcp-and-lsp-extension-points.md
@@ -7,6 +7,9 @@ parent: GitHub Copilot CLI Tutorial
 
 # Chapter 5: MCP and LSP Extension Points
 
+Welcome to **Chapter 5: MCP and LSP Extension Points**. In this part of **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Copilot CLI is extensible through MCP servers and optional LSP configuration.
 
 ## Extension Surfaces
@@ -31,3 +34,610 @@ Copilot CLI is extensible through MCP servers and optional LSP configuration.
 You now have a practical model for extending Copilot CLI with external tools and language intelligence.
 
 Next: [Chapter 6: GitHub-Native Context Workflows](06-github-native-context-workflows.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- tutorial slug: **copilot-cli-tutorial**
+- chapter focus: **Chapter 5: MCP and LSP Extension Points**
+- system context: **Copilot Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: MCP and LSP Extension Points`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Copilot CLI Repository](https://github.com/github/copilot-cli)
+- [Copilot CLI README](https://github.com/github/copilot-cli/blob/main/README.md)
+- [Copilot CLI Changelog](https://github.com/github/copilot-cli/blob/main/changelog.md)
+- [Official Copilot CLI docs](https://docs.github.com/copilot/concepts/agents/about-copilot-cli)
+- [Install script](https://github.com/github/copilot-cli/blob/main/install.sh)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: MCP and LSP Extension Points`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 5: MCP and LSP Extension Points
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: MCP and LSP Extension Points` as an operating subsystem inside **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: MCP and LSP Extension Points` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Copilot CLI Repository](https://github.com/github/copilot-cli)
+  Why it matters: authoritative reference on `Copilot CLI Repository` (github.com).
+- [Copilot CLI README](https://github.com/github/copilot-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Copilot CLI README` (github.com).
+- [Copilot CLI Changelog](https://github.com/github/copilot-cli/blob/main/changelog.md)
+  Why it matters: authoritative reference on `Copilot CLI Changelog` (github.com).
+- [Official Copilot CLI docs](https://docs.github.com/copilot/concepts/agents/about-copilot-cli)
+  Why it matters: authoritative reference on `Official Copilot CLI docs` (docs.github.com).
+- [Install script](https://github.com/github/copilot-cli/blob/main/install.sh)
+  Why it matters: authoritative reference on `Install script` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Models, Experimental Features, and Autopilot](04-models-experimental-features-and-autopilot.md)
+- [Next Chapter: Chapter 6: GitHub-Native Context Workflows](06-github-native-context-workflows.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/copilot-cli-tutorial/06-github-native-context-workflows.md b/tutorials/copilot-cli-tutorial/06-github-native-context-workflows.md
index 14ab4644..e699158a 100644
--- a/tutorials/copilot-cli-tutorial/06-github-native-context-workflows.md
+++ b/tutorials/copilot-cli-tutorial/06-github-native-context-workflows.md
@@ -7,6 +7,9 @@ parent: GitHub Copilot CLI Tutorial
 
 # Chapter 6: GitHub-Native Context Workflows
 
+Welcome to **Chapter 6: GitHub-Native Context Workflows**. In this part of **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Copilot CLI is designed to work with GitHub context out of the box, including repository, issue, and pull request awareness.
 
 ## Workflow Advantages
@@ -32,3 +35,610 @@ Copilot CLI is designed to work with GitHub context out of the box, including re
 You now understand how Copilot CLI aligns terminal execution with GitHub development flows.
 
 Next: [Chapter 7: Installation and Update Channels](07-installation-and-update-channels.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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- tutorial slug: **copilot-cli-tutorial**
+- chapter focus: **Chapter 6: GitHub-Native Context Workflows**
+- system context: **Copilot Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: GitHub-Native Context Workflows`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Copilot CLI Repository](https://github.com/github/copilot-cli)
+- [Copilot CLI README](https://github.com/github/copilot-cli/blob/main/README.md)
+- [Copilot CLI Changelog](https://github.com/github/copilot-cli/blob/main/changelog.md)
+- [Official Copilot CLI docs](https://docs.github.com/copilot/concepts/agents/about-copilot-cli)
+- [Install script](https://github.com/github/copilot-cli/blob/main/install.sh)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: GitHub-Native Context Workflows`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 6: GitHub-Native Context Workflows
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: GitHub-Native Context Workflows` as an operating subsystem inside **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: GitHub-Native Context Workflows` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Copilot CLI Repository](https://github.com/github/copilot-cli)
+  Why it matters: authoritative reference on `Copilot CLI Repository` (github.com).
+- [Copilot CLI README](https://github.com/github/copilot-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Copilot CLI README` (github.com).
+- [Copilot CLI Changelog](https://github.com/github/copilot-cli/blob/main/changelog.md)
+  Why it matters: authoritative reference on `Copilot CLI Changelog` (github.com).
+- [Official Copilot CLI docs](https://docs.github.com/copilot/concepts/agents/about-copilot-cli)
+  Why it matters: authoritative reference on `Official Copilot CLI docs` (docs.github.com).
+- [Install script](https://github.com/github/copilot-cli/blob/main/install.sh)
+  Why it matters: authoritative reference on `Install script` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: MCP and LSP Extension Points](05-mcp-and-lsp-extension-points.md)
+- [Next Chapter: Chapter 7: Installation and Update Channels](07-installation-and-update-channels.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/copilot-cli-tutorial/07-installation-and-update-channels.md b/tutorials/copilot-cli-tutorial/07-installation-and-update-channels.md
index c8f6c051..69c09f45 100644
--- a/tutorials/copilot-cli-tutorial/07-installation-and-update-channels.md
+++ b/tutorials/copilot-cli-tutorial/07-installation-and-update-channels.md
@@ -7,6 +7,9 @@ parent: GitHub Copilot CLI Tutorial
 
 # Chapter 7: Installation and Update Channels
 
+Welcome to **Chapter 7: Installation and Update Channels**. In this part of **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Copilot CLI supports multiple distribution channels suitable for local and enterprise environments.
 
 ## Channel Matrix
@@ -35,3 +38,598 @@ Copilot CLI supports multiple distribution channels suitable for local and enter
 You now have a rollout model for stable and controlled Copilot CLI upgrades.
 
 Next: [Chapter 8: Production Governance and Team Rollout](08-production-governance-and-team-rollout.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: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- tutorial slug: **copilot-cli-tutorial**
+- chapter focus: **Chapter 7: Installation and Update Channels**
+- system context: **Copilot Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Installation and Update Channels`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Copilot CLI Repository](https://github.com/github/copilot-cli)
+- [Copilot CLI README](https://github.com/github/copilot-cli/blob/main/README.md)
+- [Copilot CLI Changelog](https://github.com/github/copilot-cli/blob/main/changelog.md)
+- [Official Copilot CLI docs](https://docs.github.com/copilot/concepts/agents/about-copilot-cli)
+- [Install script](https://github.com/github/copilot-cli/blob/main/install.sh)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Installation and Update Channels`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Installation and Update Channels
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Installation and Update Channels` as an operating subsystem inside **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Installation and Update Channels` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Copilot CLI Repository](https://github.com/github/copilot-cli)
+  Why it matters: authoritative reference on `Copilot CLI Repository` (github.com).
+- [Copilot CLI README](https://github.com/github/copilot-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Copilot CLI README` (github.com).
+- [Copilot CLI Changelog](https://github.com/github/copilot-cli/blob/main/changelog.md)
+  Why it matters: authoritative reference on `Copilot CLI Changelog` (github.com).
+- [Official Copilot CLI docs](https://docs.github.com/copilot/concepts/agents/about-copilot-cli)
+  Why it matters: authoritative reference on `Official Copilot CLI docs` (docs.github.com).
+- [Install script](https://github.com/github/copilot-cli/blob/main/install.sh)
+  Why it matters: authoritative reference on `Install script` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: GitHub-Native Context Workflows](06-github-native-context-workflows.md)
+- [Next Chapter: Chapter 8: Production Governance and Team Rollout](08-production-governance-and-team-rollout.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/copilot-cli-tutorial/08-production-governance-and-team-rollout.md b/tutorials/copilot-cli-tutorial/08-production-governance-and-team-rollout.md
index fcf38adc..0e545ca8 100644
--- a/tutorials/copilot-cli-tutorial/08-production-governance-and-team-rollout.md
+++ b/tutorials/copilot-cli-tutorial/08-production-governance-and-team-rollout.md
@@ -7,6 +7,9 @@ parent: GitHub Copilot CLI Tutorial
 
 # Chapter 8: Production Governance and Team Rollout
 
+Welcome to **Chapter 8: Production Governance and Team Rollout**. In this part of **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Operating Copilot CLI in teams requires policy alignment, permissions governance, and repeatable onboarding.
 
 ## Governance Checklist
@@ -31,3 +34,609 @@ Operating Copilot CLI in teams requires policy alignment, permissions governance
 ## Summary
 
 You now have a practical governance baseline for production Copilot CLI adoption.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- tutorial slug: **copilot-cli-tutorial**
+- chapter focus: **Chapter 8: Production Governance and Team Rollout**
+- system context: **Copilot Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Governance and Team Rollout`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Copilot CLI Repository](https://github.com/github/copilot-cli)
+- [Copilot CLI README](https://github.com/github/copilot-cli/blob/main/README.md)
+- [Copilot CLI Changelog](https://github.com/github/copilot-cli/blob/main/changelog.md)
+- [Official Copilot CLI docs](https://docs.github.com/copilot/concepts/agents/about-copilot-cli)
+- [Install script](https://github.com/github/copilot-cli/blob/main/install.sh)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Governance and Team Rollout`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Production Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 8: Production Governance and Team Rollout
+
+- tutorial context: **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Governance and Team Rollout` as an operating subsystem inside **GitHub Copilot CLI Tutorial: Copilot Agent Workflows in the Terminal**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Governance and Team Rollout` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Copilot CLI Repository](https://github.com/github/copilot-cli)
+  Why it matters: authoritative reference on `Copilot CLI Repository` (github.com).
+- [Copilot CLI README](https://github.com/github/copilot-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Copilot CLI README` (github.com).
+- [Copilot CLI Changelog](https://github.com/github/copilot-cli/blob/main/changelog.md)
+  Why it matters: authoritative reference on `Copilot CLI Changelog` (github.com).
+- [Official Copilot CLI docs](https://docs.github.com/copilot/concepts/agents/about-copilot-cli)
+  Why it matters: authoritative reference on `Official Copilot CLI docs` (docs.github.com).
+- [Install script](https://github.com/github/copilot-cli/blob/main/install.sh)
+  Why it matters: authoritative reference on `Install script` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Installation and Update Channels](07-installation-and-update-channels.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/copilotkit-tutorial/01-getting-started.md b/tutorials/copilotkit-tutorial/01-getting-started.md
index 7518ff86..503558cf 100644
--- a/tutorials/copilotkit-tutorial/01-getting-started.md
+++ b/tutorials/copilotkit-tutorial/01-getting-started.md
@@ -8,6 +8,9 @@ parent: CopilotKit Tutorial
 
 # Chapter 1: Getting Started with CopilotKit
 
+Welcome to **Chapter 1: Getting Started with CopilotKit**. In this part of **CopilotKit Tutorial: Building AI Copilots for React Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Set up CopilotKit in your React application and create your first AI copilot with basic functionality.
 
 ## Overview
@@ -653,4 +656,51 @@ Now that you have a basic copilot running, let's explore how to make your app co
 
 **Ready for Chapter 2?** [Reading App Context](02-app-context.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `text`, `className`, `todo` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with CopilotKit` as an operating subsystem inside **CopilotKit Tutorial: Building AI Copilots for React Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `copilotkit`, `react`, `note` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with CopilotKit` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `text`.
+2. **Input normalization**: shape incoming data so `className` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `todo`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/CopilotKit/CopilotKit)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `text` and `className` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Reading App Context - Making Your App State Visible to AI](02-app-context.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/copilotkit-tutorial/02-app-context.md b/tutorials/copilotkit-tutorial/02-app-context.md
index c0866739..ec8521fa 100644
--- a/tutorials/copilotkit-tutorial/02-app-context.md
+++ b/tutorials/copilotkit-tutorial/02-app-context.md
@@ -8,6 +8,9 @@ parent: CopilotKit Tutorial
 
 # Chapter 2: Reading App Context - Making Your App State Visible to AI
 
+Welcome to **Chapter 2: Reading App Context - Making Your App State Visible to AI**. In this part of **CopilotKit Tutorial: Building AI Copilots for React Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master useCopilotReadable to share application state, data, and context with your AI copilot for intelligent interactions.
 
 ## Overview
@@ -775,4 +778,52 @@ Now that the AI can see your app context, let's explore how to enable AI to take
 
 **Ready for Chapter 3?** [Copilot Actions](03-copilot-actions.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `text`, `className`, `status` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Reading App Context - Making Your App State Visible to AI` as an operating subsystem inside **CopilotKit Tutorial: Building AI Copilots for React Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `dept`, `length`, `projects` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Reading App Context - Making Your App State Visible to AI` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `text`.
+2. **Input normalization**: shape incoming data so `className` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `status`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/CopilotKit/CopilotKit)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `text` and `className` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with CopilotKit](01-getting-started.md)
+- [Next Chapter: Chapter 3: Copilot Actions - Enabling AI to Take Actions in Your App](03-copilot-actions.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/copilotkit-tutorial/03-copilot-actions.md b/tutorials/copilotkit-tutorial/03-copilot-actions.md
index f5d985e5..ba09090a 100644
--- a/tutorials/copilotkit-tutorial/03-copilot-actions.md
+++ b/tutorials/copilotkit-tutorial/03-copilot-actions.md
@@ -8,6 +8,9 @@ parent: CopilotKit Tutorial
 
 # Chapter 3: Copilot Actions - Enabling AI to Take Actions in Your App
 
+Welcome to **Chapter 3: Copilot Actions - Enabling AI to Take Actions in Your App**. In this part of **CopilotKit Tutorial: Building AI Copilots for React Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master useCopilotAction to allow AI to perform operations, modify state, and interact with your application's backend.
 
 ## Overview
@@ -922,4 +925,52 @@ Now that the AI can perform actions in your app, let's explore the different cha
 
 **Ready for Chapter 4?** [Chat Components](04-chat-components.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `text`, `name`, `className` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Copilot Actions - Enabling AI to Take Actions in Your App` as an operating subsystem inside **CopilotKit Tutorial: Building AI Copilots for React Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `endpoint`, `description`, `contact` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Copilot Actions - Enabling AI to Take Actions in Your App` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `text`.
+2. **Input normalization**: shape incoming data so `name` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `className`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/CopilotKit/CopilotKit)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `text` and `name` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Reading App Context - Making Your App State Visible to AI](02-app-context.md)
+- [Next Chapter: Chapter 4: Chat Components - Building Chat Interfaces with CopilotChat and CopilotSidebar](04-chat-components.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/copilotkit-tutorial/04-chat-components.md b/tutorials/copilotkit-tutorial/04-chat-components.md
index 4575f4ec..f0b04098 100644
--- a/tutorials/copilotkit-tutorial/04-chat-components.md
+++ b/tutorials/copilotkit-tutorial/04-chat-components.md
@@ -8,6 +8,9 @@ parent: CopilotKit Tutorial
 
 # Chapter 4: Chat Components - Building Chat Interfaces with CopilotChat and CopilotSidebar
 
+Welcome to **Chapter 4: Chat Components - Building Chat Interfaces with CopilotChat and CopilotSidebar**. In this part of **CopilotKit Tutorial: Building AI Copilots for React Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master CopilotKit's chat UI components for creating conversational AI interfaces in your React applications.
 
 ## Overview
@@ -829,4 +832,52 @@ Now that you can build chat interfaces, let's explore Generative UI - AI-generat
 
 **Ready for Chapter 5?** [Generative UI](05-generative-ui.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `className`, `text`, `gray` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Chat Components - Building Chat Interfaces with CopilotChat and CopilotSidebar` as an operating subsystem inside **CopilotKit Tutorial: Building AI Copilots for React Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `rounded`, `font`, `theme` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Chat Components - Building Chat Interfaces with CopilotChat and CopilotSidebar` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `className`.
+2. **Input normalization**: shape incoming data so `text` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `gray`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/CopilotKit/CopilotKit)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `className` and `text` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Copilot Actions - Enabling AI to Take Actions in Your App](03-copilot-actions.md)
+- [Next Chapter: Chapter 5: Generative UI - AI-Generated React Components](05-generative-ui.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/copilotkit-tutorial/05-generative-ui.md b/tutorials/copilotkit-tutorial/05-generative-ui.md
index a6f4212f..1bdc0e10 100644
--- a/tutorials/copilotkit-tutorial/05-generative-ui.md
+++ b/tutorials/copilotkit-tutorial/05-generative-ui.md
@@ -8,6 +8,9 @@ parent: CopilotKit Tutorial
 
 # Chapter 5: Generative UI - AI-Generated React Components
 
+Welcome to **Chapter 5: Generative UI - AI-Generated React Components**. In this part of **CopilotKit Tutorial: Building AI Copilots for React Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Enable AI to generate and modify React components dynamically through natural language conversations.
 
 ## Overview
@@ -731,4 +734,52 @@ Now that you can generate UI components, let's explore CoAgents and LangGraph in
 
 **Ready for Chapter 6?** [CoAgents & LangGraph](06-coagents.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `className`, `text`, `gray` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Generative UI - AI-Generated React Components` as an operating subsystem inside **CopilotKit Tutorial: Building AI Copilots for React Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `font`, `component`, `form` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Generative UI - AI-Generated React Components` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `className`.
+2. **Input normalization**: shape incoming data so `text` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `gray`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/CopilotKit/CopilotKit)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `className` and `text` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Chat Components - Building Chat Interfaces with CopilotChat and CopilotSidebar](04-chat-components.md)
+- [Next Chapter: Chapter 6: CoAgents & LangGraph - Building Agentic Workflows](06-coagents.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/copilotkit-tutorial/06-coagents.md b/tutorials/copilotkit-tutorial/06-coagents.md
index 8204e1dc..b96d2609 100644
--- a/tutorials/copilotkit-tutorial/06-coagents.md
+++ b/tutorials/copilotkit-tutorial/06-coagents.md
@@ -8,6 +8,9 @@ parent: CopilotKit Tutorial
 
 # Chapter 6: CoAgents & LangGraph - Building Agentic Workflows
 
+Welcome to **Chapter 6: CoAgents & LangGraph - Building Agentic Workflows**. In this part of **CopilotKit Tutorial: Building AI Copilots for React Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Create sophisticated agentic workflows using CoAgents and LangGraph integration for complex, multi-step processes.
 
 ## Overview
@@ -806,4 +809,52 @@ Now that you can build complex agentic workflows, let's explore human-in-the-loo
 
 **Ready for Chapter 7?** [Human-in-the-Loop](07-human-in-loop.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `className`, `text`, `span` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: CoAgents & LangGraph - Building Agentic Workflows` as an operating subsystem inside **CopilotKit Tutorial: Building AI Copilots for React Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `gray`, `rounded`, `font` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: CoAgents & LangGraph - Building Agentic Workflows` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `className`.
+2. **Input normalization**: shape incoming data so `text` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `span`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/CopilotKit/CopilotKit)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `className` and `text` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Generative UI - AI-Generated React Components](05-generative-ui.md)
+- [Next Chapter: Chapter 7: Human-in-the-Loop - User Approval Flows and Interrupts](07-human-in-loop.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/copilotkit-tutorial/07-human-in-loop.md b/tutorials/copilotkit-tutorial/07-human-in-loop.md
index ae960d78..274a3463 100644
--- a/tutorials/copilotkit-tutorial/07-human-in-loop.md
+++ b/tutorials/copilotkit-tutorial/07-human-in-loop.md
@@ -8,6 +8,9 @@ parent: CopilotKit Tutorial
 
 # Chapter 7: Human-in-the-Loop - User Approval Flows and Interrupts
 
+Welcome to **Chapter 7: Human-in-the-Loop - User Approval Flows and Interrupts**. In this part of **CopilotKit Tutorial: Building AI Copilots for React Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Implement human-in-the-loop workflows where AI agents request user approval for critical actions and handle interruptions gracefully.
 
 ## Overview
@@ -1105,4 +1108,52 @@ Now that you can implement human-in-the-loop workflows, let's explore production
 
 **Ready for Chapter 8?** [Production Deployment](08-production.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `className`, `text`, `request` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Human-in-the-Loop - User Approval Flows and Interrupts` as an operating subsystem inside **CopilotKit Tutorial: Building AI Copilots for React Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `approval`, `description`, `rounded` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Human-in-the-Loop - User Approval Flows and Interrupts` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `className`.
+2. **Input normalization**: shape incoming data so `text` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `request`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/CopilotKit/CopilotKit)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `className` and `text` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: CoAgents & LangGraph - Building Agentic Workflows](06-coagents.md)
+- [Next Chapter: Chapter 8: Production Deployment - Secure, Scalable Copilot Apps](08-production.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/copilotkit-tutorial/08-production.md b/tutorials/copilotkit-tutorial/08-production.md
index d68bfefc..be417e65 100644
--- a/tutorials/copilotkit-tutorial/08-production.md
+++ b/tutorials/copilotkit-tutorial/08-production.md
@@ -8,6 +8,9 @@ parent: CopilotKit Tutorial
 
 # Chapter 8: Production Deployment - Secure, Scalable Copilot Apps
 
+Welcome to **Chapter 8: Production Deployment - Secure, Scalable Copilot Apps**. In this part of **CopilotKit Tutorial: Building AI Copilots for React Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Take your CopilotKit app to production with hardened configs, observability, and deployment patterns for cloud and self-hosted environments.
 
 ## Overview
@@ -1048,3 +1051,50 @@ This concludes the CopilotKit Tutorial. You now have a comprehensive understandi
 ---
 
 *Built with insights from the [CopilotKit repository](https://github.com/CopilotKit/CopilotKit) and community documentation.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `copilotkit`, `name`, `response` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment - Secure, Scalable Copilot Apps` as an operating subsystem inside **CopilotKit Tutorial: Building AI Copilots for React Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `model`, `headers`, `status` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment - Secure, Scalable Copilot Apps` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `copilotkit`.
+2. **Input normalization**: shape incoming data so `name` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `response`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/CopilotKit/CopilotKit)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `copilotkit` and `name` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Human-in-the-Loop - User Approval Flows and Interrupts](07-human-in-loop.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/create-python-server-tutorial/01-getting-started-and-scaffolding-workflow.md b/tutorials/create-python-server-tutorial/01-getting-started-and-scaffolding-workflow.md
index b8be1d57..2189e98c 100644
--- a/tutorials/create-python-server-tutorial/01-getting-started-and-scaffolding-workflow.md
+++ b/tutorials/create-python-server-tutorial/01-getting-started-and-scaffolding-workflow.md
@@ -7,6 +7,9 @@ parent: Create Python Server Tutorial
 
 # Chapter 1: Getting Started and Scaffolding Workflow
 
+Welcome to **Chapter 1: Getting Started and Scaffolding Workflow**. In this part of **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers initial project generation and first-run commands.
 
 ## Learning Goals
@@ -33,3 +36,601 @@ After generation, run `uv sync --dev --all-extras` and `uv run <server-name>` fr
 You now have a reproducible baseline for generating MCP Python server projects.
 
 Next: [Chapter 2: Generated Project Structure and Conventions](02-generated-project-structure-and-conventions.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: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- tutorial slug: **create-python-server-tutorial**
+- chapter focus: **Chapter 1: Getting Started and Scaffolding Workflow**
+- system context: **Create Python Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started and Scaffolding 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
+
+- [Create Python Server README](https://github.com/modelcontextprotocol/create-python-server/blob/main/README.md)
+- [Template README](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/README.md.jinja2)
+- [Template Server Implementation](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/server.py.jinja2)
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-python-server/blob/main/CONTRIBUTING.md)
+- [Security Policy](https://github.com/modelcontextprotocol/create-python-server/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [MCP Quickstart Resources Tutorial](../mcp-quickstart-resources-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [MCP Inspector Tutorial](../mcp-inspector-tutorial/)
+- [Chapter 1: Getting Started and Scaffolding Workflow](01-getting-started-and-scaffolding-workflow.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started and Scaffolding 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 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started and Scaffolding Workflow
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `create`, `server` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started and Scaffolding Workflow` as an operating subsystem inside **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started and Scaffolding Workflow` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `create`.
+2. **Input normalization**: shape incoming data so `server` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Create Python Server README](https://github.com/modelcontextprotocol/create-python-server/blob/main/README.md)
+  Why it matters: authoritative reference on `Create Python Server README` (github.com).
+- [Template README](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/README.md.jinja2)
+  Why it matters: authoritative reference on `Template README` (github.com).
+- [Template Server Implementation](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/server.py.jinja2)
+  Why it matters: authoritative reference on `Template Server Implementation` (github.com).
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-python-server/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Security Policy](https://github.com/modelcontextprotocol/create-python-server/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `create` and `server` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Generated Project Structure and Conventions](02-generated-project-structure-and-conventions.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/create-python-server-tutorial/02-generated-project-structure-and-conventions.md b/tutorials/create-python-server-tutorial/02-generated-project-structure-and-conventions.md
index 42e27259..96ceb2da 100644
--- a/tutorials/create-python-server-tutorial/02-generated-project-structure-and-conventions.md
+++ b/tutorials/create-python-server-tutorial/02-generated-project-structure-and-conventions.md
@@ -7,6 +7,9 @@ parent: Create Python Server Tutorial
 
 # Chapter 2: Generated Project Structure and Conventions
 
+Welcome to **Chapter 2: Generated Project Structure and Conventions**. In this part of **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains generated file layout and how each piece supports maintainable server development.
 
 ## Learning Goals
@@ -34,3 +37,598 @@ This chapter explains generated file layout and how each piece supports maintain
 You now have a structural map for generated MCP Python server projects.
 
 Next: [Chapter 3: Template Server Architecture: Resources, Prompts, and Tools](03-template-server-architecture-resources-prompts-and-tools.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: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- tutorial slug: **create-python-server-tutorial**
+- chapter focus: **Chapter 2: Generated Project Structure and Conventions**
+- system context: **Create Python Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Generated Project Structure and Conventions`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Create Python Server README](https://github.com/modelcontextprotocol/create-python-server/blob/main/README.md)
+- [Template README](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/README.md.jinja2)
+- [Template Server Implementation](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/server.py.jinja2)
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-python-server/blob/main/CONTRIBUTING.md)
+- [Security Policy](https://github.com/modelcontextprotocol/create-python-server/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [MCP Quickstart Resources Tutorial](../mcp-quickstart-resources-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [MCP Inspector Tutorial](../mcp-inspector-tutorial/)
+- [Chapter 1: Getting Started and Scaffolding Workflow](01-getting-started-and-scaffolding-workflow.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Generated Project Structure and Conventions`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Generated Project Structure and Conventions
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Generated Project Structure and Conventions` as an operating subsystem inside **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Generated Project Structure and Conventions` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Create Python Server README](https://github.com/modelcontextprotocol/create-python-server/blob/main/README.md)
+  Why it matters: authoritative reference on `Create Python Server README` (github.com).
+- [Template README](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/README.md.jinja2)
+  Why it matters: authoritative reference on `Template README` (github.com).
+- [Template Server Implementation](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/server.py.jinja2)
+  Why it matters: authoritative reference on `Template Server Implementation` (github.com).
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-python-server/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Security Policy](https://github.com/modelcontextprotocol/create-python-server/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started and Scaffolding Workflow](01-getting-started-and-scaffolding-workflow.md)
+- [Next Chapter: Chapter 3: Template Server Architecture: Resources, Prompts, and Tools](03-template-server-architecture-resources-prompts-and-tools.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/create-python-server-tutorial/03-template-server-architecture-resources-prompts-and-tools.md b/tutorials/create-python-server-tutorial/03-template-server-architecture-resources-prompts-and-tools.md
index edb428fc..1750b6fb 100644
--- a/tutorials/create-python-server-tutorial/03-template-server-architecture-resources-prompts-and-tools.md
+++ b/tutorials/create-python-server-tutorial/03-template-server-architecture-resources-prompts-and-tools.md
@@ -7,6 +7,9 @@ parent: Create Python Server Tutorial
 
 # Chapter 3: Template Server Architecture: Resources, Prompts, and Tools
 
+Welcome to **Chapter 3: Template Server Architecture: Resources, Prompts, and Tools**. In this part of **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter dives into the generated server template and how it models core MCP primitives.
 
 ## Learning Goals
@@ -32,3 +35,610 @@ This chapter dives into the generated server template and how it models core MCP
 You now have a concrete mental model for generated MCP primitive handlers.
 
 Next: [Chapter 4: Runtime, Dependencies, and uv Packaging](04-runtime-dependencies-and-uv-packaging.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- tutorial slug: **create-python-server-tutorial**
+- chapter focus: **Chapter 3: Template Server Architecture: Resources, Prompts, and Tools**
+- system context: **Create Python Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Template Server Architecture: Resources, Prompts, and Tools`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Create Python Server README](https://github.com/modelcontextprotocol/create-python-server/blob/main/README.md)
+- [Template README](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/README.md.jinja2)
+- [Template Server Implementation](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/server.py.jinja2)
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-python-server/blob/main/CONTRIBUTING.md)
+- [Security Policy](https://github.com/modelcontextprotocol/create-python-server/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [MCP Quickstart Resources Tutorial](../mcp-quickstart-resources-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [MCP Inspector Tutorial](../mcp-inspector-tutorial/)
+- [Chapter 1: Getting Started and Scaffolding Workflow](01-getting-started-and-scaffolding-workflow.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Template Server Architecture: Resources, Prompts, and Tools`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 3: Template Server Architecture: Resources, Prompts, and Tools
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Template Server Architecture: Resources, Prompts, and Tools` as an operating subsystem inside **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Template Server Architecture: Resources, Prompts, and Tools` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Create Python Server README](https://github.com/modelcontextprotocol/create-python-server/blob/main/README.md)
+  Why it matters: authoritative reference on `Create Python Server README` (github.com).
+- [Template README](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/README.md.jinja2)
+  Why it matters: authoritative reference on `Template README` (github.com).
+- [Template Server Implementation](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/server.py.jinja2)
+  Why it matters: authoritative reference on `Template Server Implementation` (github.com).
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-python-server/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Security Policy](https://github.com/modelcontextprotocol/create-python-server/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Generated Project Structure and Conventions](02-generated-project-structure-and-conventions.md)
+- [Next Chapter: Chapter 4: Runtime, Dependencies, and uv Packaging](04-runtime-dependencies-and-uv-packaging.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/create-python-server-tutorial/04-runtime-dependencies-and-uv-packaging.md b/tutorials/create-python-server-tutorial/04-runtime-dependencies-and-uv-packaging.md
index c5cf7b0b..2824aa40 100644
--- a/tutorials/create-python-server-tutorial/04-runtime-dependencies-and-uv-packaging.md
+++ b/tutorials/create-python-server-tutorial/04-runtime-dependencies-and-uv-packaging.md
@@ -7,6 +7,9 @@ parent: Create Python Server Tutorial
 
 # Chapter 4: Runtime, Dependencies, and uv Packaging
 
+Welcome to **Chapter 4: Runtime, Dependencies, and uv Packaging**. In this part of **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on dependency/runtime controls for reliable local and publish workflows.
 
 ## Learning Goals
@@ -32,3 +35,610 @@ This chapter focuses on dependency/runtime controls for reliable local and publi
 You now have a consistent runtime and packaging model for generated MCP servers.
 
 Next: [Chapter 5: Local Integration: Claude Desktop and Inspector](05-local-integration-claude-desktop-and-inspector.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- tutorial slug: **create-python-server-tutorial**
+- chapter focus: **Chapter 4: Runtime, Dependencies, and uv Packaging**
+- system context: **Create Python Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Runtime, Dependencies, and uv 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
+
+- [Create Python Server README](https://github.com/modelcontextprotocol/create-python-server/blob/main/README.md)
+- [Template README](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/README.md.jinja2)
+- [Template Server Implementation](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/server.py.jinja2)
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-python-server/blob/main/CONTRIBUTING.md)
+- [Security Policy](https://github.com/modelcontextprotocol/create-python-server/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [MCP Quickstart Resources Tutorial](../mcp-quickstart-resources-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [MCP Inspector Tutorial](../mcp-inspector-tutorial/)
+- [Chapter 1: Getting Started and Scaffolding Workflow](01-getting-started-and-scaffolding-workflow.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Runtime, Dependencies, and uv 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 4: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 4: Runtime, Dependencies, and uv Packaging
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Runtime, Dependencies, and uv Packaging` as an operating subsystem inside **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Runtime, Dependencies, and uv Packaging` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Create Python Server README](https://github.com/modelcontextprotocol/create-python-server/blob/main/README.md)
+  Why it matters: authoritative reference on `Create Python Server README` (github.com).
+- [Template README](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/README.md.jinja2)
+  Why it matters: authoritative reference on `Template README` (github.com).
+- [Template Server Implementation](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/server.py.jinja2)
+  Why it matters: authoritative reference on `Template Server Implementation` (github.com).
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-python-server/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Security Policy](https://github.com/modelcontextprotocol/create-python-server/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Template Server Architecture: Resources, Prompts, and Tools](03-template-server-architecture-resources-prompts-and-tools.md)
+- [Next Chapter: Chapter 5: Local Integration: Claude Desktop and Inspector](05-local-integration-claude-desktop-and-inspector.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/create-python-server-tutorial/05-local-integration-claude-desktop-and-inspector.md b/tutorials/create-python-server-tutorial/05-local-integration-claude-desktop-and-inspector.md
index 762cca42..2c4311db 100644
--- a/tutorials/create-python-server-tutorial/05-local-integration-claude-desktop-and-inspector.md
+++ b/tutorials/create-python-server-tutorial/05-local-integration-claude-desktop-and-inspector.md
@@ -7,6 +7,9 @@ parent: Create Python Server Tutorial
 
 # Chapter 5: Local Integration: Claude Desktop and Inspector
 
+Welcome to **Chapter 5: Local Integration: Claude Desktop and Inspector**. In this part of **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains local integration and debugging workflows for generated servers.
 
 ## Learning Goals
@@ -33,3 +36,598 @@ This chapter explains local integration and debugging workflows for generated se
 You now have a working local integration and debugging strategy for scaffolded servers.
 
 Next: [Chapter 6: Customization and Extension Patterns](06-customization-and-extension-patterns.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- tutorial slug: **create-python-server-tutorial**
+- chapter focus: **Chapter 5: Local Integration: Claude Desktop and Inspector**
+- system context: **Create Python Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Local Integration: Claude Desktop and Inspector`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Create Python Server README](https://github.com/modelcontextprotocol/create-python-server/blob/main/README.md)
+- [Template README](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/README.md.jinja2)
+- [Template Server Implementation](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/server.py.jinja2)
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-python-server/blob/main/CONTRIBUTING.md)
+- [Security Policy](https://github.com/modelcontextprotocol/create-python-server/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [MCP Quickstart Resources Tutorial](../mcp-quickstart-resources-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [MCP Inspector Tutorial](../mcp-inspector-tutorial/)
+- [Chapter 1: Getting Started and Scaffolding Workflow](01-getting-started-and-scaffolding-workflow.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Local Integration: Claude Desktop and Inspector`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Local Integration: Claude Desktop and Inspector
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Local Integration: Claude Desktop and Inspector` as an operating subsystem inside **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Local Integration: Claude Desktop and Inspector` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Create Python Server README](https://github.com/modelcontextprotocol/create-python-server/blob/main/README.md)
+  Why it matters: authoritative reference on `Create Python Server README` (github.com).
+- [Template README](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/README.md.jinja2)
+  Why it matters: authoritative reference on `Template README` (github.com).
+- [Template Server Implementation](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/server.py.jinja2)
+  Why it matters: authoritative reference on `Template Server Implementation` (github.com).
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-python-server/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Security Policy](https://github.com/modelcontextprotocol/create-python-server/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Runtime, Dependencies, and uv Packaging](04-runtime-dependencies-and-uv-packaging.md)
+- [Next Chapter: Chapter 6: Customization and Extension Patterns](06-customization-and-extension-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/create-python-server-tutorial/06-customization-and-extension-patterns.md b/tutorials/create-python-server-tutorial/06-customization-and-extension-patterns.md
index 29103adb..19f591f2 100644
--- a/tutorials/create-python-server-tutorial/06-customization-and-extension-patterns.md
+++ b/tutorials/create-python-server-tutorial/06-customization-and-extension-patterns.md
@@ -7,6 +7,9 @@ parent: Create Python Server Tutorial
 
 # Chapter 6: Customization and Extension Patterns
 
+Welcome to **Chapter 6: Customization and Extension Patterns**. In this part of **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers practical ways to evolve generated scaffolds into domain-specific services.
 
 ## Learning Goals
@@ -33,3 +36,598 @@ This chapter covers practical ways to evolve generated scaffolds into domain-spe
 You now have an extension model for safely evolving generated MCP servers.
 
 Next: [Chapter 7: Quality, Security, and Contribution Workflows](07-quality-security-and-contribution-workflows.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- tutorial slug: **create-python-server-tutorial**
+- chapter focus: **Chapter 6: Customization and Extension Patterns**
+- system context: **Create Python Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Customization and Extension 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
+
+- [Create Python Server README](https://github.com/modelcontextprotocol/create-python-server/blob/main/README.md)
+- [Template README](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/README.md.jinja2)
+- [Template Server Implementation](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/server.py.jinja2)
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-python-server/blob/main/CONTRIBUTING.md)
+- [Security Policy](https://github.com/modelcontextprotocol/create-python-server/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [MCP Quickstart Resources Tutorial](../mcp-quickstart-resources-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [MCP Inspector Tutorial](../mcp-inspector-tutorial/)
+- [Chapter 1: Getting Started and Scaffolding Workflow](01-getting-started-and-scaffolding-workflow.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Customization and Extension 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Customization and Extension Patterns
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Customization and Extension Patterns` as an operating subsystem inside **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Customization and Extension Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Create Python Server README](https://github.com/modelcontextprotocol/create-python-server/blob/main/README.md)
+  Why it matters: authoritative reference on `Create Python Server README` (github.com).
+- [Template README](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/README.md.jinja2)
+  Why it matters: authoritative reference on `Template README` (github.com).
+- [Template Server Implementation](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/server.py.jinja2)
+  Why it matters: authoritative reference on `Template Server Implementation` (github.com).
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-python-server/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Security Policy](https://github.com/modelcontextprotocol/create-python-server/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Local Integration: Claude Desktop and Inspector](05-local-integration-claude-desktop-and-inspector.md)
+- [Next Chapter: Chapter 7: Quality, Security, and Contribution Workflows](07-quality-security-and-contribution-workflows.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/create-python-server-tutorial/07-quality-security-and-contribution-workflows.md b/tutorials/create-python-server-tutorial/07-quality-security-and-contribution-workflows.md
index a90d7855..efe7674f 100644
--- a/tutorials/create-python-server-tutorial/07-quality-security-and-contribution-workflows.md
+++ b/tutorials/create-python-server-tutorial/07-quality-security-and-contribution-workflows.md
@@ -7,6 +7,9 @@ parent: Create Python Server Tutorial
 
 # Chapter 7: Quality, Security, and Contribution Workflows
 
+Welcome to **Chapter 7: Quality, Security, and Contribution Workflows**. In this part of **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter outlines governance controls for scaffold-based MCP server projects.
 
 ## Learning Goals
@@ -26,3 +29,610 @@ This chapter outlines governance controls for scaffold-based MCP server projects
 You now have a governance model for secure and maintainable scaffold-derived projects.
 
 Next: [Chapter 8: Archived Status, Migration, and Long-Term Operations](08-archived-status-migration-and-long-term-operations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- tutorial slug: **create-python-server-tutorial**
+- chapter focus: **Chapter 7: Quality, Security, and Contribution Workflows**
+- system context: **Create Python Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Quality, Security, and Contribution Workflows`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Create Python Server README](https://github.com/modelcontextprotocol/create-python-server/blob/main/README.md)
+- [Template README](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/README.md.jinja2)
+- [Template Server Implementation](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/server.py.jinja2)
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-python-server/blob/main/CONTRIBUTING.md)
+- [Security Policy](https://github.com/modelcontextprotocol/create-python-server/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [MCP Quickstart Resources Tutorial](../mcp-quickstart-resources-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [MCP Inspector Tutorial](../mcp-inspector-tutorial/)
+- [Chapter 1: Getting Started and Scaffolding Workflow](01-getting-started-and-scaffolding-workflow.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Quality, Security, and Contribution Workflows`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 7: Quality, Security, and Contribution Workflows
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Quality, Security, and Contribution Workflows` as an operating subsystem inside **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Quality, Security, and Contribution Workflows` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Create Python Server README](https://github.com/modelcontextprotocol/create-python-server/blob/main/README.md)
+  Why it matters: authoritative reference on `Create Python Server README` (github.com).
+- [Template README](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/README.md.jinja2)
+  Why it matters: authoritative reference on `Template README` (github.com).
+- [Template Server Implementation](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/server.py.jinja2)
+  Why it matters: authoritative reference on `Template Server Implementation` (github.com).
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-python-server/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Security Policy](https://github.com/modelcontextprotocol/create-python-server/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Customization and Extension Patterns](06-customization-and-extension-patterns.md)
+- [Next Chapter: Chapter 8: Archived Status, Migration, and Long-Term Operations](08-archived-status-migration-and-long-term-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/create-python-server-tutorial/08-archived-status-migration-and-long-term-operations.md b/tutorials/create-python-server-tutorial/08-archived-status-migration-and-long-term-operations.md
index a6dd6641..e21efe44 100644
--- a/tutorials/create-python-server-tutorial/08-archived-status-migration-and-long-term-operations.md
+++ b/tutorials/create-python-server-tutorial/08-archived-status-migration-and-long-term-operations.md
@@ -7,6 +7,9 @@ parent: Create Python Server Tutorial
 
 # Chapter 8: Archived Status, Migration, and Long-Term Operations
 
+Welcome to **Chapter 8: Archived Status, Migration, and Long-Term Operations**. In this part of **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers long-term maintenance strategy for teams relying on archived scaffolding tooling.
 
 ## Learning Goals
@@ -36,3 +39,597 @@ This chapter covers long-term maintenance strategy for teams relying on archived
 You now have a long-term operating model for scaffold-derived Python MCP services in archived-tool scenarios.
 
 Return to the [Create Python Server Tutorial index](index.md).
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- tutorial slug: **create-python-server-tutorial**
+- chapter focus: **Chapter 8: Archived Status, Migration, and Long-Term Operations**
+- system context: **Create Python Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Archived Status, Migration, and Long-Term Operations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Create Python Server README](https://github.com/modelcontextprotocol/create-python-server/blob/main/README.md)
+- [Template README](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/README.md.jinja2)
+- [Template Server Implementation](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/server.py.jinja2)
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-python-server/blob/main/CONTRIBUTING.md)
+- [Security Policy](https://github.com/modelcontextprotocol/create-python-server/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [MCP Quickstart Resources Tutorial](../mcp-quickstart-resources-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [MCP Inspector Tutorial](../mcp-inspector-tutorial/)
+- [Chapter 1: Getting Started and Scaffolding Workflow](01-getting-started-and-scaffolding-workflow.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Archived Status, Migration, and Long-Term Operations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Archived Status, Migration, and Long-Term Operations
+
+- tutorial context: **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Archived Status, Migration, and Long-Term Operations` as an operating subsystem inside **Create Python Server Tutorial: Scaffold and Ship MCP Servers with uvx**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Archived Status, Migration, and Long-Term Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Create Python Server README](https://github.com/modelcontextprotocol/create-python-server/blob/main/README.md)
+  Why it matters: authoritative reference on `Create Python Server README` (github.com).
+- [Template README](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/README.md.jinja2)
+  Why it matters: authoritative reference on `Template README` (github.com).
+- [Template Server Implementation](https://github.com/modelcontextprotocol/create-python-server/blob/main/src/create_mcp_server/template/server.py.jinja2)
+  Why it matters: authoritative reference on `Template Server Implementation` (github.com).
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-python-server/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Security Policy](https://github.com/modelcontextprotocol/create-python-server/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Quality, Security, and Contribution Workflows](07-quality-security-and-contribution-workflows.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/create-typescript-server-tutorial/01-getting-started-and-scaffolding-flow.md b/tutorials/create-typescript-server-tutorial/01-getting-started-and-scaffolding-flow.md
index 41c04432..71f8998f 100644
--- a/tutorials/create-typescript-server-tutorial/01-getting-started-and-scaffolding-flow.md
+++ b/tutorials/create-typescript-server-tutorial/01-getting-started-and-scaffolding-flow.md
@@ -7,6 +7,9 @@ parent: Create TypeScript Server Tutorial
 
 # Chapter 1: Getting Started and Scaffolding Flow
 
+Welcome to **Chapter 1: Getting Started and Scaffolding Flow**. In this part of **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers first-run setup for new TypeScript MCP server projects.
 
 ## Learning Goals
@@ -33,3 +36,604 @@ Then run `npm install`, `npm run build`, and optionally `npm run watch` for iter
 You now have a reliable baseline for generating TypeScript MCP servers.
 
 Next: [Chapter 2: Generated Structure and Build Pipeline](02-generated-structure-and-build-pipeline.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- tutorial slug: **create-typescript-server-tutorial**
+- chapter focus: **Chapter 1: Getting Started and Scaffolding Flow**
+- system context: **Create Typescript Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started and Scaffolding Flow`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Create TypeScript Server README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/README.md)
+- [Template README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/README.md.ejs)
+- [Template Server Source](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/src/index.ts.ejs)
+- [Template Package Metadata](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/package.json.ejs)
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/CONTRIBUTING.md)
+- [Security Policy](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP TypeScript SDK Tutorial](../mcp-typescript-sdk-tutorial/)
+- [Create Python Server Tutorial](../create-python-server-tutorial/)
+- [MCP Quickstart Resources Tutorial](../mcp-quickstart-resources-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started and Scaffolding Flow](01-getting-started-and-scaffolding-flow.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started and Scaffolding Flow`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started and Scaffolding Flow
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `server`, `modelcontextprotocol`, `create` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started and Scaffolding Flow` as an operating subsystem inside **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started and Scaffolding Flow` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `server`.
+2. **Input normalization**: shape incoming data so `modelcontextprotocol` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `create`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Create TypeScript Server README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/README.md)
+  Why it matters: authoritative reference on `Create TypeScript Server README` (github.com).
+- [Template README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/README.md.ejs)
+  Why it matters: authoritative reference on `Template README` (github.com).
+- [Template Server Source](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/src/index.ts.ejs)
+  Why it matters: authoritative reference on `Template Server Source` (github.com).
+- [Template Package Metadata](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/package.json.ejs)
+  Why it matters: authoritative reference on `Template Package Metadata` (github.com).
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Security Policy](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `server` and `modelcontextprotocol` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Generated Structure and Build Pipeline](02-generated-structure-and-build-pipeline.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/create-typescript-server-tutorial/02-generated-structure-and-build-pipeline.md b/tutorials/create-typescript-server-tutorial/02-generated-structure-and-build-pipeline.md
index 43fd8172..eb0eeca5 100644
--- a/tutorials/create-typescript-server-tutorial/02-generated-structure-and-build-pipeline.md
+++ b/tutorials/create-typescript-server-tutorial/02-generated-structure-and-build-pipeline.md
@@ -7,6 +7,9 @@ parent: Create TypeScript Server Tutorial
 
 # Chapter 2: Generated Structure and Build Pipeline
 
+Welcome to **Chapter 2: Generated Structure and Build Pipeline**. In this part of **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains scaffold output and build toolchain expectations.
 
 ## Learning Goals
@@ -26,3 +29,613 @@ This chapter explains scaffold output and build toolchain expectations.
 You now have structural and build-level orientation for generated projects.
 
 Next: [Chapter 3: Template MCP Primitives: Resources, Tools, Prompts](03-template-mcp-primitives-resources-tools-prompts.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: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- tutorial slug: **create-typescript-server-tutorial**
+- chapter focus: **Chapter 2: Generated Structure and Build Pipeline**
+- system context: **Create Typescript Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Generated Structure and Build Pipeline`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Create TypeScript Server README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/README.md)
+- [Template README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/README.md.ejs)
+- [Template Server Source](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/src/index.ts.ejs)
+- [Template Package Metadata](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/package.json.ejs)
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/CONTRIBUTING.md)
+- [Security Policy](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP TypeScript SDK Tutorial](../mcp-typescript-sdk-tutorial/)
+- [Create Python Server Tutorial](../create-python-server-tutorial/)
+- [MCP Quickstart Resources Tutorial](../mcp-quickstart-resources-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started and Scaffolding Flow](01-getting-started-and-scaffolding-flow.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Generated Structure and Build Pipeline`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 2: Generated Structure and Build Pipeline
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Generated Structure and Build Pipeline` as an operating subsystem inside **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Generated Structure and Build Pipeline` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Create TypeScript Server README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/README.md)
+  Why it matters: authoritative reference on `Create TypeScript Server README` (github.com).
+- [Template README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/README.md.ejs)
+  Why it matters: authoritative reference on `Template README` (github.com).
+- [Template Server Source](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/src/index.ts.ejs)
+  Why it matters: authoritative reference on `Template Server Source` (github.com).
+- [Template Package Metadata](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/package.json.ejs)
+  Why it matters: authoritative reference on `Template Package Metadata` (github.com).
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Security Policy](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started and Scaffolding Flow](01-getting-started-and-scaffolding-flow.md)
+- [Next Chapter: Chapter 3: Template MCP Primitives: Resources, Tools, Prompts](03-template-mcp-primitives-resources-tools-prompts.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/create-typescript-server-tutorial/03-template-mcp-primitives-resources-tools-prompts.md b/tutorials/create-typescript-server-tutorial/03-template-mcp-primitives-resources-tools-prompts.md
index 4744f554..e7d8f3e3 100644
--- a/tutorials/create-typescript-server-tutorial/03-template-mcp-primitives-resources-tools-prompts.md
+++ b/tutorials/create-typescript-server-tutorial/03-template-mcp-primitives-resources-tools-prompts.md
@@ -7,6 +7,9 @@ parent: Create TypeScript Server Tutorial
 
 # Chapter 3: Template MCP Primitives: Resources, Tools, Prompts
 
+Welcome to **Chapter 3: Template MCP Primitives: Resources, Tools, Prompts**. In this part of **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter examines generated primitive handlers and protocol mapping.
 
 ## Learning Goals
@@ -26,3 +29,613 @@ This chapter examines generated primitive handlers and protocol mapping.
 You now have a primitive-level model for evolving generated TypeScript server code.
 
 Next: [Chapter 4: Configuration, Metadata, and Packaging](04-configuration-metadata-and-packaging.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- tutorial slug: **create-typescript-server-tutorial**
+- chapter focus: **Chapter 3: Template MCP Primitives: Resources, Tools, Prompts**
+- system context: **Create Typescript Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Template MCP Primitives: Resources, Tools, Prompts`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Create TypeScript Server README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/README.md)
+- [Template README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/README.md.ejs)
+- [Template Server Source](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/src/index.ts.ejs)
+- [Template Package Metadata](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/package.json.ejs)
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/CONTRIBUTING.md)
+- [Security Policy](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP TypeScript SDK Tutorial](../mcp-typescript-sdk-tutorial/)
+- [Create Python Server Tutorial](../create-python-server-tutorial/)
+- [MCP Quickstart Resources Tutorial](../mcp-quickstart-resources-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started and Scaffolding Flow](01-getting-started-and-scaffolding-flow.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Template MCP Primitives: Resources, Tools, Prompts`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 3: Template MCP Primitives: Resources, Tools, Prompts
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Template MCP Primitives: Resources, Tools, Prompts` as an operating subsystem inside **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Template MCP Primitives: Resources, Tools, Prompts` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Create TypeScript Server README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/README.md)
+  Why it matters: authoritative reference on `Create TypeScript Server README` (github.com).
+- [Template README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/README.md.ejs)
+  Why it matters: authoritative reference on `Template README` (github.com).
+- [Template Server Source](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/src/index.ts.ejs)
+  Why it matters: authoritative reference on `Template Server Source` (github.com).
+- [Template Package Metadata](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/package.json.ejs)
+  Why it matters: authoritative reference on `Template Package Metadata` (github.com).
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Security Policy](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Generated Structure and Build Pipeline](02-generated-structure-and-build-pipeline.md)
+- [Next Chapter: Chapter 4: Configuration, Metadata, and Packaging](04-configuration-metadata-and-packaging.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/create-typescript-server-tutorial/04-configuration-metadata-and-packaging.md b/tutorials/create-typescript-server-tutorial/04-configuration-metadata-and-packaging.md
index b684e7b3..376cec23 100644
--- a/tutorials/create-typescript-server-tutorial/04-configuration-metadata-and-packaging.md
+++ b/tutorials/create-typescript-server-tutorial/04-configuration-metadata-and-packaging.md
@@ -7,6 +7,9 @@ parent: Create TypeScript Server Tutorial
 
 # Chapter 4: Configuration, Metadata, and Packaging
 
+Welcome to **Chapter 4: Configuration, Metadata, and Packaging**. In this part of **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on package metadata and release-shape decisions.
 
 ## Learning Goals
@@ -26,3 +29,613 @@ This chapter focuses on package metadata and release-shape decisions.
 You now have a packaging and metadata strategy for generated MCP TypeScript servers.
 
 Next: [Chapter 5: Development Workflows: Build, Watch, and Link](05-development-workflows-build-watch-and-link.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: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- tutorial slug: **create-typescript-server-tutorial**
+- chapter focus: **Chapter 4: Configuration, Metadata, and Packaging**
+- system context: **Create Typescript Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Configuration, Metadata, and Packaging`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Create TypeScript Server README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/README.md)
+- [Template README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/README.md.ejs)
+- [Template Server Source](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/src/index.ts.ejs)
+- [Template Package Metadata](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/package.json.ejs)
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/CONTRIBUTING.md)
+- [Security Policy](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP TypeScript SDK Tutorial](../mcp-typescript-sdk-tutorial/)
+- [Create Python Server Tutorial](../create-python-server-tutorial/)
+- [MCP Quickstart Resources Tutorial](../mcp-quickstart-resources-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started and Scaffolding Flow](01-getting-started-and-scaffolding-flow.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Configuration, Metadata, and Packaging`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 4: Configuration, Metadata, and Packaging
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Configuration, Metadata, and Packaging` as an operating subsystem inside **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Configuration, Metadata, and Packaging` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Create TypeScript Server README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/README.md)
+  Why it matters: authoritative reference on `Create TypeScript Server README` (github.com).
+- [Template README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/README.md.ejs)
+  Why it matters: authoritative reference on `Template README` (github.com).
+- [Template Server Source](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/src/index.ts.ejs)
+  Why it matters: authoritative reference on `Template Server Source` (github.com).
+- [Template Package Metadata](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/package.json.ejs)
+  Why it matters: authoritative reference on `Template Package Metadata` (github.com).
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Security Policy](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Template MCP Primitives: Resources, Tools, Prompts](03-template-mcp-primitives-resources-tools-prompts.md)
+- [Next Chapter: Chapter 5: Development Workflows: Build, Watch, and Link](05-development-workflows-build-watch-and-link.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/create-typescript-server-tutorial/05-development-workflows-build-watch-and-link.md b/tutorials/create-typescript-server-tutorial/05-development-workflows-build-watch-and-link.md
index 05a3bb80..cde40680 100644
--- a/tutorials/create-typescript-server-tutorial/05-development-workflows-build-watch-and-link.md
+++ b/tutorials/create-typescript-server-tutorial/05-development-workflows-build-watch-and-link.md
@@ -7,6 +7,9 @@ parent: Create TypeScript Server Tutorial
 
 # Chapter 5: Development Workflows: Build, Watch, and Link
 
+Welcome to **Chapter 5: Development Workflows: Build, Watch, and Link**. In this part of **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers practical day-to-day development loops.
 
 ## Learning Goals
@@ -32,3 +35,601 @@ This chapter covers practical day-to-day development loops.
 You now have a repeatable development loop for generated server projects.
 
 Next: [Chapter 6: Debugging and Local Integration](06-debugging-and-local-integration.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- tutorial slug: **create-typescript-server-tutorial**
+- chapter focus: **Chapter 5: Development Workflows: Build, Watch, and Link**
+- system context: **Create Typescript Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Development Workflows: Build, Watch, and Link`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Create TypeScript Server README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/README.md)
+- [Template README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/README.md.ejs)
+- [Template Server Source](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/src/index.ts.ejs)
+- [Template Package Metadata](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/package.json.ejs)
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/CONTRIBUTING.md)
+- [Security Policy](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP TypeScript SDK Tutorial](../mcp-typescript-sdk-tutorial/)
+- [Create Python Server Tutorial](../create-python-server-tutorial/)
+- [MCP Quickstart Resources Tutorial](../mcp-quickstart-resources-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started and Scaffolding Flow](01-getting-started-and-scaffolding-flow.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Development Workflows: Build, Watch, and Link`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Development Workflows: Build, Watch, and Link
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Development Workflows: Build, Watch, and Link` as an operating subsystem inside **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Development Workflows: Build, Watch, and Link` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Create TypeScript Server README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/README.md)
+  Why it matters: authoritative reference on `Create TypeScript Server README` (github.com).
+- [Template README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/README.md.ejs)
+  Why it matters: authoritative reference on `Template README` (github.com).
+- [Template Server Source](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/src/index.ts.ejs)
+  Why it matters: authoritative reference on `Template Server Source` (github.com).
+- [Template Package Metadata](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/package.json.ejs)
+  Why it matters: authoritative reference on `Template Package Metadata` (github.com).
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Security Policy](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Configuration, Metadata, and Packaging](04-configuration-metadata-and-packaging.md)
+- [Next Chapter: Chapter 6: Debugging and Local Integration](06-debugging-and-local-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/create-typescript-server-tutorial/06-debugging-and-local-integration.md b/tutorials/create-typescript-server-tutorial/06-debugging-and-local-integration.md
index 858287b0..417b547f 100644
--- a/tutorials/create-typescript-server-tutorial/06-debugging-and-local-integration.md
+++ b/tutorials/create-typescript-server-tutorial/06-debugging-and-local-integration.md
@@ -7,6 +7,9 @@ parent: Create TypeScript Server Tutorial
 
 # Chapter 6: Debugging and Local Integration
 
+Welcome to **Chapter 6: Debugging and Local Integration**. In this part of **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains debugging and local client integration patterns.
 
 ## Learning Goals
@@ -26,3 +29,613 @@ This chapter explains debugging and local client integration patterns.
 You now have a debugging and local-validation strategy for scaffolded TypeScript servers.
 
 Next: [Chapter 7: Quality, Security, and Contribution Practices](07-quality-security-and-contribution-practices.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- tutorial slug: **create-typescript-server-tutorial**
+- chapter focus: **Chapter 6: Debugging and Local Integration**
+- system context: **Create Typescript Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Debugging and Local Integration`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Create TypeScript Server README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/README.md)
+- [Template README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/README.md.ejs)
+- [Template Server Source](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/src/index.ts.ejs)
+- [Template Package Metadata](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/package.json.ejs)
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/CONTRIBUTING.md)
+- [Security Policy](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP TypeScript SDK Tutorial](../mcp-typescript-sdk-tutorial/)
+- [Create Python Server Tutorial](../create-python-server-tutorial/)
+- [MCP Quickstart Resources Tutorial](../mcp-quickstart-resources-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started and Scaffolding Flow](01-getting-started-and-scaffolding-flow.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Debugging and Local Integration`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 6: Debugging and Local Integration
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Debugging and Local Integration` as an operating subsystem inside **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Debugging and Local Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Create TypeScript Server README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/README.md)
+  Why it matters: authoritative reference on `Create TypeScript Server README` (github.com).
+- [Template README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/README.md.ejs)
+  Why it matters: authoritative reference on `Template README` (github.com).
+- [Template Server Source](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/src/index.ts.ejs)
+  Why it matters: authoritative reference on `Template Server Source` (github.com).
+- [Template Package Metadata](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/package.json.ejs)
+  Why it matters: authoritative reference on `Template Package Metadata` (github.com).
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Security Policy](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Development Workflows: Build, Watch, and Link](05-development-workflows-build-watch-and-link.md)
+- [Next Chapter: Chapter 7: Quality, Security, and Contribution Practices](07-quality-security-and-contribution-practices.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/create-typescript-server-tutorial/07-quality-security-and-contribution-practices.md b/tutorials/create-typescript-server-tutorial/07-quality-security-and-contribution-practices.md
index f4e5dc6e..844d718b 100644
--- a/tutorials/create-typescript-server-tutorial/07-quality-security-and-contribution-practices.md
+++ b/tutorials/create-typescript-server-tutorial/07-quality-security-and-contribution-practices.md
@@ -7,6 +7,9 @@ parent: Create TypeScript Server Tutorial
 
 # Chapter 7: Quality, Security, and Contribution Practices
 
+Welcome to **Chapter 7: Quality, Security, and Contribution Practices**. In this part of **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers governance guardrails for generated-project ecosystems.
 
 ## Learning Goals
@@ -26,3 +29,613 @@ This chapter covers governance guardrails for generated-project ecosystems.
 You now have a governance baseline for secure and maintainable scaffold-derived projects.
 
 Next: [Chapter 8: Archived Status, Migration, and Long-Term Strategy](08-archived-status-migration-and-long-term-strategy.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- tutorial slug: **create-typescript-server-tutorial**
+- chapter focus: **Chapter 7: Quality, Security, and Contribution Practices**
+- system context: **Create Typescript Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Quality, Security, and Contribution 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
+
+- [Create TypeScript Server README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/README.md)
+- [Template README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/README.md.ejs)
+- [Template Server Source](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/src/index.ts.ejs)
+- [Template Package Metadata](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/package.json.ejs)
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/CONTRIBUTING.md)
+- [Security Policy](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP TypeScript SDK Tutorial](../mcp-typescript-sdk-tutorial/)
+- [Create Python Server Tutorial](../create-python-server-tutorial/)
+- [MCP Quickstart Resources Tutorial](../mcp-quickstart-resources-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started and Scaffolding Flow](01-getting-started-and-scaffolding-flow.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Quality, Security, and Contribution Practices`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 7: Quality, Security, and Contribution Practices
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Quality, Security, and Contribution Practices` as an operating subsystem inside **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Quality, Security, and Contribution Practices` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Create TypeScript Server README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/README.md)
+  Why it matters: authoritative reference on `Create TypeScript Server README` (github.com).
+- [Template README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/README.md.ejs)
+  Why it matters: authoritative reference on `Template README` (github.com).
+- [Template Server Source](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/src/index.ts.ejs)
+  Why it matters: authoritative reference on `Template Server Source` (github.com).
+- [Template Package Metadata](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/package.json.ejs)
+  Why it matters: authoritative reference on `Template Package Metadata` (github.com).
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Security Policy](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Debugging and Local Integration](06-debugging-and-local-integration.md)
+- [Next Chapter: Chapter 8: Archived Status, Migration, and Long-Term Strategy](08-archived-status-migration-and-long-term-strategy.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/create-typescript-server-tutorial/08-archived-status-migration-and-long-term-strategy.md b/tutorials/create-typescript-server-tutorial/08-archived-status-migration-and-long-term-strategy.md
index e4dfdd4d..37fcae9c 100644
--- a/tutorials/create-typescript-server-tutorial/08-archived-status-migration-and-long-term-strategy.md
+++ b/tutorials/create-typescript-server-tutorial/08-archived-status-migration-and-long-term-strategy.md
@@ -7,6 +7,9 @@ parent: Create TypeScript Server Tutorial
 
 # Chapter 8: Archived Status, Migration, and Long-Term Strategy
 
+Welcome to **Chapter 8: Archived Status, Migration, and Long-Term Strategy**. In this part of **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter defines long-term maintenance strategy when relying on archived scaffolding tooling.
 
 ## Learning Goals
@@ -36,3 +39,600 @@ This chapter defines long-term maintenance strategy when relying on archived sca
 You now have a pragmatic long-term strategy for scaffold-based TypeScript MCP server development.
 
 Return to the [Create TypeScript Server Tutorial index](index.md).
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- tutorial slug: **create-typescript-server-tutorial**
+- chapter focus: **Chapter 8: Archived Status, Migration, and Long-Term Strategy**
+- system context: **Create Typescript Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Archived Status, Migration, and Long-Term Strategy`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Create TypeScript Server README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/README.md)
+- [Template README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/README.md.ejs)
+- [Template Server Source](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/src/index.ts.ejs)
+- [Template Package Metadata](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/package.json.ejs)
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/CONTRIBUTING.md)
+- [Security Policy](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/SECURITY.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP TypeScript SDK Tutorial](../mcp-typescript-sdk-tutorial/)
+- [Create Python Server Tutorial](../create-python-server-tutorial/)
+- [MCP Quickstart Resources Tutorial](../mcp-quickstart-resources-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started and Scaffolding Flow](01-getting-started-and-scaffolding-flow.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Archived Status, Migration, and Long-Term Strategy`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Archived Status, Migration, and Long-Term Strategy
+
+- tutorial context: **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Archived Status, Migration, and Long-Term Strategy` as an operating subsystem inside **Create TypeScript Server Tutorial: Scaffold MCP Servers with TypeScript Templates**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Archived Status, Migration, and Long-Term Strategy` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Create TypeScript Server README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/README.md)
+  Why it matters: authoritative reference on `Create TypeScript Server README` (github.com).
+- [Template README](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/README.md.ejs)
+  Why it matters: authoritative reference on `Template README` (github.com).
+- [Template Server Source](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/src/index.ts.ejs)
+  Why it matters: authoritative reference on `Template Server Source` (github.com).
+- [Template Package Metadata](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/template/package.json.ejs)
+  Why it matters: authoritative reference on `Template Package Metadata` (github.com).
+- [Contributing Guide](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Security Policy](https://github.com/modelcontextprotocol/create-typescript-server/blob/main/SECURITY.md)
+  Why it matters: authoritative reference on `Security Policy` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Quality, Security, and Contribution Practices](07-quality-security-and-contribution-practices.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/crewai-tutorial/01-getting-started.md b/tutorials/crewai-tutorial/01-getting-started.md
index 9f259866..c0f9a0cb 100644
--- a/tutorials/crewai-tutorial/01-getting-started.md
+++ b/tutorials/crewai-tutorial/01-getting-started.md
@@ -406,3 +406,48 @@ Ready to explore specialized agent roles? In [Chapter 2: Agent Roles & Specializ
 4. Implement error handling for your crew executions
 
 *Remember: Great crews start with well-defined roles and clear communication patterns!*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Agent`, `crew`, `Crew` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with CrewAI` as an operating subsystem inside **CrewAI Tutorial: Building Collaborative AI Agent Teams**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `crewai`, `agent`, `Task` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with CrewAI` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Agent`.
+2. **Input normalization**: shape incoming data so `crew` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Crew`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/crewAIInc/crewAI)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Agent` and `crew` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Agent Roles & Specializations](02-agent-roles.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/crewai-tutorial/02-agent-roles.md b/tutorials/crewai-tutorial/02-agent-roles.md
index 78aea736..3dfdeb0c 100644
--- a/tutorials/crewai-tutorial/02-agent-roles.md
+++ b/tutorials/crewai-tutorial/02-agent-roles.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Agent Roles & Specializations
 
+Welcome to **Chapter 2: Agent Roles & Specializations**. In this part of **CrewAI Tutorial: Building Collaborative AI Agent Teams**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Welcome back! Now that you understand the basics of CrewAI, let's explore how to create specialized agents with distinct roles and expertise areas. Effective agent specialization is the key to building powerful collaborative AI teams.
 
 ## Understanding Agent Roles
@@ -519,3 +522,49 @@ Ready to tackle complex task planning? In [Chapter 3: Task Definition & Planning
 ---
 
 **Key Takeaway:** The most effective AI crews are built with clearly defined roles, complementary skills, and intelligent coordination mechanisms. Specialization enables agents to excel in their areas of expertise while working seamlessly together toward common goals.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `agent`, `expertise` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Agent Roles & Specializations` as an operating subsystem inside **CrewAI Tutorial: Building Collaborative AI Agent Teams**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `skill`, `role`, `goal` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Agent Roles & Specializations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `agent` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `expertise`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/crewAIInc/crewAI)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `agent` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with CrewAI](01-getting-started.md)
+- [Next Chapter: Chapter 3: Task Definition & Planning](03-task-planning.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/crewai-tutorial/03-task-planning.md b/tutorials/crewai-tutorial/03-task-planning.md
index 30399539..5fdd3649 100644
--- a/tutorials/crewai-tutorial/03-task-planning.md
+++ b/tutorials/crewai-tutorial/03-task-planning.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Task Definition & Planning
 
+Welcome to **Chapter 3: Task Definition & Planning**. In this part of **CrewAI Tutorial: Building Collaborative AI Agent Teams**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Mastering task definition and planning is crucial for building effective AI crews. This chapter explores how to break down complex objectives into actionable tasks that agents can execute collaboratively.
 
 ## Task Definition Framework
@@ -450,3 +453,49 @@ Ready to equip your agents with tools? In [Chapter 4: Tool Integration](04-tool-
 ---
 
 **Key Takeaway:** Effective task planning is the foundation of successful AI crew execution. Well-planned tasks with clear dependencies, proper resource allocation, and continuous monitoring enable agents to work efficiently toward complex objectives.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `task`, `agent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Task Definition & Planning` as an operating subsystem inside **CrewAI Tutorial: Building Collaborative AI Agent Teams**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `CrewTask`, `tasks`, `List` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Task Definition & Planning` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `task` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `agent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/crewAIInc/crewAI)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `task` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Agent Roles & Specializations](02-agent-roles.md)
+- [Next Chapter: Chapter 4: Tool Integration](04-tool-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/crewai-tutorial/04-tool-integration.md b/tutorials/crewai-tutorial/04-tool-integration.md
index 76b63566..ac731c95 100644
--- a/tutorials/crewai-tutorial/04-tool-integration.md
+++ b/tutorials/crewai-tutorial/04-tool-integration.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Tool Integration
 
+Welcome to **Chapter 4: Tool Integration**. In this part of **CrewAI Tutorial: Building Collaborative AI Agent Teams**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Tools are the extensions that give AI agents superpowers. This chapter explores how to integrate external tools, APIs, and services into your CrewAI agents to expand their capabilities and enable real-world interactions.
 
 ## Tool Integration Fundamentals
@@ -501,3 +504,49 @@ Ready to manage crew communication? In [Chapter 5: Crew Communication](05-crew-c
 ---
 
 **Key Takeaway:** Tools are the bridge between AI agents and the real world. Well-designed tools with proper error handling, monitoring, and integration enable agents to accomplish tasks that would otherwise be impossible.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `tool`, `name` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Tool Integration` as an operating subsystem inside **CrewAI Tutorial: Building Collaborative AI Agent Teams**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `tools`, `CrewTool`, `description` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Tool Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `tool` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `name`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/crewAIInc/crewAI)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `tool` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Task Definition & Planning](03-task-planning.md)
+- [Next Chapter: Chapter 5: Crew Communication](05-crew-communication.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/crewai-tutorial/05-crew-communication.md b/tutorials/crewai-tutorial/05-crew-communication.md
index aa0b9117..2b9805d8 100644
--- a/tutorials/crewai-tutorial/05-crew-communication.md
+++ b/tutorials/crewai-tutorial/05-crew-communication.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Crew Communication
 
+Welcome to **Chapter 5: Crew Communication**. In this part of **CrewAI Tutorial: Building Collaborative AI Agent Teams**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Effective communication is the lifeblood of successful AI crews. This chapter explores how agents communicate, share information, coordinate activities, and maintain context across complex multi-agent workflows.
 
 ## Communication Fundamentals
@@ -510,3 +513,49 @@ Ready to manage different execution processes? In [Chapter 6: Process Management
 ---
 
 **Key Takeaway:** Effective communication is the foundation of successful multi-agent collaboration. Well-designed communication protocols, context management, and coordination patterns enable agents to work together efficiently toward complex objectives.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `Dict`, `agent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Crew Communication` as an operating subsystem inside **CrewAI Tutorial: Building Collaborative AI Agent Teams**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `context`, `task`, `sender` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Crew Communication` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `Dict` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `agent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/crewAIInc/crewAI)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `Dict` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Tool Integration](04-tool-integration.md)
+- [Next Chapter: Chapter 6: Process Management](06-process-management.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/crewai-tutorial/06-process-management.md b/tutorials/crewai-tutorial/06-process-management.md
index 7a84b73d..ec5ab5ea 100644
--- a/tutorials/crewai-tutorial/06-process-management.md
+++ b/tutorials/crewai-tutorial/06-process-management.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Process Management
 
+Welcome to **Chapter 6: Process Management**. In this part of **CrewAI Tutorial: Building Collaborative AI Agent Teams**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Different tasks require different execution approaches. This chapter explores various process management patterns in CrewAI, from sequential workflows to complex parallel processing and adaptive execution strategies.
 
 ## Process Types
@@ -511,3 +514,49 @@ Ready for advanced multi-crew systems? In [Chapter 7: Advanced Crew Patterns](07
 ---
 
 **Key Takeaway:** Different tasks require different execution approaches. Understanding when to use sequential, parallel, hierarchical, or adaptive processing is crucial for building efficient and effective AI crew systems.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `task`, `process_id` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Process Management` as an operating subsystem inside **CrewAI Tutorial: Building Collaborative AI Agent Teams**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `process`, `result`, `tasks` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Process Management` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `task` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `process_id`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/crewAIInc/crewAI)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `task` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Crew Communication](05-crew-communication.md)
+- [Next Chapter: Chapter 7: Advanced Crew Patterns](07-advanced-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/crewai-tutorial/07-advanced-patterns.md b/tutorials/crewai-tutorial/07-advanced-patterns.md
index 8c74a193..bdc35672 100644
--- a/tutorials/crewai-tutorial/07-advanced-patterns.md
+++ b/tutorials/crewai-tutorial/07-advanced-patterns.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Advanced Crew Patterns
 
+Welcome to **Chapter 7: Advanced Crew Patterns**. In this part of **CrewAI Tutorial: Building Collaborative AI Agent Teams**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explores sophisticated multi-crew architectures, specialized formations, and advanced collaboration patterns that enable complex problem-solving at scale.
 
 ## Multi-Crew Architectures
@@ -528,3 +531,49 @@ Ready for production deployment? In [Chapter 8: Production Deployment](08-produc
 ---
 
 **Key Takeaway:** Advanced crew patterns enable sophisticated problem-solving at scale. From federated systems to hierarchical organizations, these patterns allow AI crews to tackle increasingly complex challenges through specialized collaboration and intelligent scaling.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `crew`, `task` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Advanced Crew Patterns` as an operating subsystem inside **CrewAI Tutorial: Building Collaborative AI Agent Teams**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Dict`, `Task`, `agents` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Advanced Crew Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `crew` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `task`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/crewAIInc/crewAI)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `crew` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Process Management](06-process-management.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/crewai-tutorial/08-production-deployment.md b/tutorials/crewai-tutorial/08-production-deployment.md
index e76f495f..0bff3a52 100644
--- a/tutorials/crewai-tutorial/08-production-deployment.md
+++ b/tutorials/crewai-tutorial/08-production-deployment.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **CrewAI Tutorial: Building Collaborative AI Agent Teams**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Congratulations! You've mastered CrewAI from basic agents to advanced multi-crew architectures. Now it's time to deploy your AI crew systems to production with robust monitoring, scaling, and maintenance strategies.
 
 ## Production Architecture
@@ -572,3 +575,48 @@ Your CrewAI journey continues with:
 **Final Thought**: CrewAI represents the future of AI collaboration—where specialized agents work together like a well-coordinated team to solve complex problems. You've mastered the fundamentals and are ready to build the next generation of intelligent systems!
 
 *Welcome to the era of collaborative AI! 🚀*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `system_id`, `config` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **CrewAI Tutorial: Building Collaborative AI Agent Teams**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `metrics`, `crew_id`, `Dict` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `system_id` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `config`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/crewAIInc/crewAI)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `system_id` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Advanced Crew Patterns](07-advanced-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/crush-tutorial/01-getting-started.md b/tutorials/crush-tutorial/01-getting-started.md
index 6a6fb505..70432426 100644
--- a/tutorials/crush-tutorial/01-getting-started.md
+++ b/tutorials/crush-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Crush Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets Crush installed and ready for real coding work in minutes.
 
 ## Learning Goals
@@ -58,3 +61,577 @@ crush
 You now have Crush installed and running with a valid provider path.
 
 Next: [Chapter 2: Architecture and Session Model](02-architecture-and-session-model.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- tutorial slug: **crush-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Crush 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
+
+- [Crush Repository](https://github.com/charmbracelet/crush)
+- [Crush README](https://github.com/charmbracelet/crush/blob/main/README.md)
+- [Crush Releases](https://github.com/charmbracelet/crush/releases)
+- [Crush Configuration Schema](https://github.com/charmbracelet/crush/blob/main/schema.json)
+- [Crush Command Loader](https://github.com/charmbracelet/crush/blob/main/internal/commands/commands.go)
+
+### Cross-Tutorial Connection Map
+
+- [Goose Tutorial](../goose-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Kilo Code Tutorial](../kilocode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `crush` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `crush`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Crush Repository](https://github.com/charmbracelet/crush)
+  Why it matters: authoritative reference on `Crush Repository` (github.com).
+- [Crush README](https://github.com/charmbracelet/crush/blob/main/README.md)
+  Why it matters: authoritative reference on `Crush README` (github.com).
+- [Crush Releases](https://github.com/charmbracelet/crush/releases)
+  Why it matters: authoritative reference on `Crush Releases` (github.com).
+- [Crush Configuration Schema](https://github.com/charmbracelet/crush/blob/main/schema.json)
+  Why it matters: authoritative reference on `Crush Configuration Schema` (github.com).
+- [Crush Command Loader](https://github.com/charmbracelet/crush/blob/main/internal/commands/commands.go)
+  Why it matters: authoritative reference on `Crush Command Loader` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `crush` and `crush` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Architecture and Session Model](02-architecture-and-session-model.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/crush-tutorial/02-architecture-and-session-model.md b/tutorials/crush-tutorial/02-architecture-and-session-model.md
index 2f231977..af0e0699 100644
--- a/tutorials/crush-tutorial/02-architecture-and-session-model.md
+++ b/tutorials/crush-tutorial/02-architecture-and-session-model.md
@@ -7,6 +7,9 @@ parent: Crush Tutorial
 
 # Chapter 2: Architecture and Session Model
 
+Welcome to **Chapter 2: Architecture and Session Model**. In this part of **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains the core operating model behind Crush's terminal workflows.
 
 ## Learning Goals
@@ -51,3 +54,586 @@ This lets teams enforce repo-level conventions while preserving personal default
 You now understand how Crush organizes context and configuration across sessions and projects.
 
 Next: [Chapter 3: Providers and Model Configuration](03-providers-and-model-configuration.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- tutorial slug: **crush-tutorial**
+- chapter focus: **Chapter 2: Architecture and Session Model**
+- system context: **Crush Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Architecture and Session 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
+
+- [Crush Repository](https://github.com/charmbracelet/crush)
+- [Crush README](https://github.com/charmbracelet/crush/blob/main/README.md)
+- [Crush Releases](https://github.com/charmbracelet/crush/releases)
+- [Crush Configuration Schema](https://github.com/charmbracelet/crush/blob/main/schema.json)
+- [Crush Command Loader](https://github.com/charmbracelet/crush/blob/main/internal/commands/commands.go)
+
+### Cross-Tutorial Connection Map
+
+- [Goose Tutorial](../goose-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Kilo Code Tutorial](../kilocode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Architecture and Session Model`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Architecture and Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Architecture and Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Architecture and Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Architecture and Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Architecture and Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Architecture and Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Architecture and Session Model
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Architecture and Session Model` as an operating subsystem inside **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Architecture and Session Model` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Crush Repository](https://github.com/charmbracelet/crush)
+  Why it matters: authoritative reference on `Crush Repository` (github.com).
+- [Crush README](https://github.com/charmbracelet/crush/blob/main/README.md)
+  Why it matters: authoritative reference on `Crush README` (github.com).
+- [Crush Releases](https://github.com/charmbracelet/crush/releases)
+  Why it matters: authoritative reference on `Crush Releases` (github.com).
+- [Crush Configuration Schema](https://github.com/charmbracelet/crush/blob/main/schema.json)
+  Why it matters: authoritative reference on `Crush Configuration Schema` (github.com).
+- [Crush Command Loader](https://github.com/charmbracelet/crush/blob/main/internal/commands/commands.go)
+  Why it matters: authoritative reference on `Crush Command Loader` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Providers and Model Configuration](03-providers-and-model-configuration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/crush-tutorial/03-providers-and-model-configuration.md b/tutorials/crush-tutorial/03-providers-and-model-configuration.md
index df920b4f..174608f0 100644
--- a/tutorials/crush-tutorial/03-providers-and-model-configuration.md
+++ b/tutorials/crush-tutorial/03-providers-and-model-configuration.md
@@ -7,6 +7,9 @@ parent: Crush Tutorial
 
 # Chapter 3: Providers and Model Configuration
 
+Welcome to **Chapter 3: Providers and Model Configuration**. In this part of **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers provider setup, model routing, and custom provider definitions.
 
 ## Learning Goals
@@ -57,3 +60,574 @@ Include model metadata such as context window and token defaults when available.
 You now have a predictable strategy for provider selection and model routing in Crush.
 
 Next: [Chapter 4: Permissions and Tool Controls](04-permissions-and-tool-controls.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- tutorial slug: **crush-tutorial**
+- chapter focus: **Chapter 3: Providers and Model Configuration**
+- system context: **Crush Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Providers and Model Configuration`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Crush Repository](https://github.com/charmbracelet/crush)
+- [Crush README](https://github.com/charmbracelet/crush/blob/main/README.md)
+- [Crush Releases](https://github.com/charmbracelet/crush/releases)
+- [Crush Configuration Schema](https://github.com/charmbracelet/crush/blob/main/schema.json)
+- [Crush Command Loader](https://github.com/charmbracelet/crush/blob/main/internal/commands/commands.go)
+
+### Cross-Tutorial Connection Map
+
+- [Goose Tutorial](../goose-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Kilo Code Tutorial](../kilocode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Providers and Model Configuration`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Providers and Model Configuration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Providers and Model Configuration` as an operating subsystem inside **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Providers and Model Configuration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Crush Repository](https://github.com/charmbracelet/crush)
+  Why it matters: authoritative reference on `Crush Repository` (github.com).
+- [Crush README](https://github.com/charmbracelet/crush/blob/main/README.md)
+  Why it matters: authoritative reference on `Crush README` (github.com).
+- [Crush Releases](https://github.com/charmbracelet/crush/releases)
+  Why it matters: authoritative reference on `Crush Releases` (github.com).
+- [Crush Configuration Schema](https://github.com/charmbracelet/crush/blob/main/schema.json)
+  Why it matters: authoritative reference on `Crush Configuration Schema` (github.com).
+- [Crush Command Loader](https://github.com/charmbracelet/crush/blob/main/internal/commands/commands.go)
+  Why it matters: authoritative reference on `Crush Command Loader` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Architecture and Session Model](02-architecture-and-session-model.md)
+- [Next Chapter: Chapter 4: Permissions and Tool Controls](04-permissions-and-tool-controls.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/crush-tutorial/04-permissions-and-tool-controls.md b/tutorials/crush-tutorial/04-permissions-and-tool-controls.md
index 03eeb2a6..29b2838b 100644
--- a/tutorials/crush-tutorial/04-permissions-and-tool-controls.md
+++ b/tutorials/crush-tutorial/04-permissions-and-tool-controls.md
@@ -7,6 +7,9 @@ parent: Crush Tutorial
 
 # Chapter 4: Permissions and Tool Controls
 
+Welcome to **Chapter 4: Permissions and Tool Controls**. In this part of **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers how to define safe execution boundaries without killing productivity.
 
 ## Learning Goals
@@ -43,3 +46,598 @@ This chapter covers how to define safe execution boundaries without killing prod
 You now have a practical control model for balancing Crush autonomy and safety.
 
 Next: [Chapter 5: LSP and MCP Integration](05-lsp-and-mcp-integration.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- tutorial slug: **crush-tutorial**
+- chapter focus: **Chapter 4: Permissions and Tool Controls**
+- system context: **Crush Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Permissions and Tool Controls`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Crush Repository](https://github.com/charmbracelet/crush)
+- [Crush README](https://github.com/charmbracelet/crush/blob/main/README.md)
+- [Crush Releases](https://github.com/charmbracelet/crush/releases)
+- [Crush Configuration Schema](https://github.com/charmbracelet/crush/blob/main/schema.json)
+- [Crush Command Loader](https://github.com/charmbracelet/crush/blob/main/internal/commands/commands.go)
+
+### Cross-Tutorial Connection Map
+
+- [Goose Tutorial](../goose-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Kilo Code Tutorial](../kilocode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Permissions and Tool Controls`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Permissions and Tool Controls
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Permissions and Tool Controls` as an operating subsystem inside **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Permissions and Tool Controls` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Crush Repository](https://github.com/charmbracelet/crush)
+  Why it matters: authoritative reference on `Crush Repository` (github.com).
+- [Crush README](https://github.com/charmbracelet/crush/blob/main/README.md)
+  Why it matters: authoritative reference on `Crush README` (github.com).
+- [Crush Releases](https://github.com/charmbracelet/crush/releases)
+  Why it matters: authoritative reference on `Crush Releases` (github.com).
+- [Crush Configuration Schema](https://github.com/charmbracelet/crush/blob/main/schema.json)
+  Why it matters: authoritative reference on `Crush Configuration Schema` (github.com).
+- [Crush Command Loader](https://github.com/charmbracelet/crush/blob/main/internal/commands/commands.go)
+  Why it matters: authoritative reference on `Crush Command Loader` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Providers and Model Configuration](03-providers-and-model-configuration.md)
+- [Next Chapter: Chapter 5: LSP and MCP Integration](05-lsp-and-mcp-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/crush-tutorial/05-lsp-and-mcp-integration.md b/tutorials/crush-tutorial/05-lsp-and-mcp-integration.md
index 6607d243..c057db6c 100644
--- a/tutorials/crush-tutorial/05-lsp-and-mcp-integration.md
+++ b/tutorials/crush-tutorial/05-lsp-and-mcp-integration.md
@@ -7,6 +7,9 @@ parent: Crush Tutorial
 
 # Chapter 5: LSP and MCP Integration
 
+Welcome to **Chapter 5: LSP and MCP Integration**. In this part of **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains how to extend Crush with richer code intelligence and external tools.
 
 ## Learning Goals
@@ -62,3 +65,578 @@ This chapter explains how to extend Crush with richer code intelligence and exte
 You now know how to wire Crush into language tooling and MCP ecosystems safely.
 
 Next: [Chapter 6: Skills, Commands, and Workflow Customization](06-skills-commands-and-workflow-customization.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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- tutorial slug: **crush-tutorial**
+- chapter focus: **Chapter 5: LSP and MCP Integration**
+- system context: **Crush Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: LSP and MCP Integration`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Crush Repository](https://github.com/charmbracelet/crush)
+- [Crush README](https://github.com/charmbracelet/crush/blob/main/README.md)
+- [Crush Releases](https://github.com/charmbracelet/crush/releases)
+- [Crush Configuration Schema](https://github.com/charmbracelet/crush/blob/main/schema.json)
+- [Crush Command Loader](https://github.com/charmbracelet/crush/blob/main/internal/commands/commands.go)
+
+### Cross-Tutorial Connection Map
+
+- [Goose Tutorial](../goose-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Kilo Code Tutorial](../kilocode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: LSP and MCP Integration`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: LSP and MCP Integration
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `command`, `schema`, `https` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: LSP and MCP Integration` as an operating subsystem inside **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `charm`, `land`, `crush` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: LSP and MCP Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `command`.
+2. **Input normalization**: shape incoming data so `schema` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `https`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Crush Repository](https://github.com/charmbracelet/crush)
+  Why it matters: authoritative reference on `Crush Repository` (github.com).
+- [Crush README](https://github.com/charmbracelet/crush/blob/main/README.md)
+  Why it matters: authoritative reference on `Crush README` (github.com).
+- [Crush Releases](https://github.com/charmbracelet/crush/releases)
+  Why it matters: authoritative reference on `Crush Releases` (github.com).
+- [Crush Configuration Schema](https://github.com/charmbracelet/crush/blob/main/schema.json)
+  Why it matters: authoritative reference on `Crush Configuration Schema` (github.com).
+- [Crush Command Loader](https://github.com/charmbracelet/crush/blob/main/internal/commands/commands.go)
+  Why it matters: authoritative reference on `Crush Command Loader` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `command` and `schema` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Permissions and Tool Controls](04-permissions-and-tool-controls.md)
+- [Next Chapter: Chapter 6: Skills, Commands, and Workflow Customization](06-skills-commands-and-workflow-customization.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/crush-tutorial/06-skills-commands-and-workflow-customization.md b/tutorials/crush-tutorial/06-skills-commands-and-workflow-customization.md
index f9d1684b..dac4cc88 100644
--- a/tutorials/crush-tutorial/06-skills-commands-and-workflow-customization.md
+++ b/tutorials/crush-tutorial/06-skills-commands-and-workflow-customization.md
@@ -7,6 +7,9 @@ parent: Crush Tutorial
 
 # Chapter 6: Skills, Commands, and Workflow Customization
 
+Welcome to **Chapter 6: Skills, Commands, and Workflow Customization**. In this part of **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter turns Crush into a reusable engineering system rather than a one-off assistant.
 
 ## Learning Goals
@@ -52,3 +55,586 @@ This supports personal command libraries plus project-scoped commands.
 You now have the building blocks for durable, reusable Crush workflows.
 
 Next: [Chapter 7: Logs, Debugging, and Operations](07-logs-debugging-and-operations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- tutorial slug: **crush-tutorial**
+- chapter focus: **Chapter 6: Skills, Commands, and Workflow Customization**
+- system context: **Crush Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Skills, Commands, and Workflow Customization`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Crush Repository](https://github.com/charmbracelet/crush)
+- [Crush README](https://github.com/charmbracelet/crush/blob/main/README.md)
+- [Crush Releases](https://github.com/charmbracelet/crush/releases)
+- [Crush Configuration Schema](https://github.com/charmbracelet/crush/blob/main/schema.json)
+- [Crush Command Loader](https://github.com/charmbracelet/crush/blob/main/internal/commands/commands.go)
+
+### Cross-Tutorial Connection Map
+
+- [Goose Tutorial](../goose-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Kilo Code Tutorial](../kilocode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Skills, Commands, and Workflow Customization`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Skills, Commands, and Workflow Customization
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Skills, Commands, and Workflow Customization` as an operating subsystem inside **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Skills, Commands, and Workflow Customization` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Crush Repository](https://github.com/charmbracelet/crush)
+  Why it matters: authoritative reference on `Crush Repository` (github.com).
+- [Crush README](https://github.com/charmbracelet/crush/blob/main/README.md)
+  Why it matters: authoritative reference on `Crush README` (github.com).
+- [Crush Releases](https://github.com/charmbracelet/crush/releases)
+  Why it matters: authoritative reference on `Crush Releases` (github.com).
+- [Crush Configuration Schema](https://github.com/charmbracelet/crush/blob/main/schema.json)
+  Why it matters: authoritative reference on `Crush Configuration Schema` (github.com).
+- [Crush Command Loader](https://github.com/charmbracelet/crush/blob/main/internal/commands/commands.go)
+  Why it matters: authoritative reference on `Crush Command Loader` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: LSP and MCP Integration](05-lsp-and-mcp-integration.md)
+- [Next Chapter: Chapter 7: Logs, Debugging, and Operations](07-logs-debugging-and-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/crush-tutorial/07-logs-debugging-and-operations.md b/tutorials/crush-tutorial/07-logs-debugging-and-operations.md
index a9f094c6..31acbd9f 100644
--- a/tutorials/crush-tutorial/07-logs-debugging-and-operations.md
+++ b/tutorials/crush-tutorial/07-logs-debugging-and-operations.md
@@ -7,6 +7,9 @@ parent: Crush Tutorial
 
 # Chapter 7: Logs, Debugging, and Operations
 
+Welcome to **Chapter 7: Logs, Debugging, and Operations**. In this part of **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers the operator workflows you need when Crush behavior deviates from expectations.
 
 ## Learning Goals
@@ -44,3 +47,598 @@ Crush can auto-update provider metadata from Catwalk. For restricted environment
 You now have practical diagnostics and maintenance workflows for operating Crush reliably.
 
 Next: [Chapter 8: Production Governance and Rollout](08-production-governance-and-rollout.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: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- tutorial slug: **crush-tutorial**
+- chapter focus: **Chapter 7: Logs, Debugging, and Operations**
+- system context: **Crush Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Logs, Debugging, and Operations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Crush Repository](https://github.com/charmbracelet/crush)
+- [Crush README](https://github.com/charmbracelet/crush/blob/main/README.md)
+- [Crush Releases](https://github.com/charmbracelet/crush/releases)
+- [Crush Configuration Schema](https://github.com/charmbracelet/crush/blob/main/schema.json)
+- [Crush Command Loader](https://github.com/charmbracelet/crush/blob/main/internal/commands/commands.go)
+
+### Cross-Tutorial Connection Map
+
+- [Goose Tutorial](../goose-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Kilo Code Tutorial](../kilocode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Logs, Debugging, and Operations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Logs, Debugging, and Operations
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Logs, Debugging, and Operations` as an operating subsystem inside **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Logs, Debugging, and Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Crush Repository](https://github.com/charmbracelet/crush)
+  Why it matters: authoritative reference on `Crush Repository` (github.com).
+- [Crush README](https://github.com/charmbracelet/crush/blob/main/README.md)
+  Why it matters: authoritative reference on `Crush README` (github.com).
+- [Crush Releases](https://github.com/charmbracelet/crush/releases)
+  Why it matters: authoritative reference on `Crush Releases` (github.com).
+- [Crush Configuration Schema](https://github.com/charmbracelet/crush/blob/main/schema.json)
+  Why it matters: authoritative reference on `Crush Configuration Schema` (github.com).
+- [Crush Command Loader](https://github.com/charmbracelet/crush/blob/main/internal/commands/commands.go)
+  Why it matters: authoritative reference on `Crush Command Loader` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Skills, Commands, and Workflow Customization](06-skills-commands-and-workflow-customization.md)
+- [Next Chapter: Chapter 8: Production Governance and Rollout](08-production-governance-and-rollout.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/crush-tutorial/08-production-governance-and-rollout.md b/tutorials/crush-tutorial/08-production-governance-and-rollout.md
index 0ac8d802..7774affc 100644
--- a/tutorials/crush-tutorial/08-production-governance-and-rollout.md
+++ b/tutorials/crush-tutorial/08-production-governance-and-rollout.md
@@ -7,6 +7,9 @@ parent: Crush Tutorial
 
 # Chapter 8: Production Governance and Rollout
 
+Welcome to **Chapter 8: Production Governance and Rollout**. In this part of **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter provides a governance framework for deploying Crush across real engineering teams.
 
 ## Learning Goals
@@ -45,3 +48,585 @@ This chapter provides a governance framework for deploying Crush across real eng
 You now have an end-to-end framework for adopting Crush as a governed coding-agent platform.
 
 Compare terminal-first practices in the [Goose Tutorial](../goose-tutorial/).
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- tutorial slug: **crush-tutorial**
+- chapter focus: **Chapter 8: Production Governance and Rollout**
+- system context: **Crush Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Governance and Rollout`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Crush Repository](https://github.com/charmbracelet/crush)
+- [Crush README](https://github.com/charmbracelet/crush/blob/main/README.md)
+- [Crush Releases](https://github.com/charmbracelet/crush/releases)
+- [Crush Configuration Schema](https://github.com/charmbracelet/crush/blob/main/schema.json)
+- [Crush Command Loader](https://github.com/charmbracelet/crush/blob/main/internal/commands/commands.go)
+
+### Cross-Tutorial Connection Map
+
+- [Goose Tutorial](../goose-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Kilo Code Tutorial](../kilocode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Governance and Rollout`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Governance and Rollout
+
+- tutorial context: **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Governance and Rollout` as an operating subsystem inside **Crush Tutorial: Multi-Model Terminal Coding Agent with Strong Extensibility**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Governance and Rollout` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Crush Repository](https://github.com/charmbracelet/crush)
+  Why it matters: authoritative reference on `Crush Repository` (github.com).
+- [Crush README](https://github.com/charmbracelet/crush/blob/main/README.md)
+  Why it matters: authoritative reference on `Crush README` (github.com).
+- [Crush Releases](https://github.com/charmbracelet/crush/releases)
+  Why it matters: authoritative reference on `Crush Releases` (github.com).
+- [Crush Configuration Schema](https://github.com/charmbracelet/crush/blob/main/schema.json)
+  Why it matters: authoritative reference on `Crush Configuration Schema` (github.com).
+- [Crush Command Loader](https://github.com/charmbracelet/crush/blob/main/internal/commands/commands.go)
+  Why it matters: authoritative reference on `Crush Command Loader` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Logs, Debugging, and Operations](07-logs-debugging-and-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/daytona-tutorial/01-getting-started.md b/tutorials/daytona-tutorial/01-getting-started.md
index 96ab9f0a..68c7281e 100644
--- a/tutorials/daytona-tutorial/01-getting-started.md
+++ b/tutorials/daytona-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Daytona Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter sets up the fastest path from account setup to a running sandbox.
 
 ## Learning Goals
@@ -36,3 +39,606 @@ This chapter sets up the fastest path from account setup to a running sandbox.
 You now have a working Daytona baseline with authenticated access and first code execution.
 
 Next: [Chapter 2: Sandbox Lifecycle, Resources, and Regions](02-sandbox-lifecycle-resources-and-regions.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: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- tutorial slug: **daytona-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Daytona 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
+
+- [Daytona Repository](https://github.com/daytonaio/daytona)
+- [README](https://github.com/daytonaio/daytona/blob/main/README.md)
+- [Getting Started](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/getting-started.mdx)
+- [Sandboxes](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/sandboxes.mdx)
+- [Process and Code Execution](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/process-code-execution.mdx)
+- [Daytona MCP Server](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/mcp.mdx)
+- [CLI MCP README](https://github.com/daytonaio/daytona/blob/main/apps/cli/mcp/README.md)
+- [Environment Configuration](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/configuration.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [HAPI Tutorial](../hapi-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Claude Flow Tutorial](../claude-flow-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: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Daytona Repository](https://github.com/daytonaio/daytona)
+  Why it matters: authoritative reference on `Daytona Repository` (github.com).
+- [README](https://github.com/daytonaio/daytona/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Getting Started](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/getting-started.mdx)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Sandboxes](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/sandboxes.mdx)
+  Why it matters: authoritative reference on `Sandboxes` (github.com).
+- [Process and Code Execution](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/process-code-execution.mdx)
+  Why it matters: authoritative reference on `Process and Code Execution` (github.com).
+- [Daytona MCP Server](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/mcp.mdx)
+  Why it matters: authoritative reference on `Daytona MCP Server` (github.com).
+- [CLI MCP README](https://github.com/daytonaio/daytona/blob/main/apps/cli/mcp/README.md)
+  Why it matters: authoritative reference on `CLI MCP README` (github.com).
+- [Environment Configuration](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/configuration.mdx)
+  Why it matters: authoritative reference on `Environment Configuration` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Sandbox Lifecycle, Resources, and Regions](02-sandbox-lifecycle-resources-and-regions.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/daytona-tutorial/02-sandbox-lifecycle-resources-and-regions.md b/tutorials/daytona-tutorial/02-sandbox-lifecycle-resources-and-regions.md
index 5c71c217..d3ae15e1 100644
--- a/tutorials/daytona-tutorial/02-sandbox-lifecycle-resources-and-regions.md
+++ b/tutorials/daytona-tutorial/02-sandbox-lifecycle-resources-and-regions.md
@@ -7,6 +7,9 @@ parent: Daytona Tutorial
 
 # Chapter 2: Sandbox Lifecycle, Resources, and Regions
 
+Welcome to **Chapter 2: Sandbox Lifecycle, Resources, and Regions**. In this part of **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains how Daytona sandboxes transition state and consume organization quotas.
 
 ## Learning Goals
@@ -32,3 +35,607 @@ Use `running` only for active work, move to `stopped` for idle periods, and arch
 You now understand how to shape sandbox lifecycle and resource policy around real workload behavior.
 
 Next: [Chapter 3: Process and Code Execution Patterns](03-process-and-code-execution-patterns.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- tutorial slug: **daytona-tutorial**
+- chapter focus: **Chapter 2: Sandbox Lifecycle, Resources, and Regions**
+- system context: **Daytona Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Sandbox Lifecycle, Resources, and Regions`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Daytona Repository](https://github.com/daytonaio/daytona)
+- [README](https://github.com/daytonaio/daytona/blob/main/README.md)
+- [Getting Started](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/getting-started.mdx)
+- [Sandboxes](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/sandboxes.mdx)
+- [Process and Code Execution](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/process-code-execution.mdx)
+- [Daytona MCP Server](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/mcp.mdx)
+- [CLI MCP README](https://github.com/daytonaio/daytona/blob/main/apps/cli/mcp/README.md)
+- [Environment Configuration](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/configuration.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [HAPI Tutorial](../hapi-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Claude Flow Tutorial](../claude-flow-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Sandbox Lifecycle, Resources, and Regions`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Sandbox Lifecycle, Resources, and Regions
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Sandbox Lifecycle, Resources, and Regions` as an operating subsystem inside **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Sandbox Lifecycle, Resources, and Regions` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Daytona Repository](https://github.com/daytonaio/daytona)
+  Why it matters: authoritative reference on `Daytona Repository` (github.com).
+- [README](https://github.com/daytonaio/daytona/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Getting Started](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/getting-started.mdx)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Sandboxes](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/sandboxes.mdx)
+  Why it matters: authoritative reference on `Sandboxes` (github.com).
+- [Process and Code Execution](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/process-code-execution.mdx)
+  Why it matters: authoritative reference on `Process and Code Execution` (github.com).
+- [Daytona MCP Server](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/mcp.mdx)
+  Why it matters: authoritative reference on `Daytona MCP Server` (github.com).
+- [CLI MCP README](https://github.com/daytonaio/daytona/blob/main/apps/cli/mcp/README.md)
+  Why it matters: authoritative reference on `CLI MCP README` (github.com).
+- [Environment Configuration](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/configuration.mdx)
+  Why it matters: authoritative reference on `Environment Configuration` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Process and Code Execution Patterns](03-process-and-code-execution-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/daytona-tutorial/03-process-and-code-execution-patterns.md b/tutorials/daytona-tutorial/03-process-and-code-execution-patterns.md
index b00535f6..f26bd569 100644
--- a/tutorials/daytona-tutorial/03-process-and-code-execution-patterns.md
+++ b/tutorials/daytona-tutorial/03-process-and-code-execution-patterns.md
@@ -7,6 +7,9 @@ parent: Daytona Tutorial
 
 # Chapter 3: Process and Code Execution Patterns
 
+Welcome to **Chapter 3: Process and Code Execution Patterns**. In this part of **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers process execution and code-run workflows across SDK surfaces.
 
 ## Learning Goals
@@ -32,3 +35,607 @@ Use stateless execution for isolated snippets and predictable idempotent jobs. U
 You now have an execution model that balances speed, isolation, and observability.
 
 Next: [Chapter 4: File, Git, and Preview Workflows](04-file-git-and-preview-workflows.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- tutorial slug: **daytona-tutorial**
+- chapter focus: **Chapter 3: Process and Code Execution Patterns**
+- system context: **Daytona Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Process and Code Execution Patterns`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Daytona Repository](https://github.com/daytonaio/daytona)
+- [README](https://github.com/daytonaio/daytona/blob/main/README.md)
+- [Getting Started](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/getting-started.mdx)
+- [Sandboxes](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/sandboxes.mdx)
+- [Process and Code Execution](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/process-code-execution.mdx)
+- [Daytona MCP Server](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/mcp.mdx)
+- [CLI MCP README](https://github.com/daytonaio/daytona/blob/main/apps/cli/mcp/README.md)
+- [Environment Configuration](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/configuration.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [HAPI Tutorial](../hapi-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Claude Flow Tutorial](../claude-flow-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Process and Code Execution Patterns`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Process and Code Execution Patterns
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Process and Code Execution Patterns` as an operating subsystem inside **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Process and Code Execution Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Daytona Repository](https://github.com/daytonaio/daytona)
+  Why it matters: authoritative reference on `Daytona Repository` (github.com).
+- [README](https://github.com/daytonaio/daytona/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Getting Started](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/getting-started.mdx)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Sandboxes](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/sandboxes.mdx)
+  Why it matters: authoritative reference on `Sandboxes` (github.com).
+- [Process and Code Execution](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/process-code-execution.mdx)
+  Why it matters: authoritative reference on `Process and Code Execution` (github.com).
+- [Daytona MCP Server](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/mcp.mdx)
+  Why it matters: authoritative reference on `Daytona MCP Server` (github.com).
+- [CLI MCP README](https://github.com/daytonaio/daytona/blob/main/apps/cli/mcp/README.md)
+  Why it matters: authoritative reference on `CLI MCP README` (github.com).
+- [Environment Configuration](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/configuration.mdx)
+  Why it matters: authoritative reference on `Environment Configuration` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Sandbox Lifecycle, Resources, and Regions](02-sandbox-lifecycle-resources-and-regions.md)
+- [Next Chapter: Chapter 4: File, Git, and Preview Workflows](04-file-git-and-preview-workflows.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/daytona-tutorial/04-file-git-and-preview-workflows.md b/tutorials/daytona-tutorial/04-file-git-and-preview-workflows.md
index 4786bd56..e8245002 100644
--- a/tutorials/daytona-tutorial/04-file-git-and-preview-workflows.md
+++ b/tutorials/daytona-tutorial/04-file-git-and-preview-workflows.md
@@ -7,6 +7,9 @@ parent: Daytona Tutorial
 
 # Chapter 4: File, Git, and Preview Workflows
 
+Welcome to **Chapter 4: File, Git, and Preview Workflows**. In this part of **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter maps the day-to-day development workflow inside Daytona sandboxes.
 
 ## Learning Goals
@@ -32,3 +35,607 @@ Treat each sandbox as a disposable but scriptable workspace: hydrate files, clon
 You can now run a full code-to-preview loop inside Daytona with cleaner automation boundaries.
 
 Next: [Chapter 5: MCP Agent Integration and Tooling](05-mcp-agent-integration-and-tooling.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: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- tutorial slug: **daytona-tutorial**
+- chapter focus: **Chapter 4: File, Git, and Preview Workflows**
+- system context: **Daytona Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: File, Git, and Preview Workflows`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Daytona Repository](https://github.com/daytonaio/daytona)
+- [README](https://github.com/daytonaio/daytona/blob/main/README.md)
+- [Getting Started](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/getting-started.mdx)
+- [Sandboxes](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/sandboxes.mdx)
+- [Process and Code Execution](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/process-code-execution.mdx)
+- [Daytona MCP Server](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/mcp.mdx)
+- [CLI MCP README](https://github.com/daytonaio/daytona/blob/main/apps/cli/mcp/README.md)
+- [Environment Configuration](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/configuration.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [HAPI Tutorial](../hapi-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Claude Flow Tutorial](../claude-flow-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: File, Git, and Preview Workflows`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: File, Git, and Preview Workflows
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: File, Git, and Preview Workflows` as an operating subsystem inside **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: File, Git, and Preview Workflows` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Daytona Repository](https://github.com/daytonaio/daytona)
+  Why it matters: authoritative reference on `Daytona Repository` (github.com).
+- [README](https://github.com/daytonaio/daytona/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Getting Started](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/getting-started.mdx)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Sandboxes](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/sandboxes.mdx)
+  Why it matters: authoritative reference on `Sandboxes` (github.com).
+- [Process and Code Execution](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/process-code-execution.mdx)
+  Why it matters: authoritative reference on `Process and Code Execution` (github.com).
+- [Daytona MCP Server](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/mcp.mdx)
+  Why it matters: authoritative reference on `Daytona MCP Server` (github.com).
+- [CLI MCP README](https://github.com/daytonaio/daytona/blob/main/apps/cli/mcp/README.md)
+  Why it matters: authoritative reference on `CLI MCP README` (github.com).
+- [Environment Configuration](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/configuration.mdx)
+  Why it matters: authoritative reference on `Environment Configuration` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Process and Code Execution Patterns](03-process-and-code-execution-patterns.md)
+- [Next Chapter: Chapter 5: MCP Agent Integration and Tooling](05-mcp-agent-integration-and-tooling.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/daytona-tutorial/05-mcp-agent-integration-and-tooling.md b/tutorials/daytona-tutorial/05-mcp-agent-integration-and-tooling.md
index 7e0760c6..5fbc6810 100644
--- a/tutorials/daytona-tutorial/05-mcp-agent-integration-and-tooling.md
+++ b/tutorials/daytona-tutorial/05-mcp-agent-integration-and-tooling.md
@@ -7,6 +7,9 @@ parent: Daytona Tutorial
 
 # Chapter 5: MCP Agent Integration and Tooling
 
+Welcome to **Chapter 5: MCP Agent Integration and Tooling**. In this part of **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on integrating Daytona with coding-agent hosts through MCP.
 
 ## Learning Goals
@@ -31,3 +34,607 @@ Use CLI setup (`daytona mcp init ...`) for standard hosts. For custom hosts, gen
 You can now connect Daytona capabilities directly into MCP-compatible coding-agent environments.
 
 Next: [Chapter 6: Configuration, API, and Deployment Models](06-configuration-api-and-deployment-models.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- tutorial slug: **daytona-tutorial**
+- chapter focus: **Chapter 5: MCP Agent Integration and Tooling**
+- system context: **Daytona Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: MCP Agent Integration and Tooling`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Daytona Repository](https://github.com/daytonaio/daytona)
+- [README](https://github.com/daytonaio/daytona/blob/main/README.md)
+- [Getting Started](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/getting-started.mdx)
+- [Sandboxes](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/sandboxes.mdx)
+- [Process and Code Execution](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/process-code-execution.mdx)
+- [Daytona MCP Server](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/mcp.mdx)
+- [CLI MCP README](https://github.com/daytonaio/daytona/blob/main/apps/cli/mcp/README.md)
+- [Environment Configuration](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/configuration.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [HAPI Tutorial](../hapi-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Claude Flow Tutorial](../claude-flow-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: MCP Agent Integration and Tooling`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: MCP Agent Integration and Tooling
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: MCP Agent Integration and Tooling` as an operating subsystem inside **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: MCP Agent Integration and Tooling` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Daytona Repository](https://github.com/daytonaio/daytona)
+  Why it matters: authoritative reference on `Daytona Repository` (github.com).
+- [README](https://github.com/daytonaio/daytona/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Getting Started](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/getting-started.mdx)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Sandboxes](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/sandboxes.mdx)
+  Why it matters: authoritative reference on `Sandboxes` (github.com).
+- [Process and Code Execution](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/process-code-execution.mdx)
+  Why it matters: authoritative reference on `Process and Code Execution` (github.com).
+- [Daytona MCP Server](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/mcp.mdx)
+  Why it matters: authoritative reference on `Daytona MCP Server` (github.com).
+- [CLI MCP README](https://github.com/daytonaio/daytona/blob/main/apps/cli/mcp/README.md)
+  Why it matters: authoritative reference on `CLI MCP README` (github.com).
+- [Environment Configuration](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/configuration.mdx)
+  Why it matters: authoritative reference on `Environment Configuration` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: File, Git, and Preview Workflows](04-file-git-and-preview-workflows.md)
+- [Next Chapter: Chapter 6: Configuration, API, and Deployment Models](06-configuration-api-and-deployment-models.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/daytona-tutorial/06-configuration-api-and-deployment-models.md b/tutorials/daytona-tutorial/06-configuration-api-and-deployment-models.md
index cb34b4fe..c6d453cd 100644
--- a/tutorials/daytona-tutorial/06-configuration-api-and-deployment-models.md
+++ b/tutorials/daytona-tutorial/06-configuration-api-and-deployment-models.md
@@ -7,6 +7,9 @@ parent: Daytona Tutorial
 
 # Chapter 6: Configuration, API, and Deployment Models
 
+Welcome to **Chapter 6: Configuration, API, and Deployment Models**. In this part of **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains how to standardize Daytona configuration and deployment choices.
 
 ## Learning Goals
@@ -31,3 +34,607 @@ Keep hosted Daytona for production reliability unless you explicitly need OSS se
 You now have a clearer contract for environment setup and deployment mode selection.
 
 Next: [Chapter 7: Limits, Network Controls, and Security](07-limits-network-controls-and-security.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- tutorial slug: **daytona-tutorial**
+- chapter focus: **Chapter 6: Configuration, API, and Deployment Models**
+- system context: **Daytona Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Configuration, API, and Deployment Models`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Daytona Repository](https://github.com/daytonaio/daytona)
+- [README](https://github.com/daytonaio/daytona/blob/main/README.md)
+- [Getting Started](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/getting-started.mdx)
+- [Sandboxes](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/sandboxes.mdx)
+- [Process and Code Execution](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/process-code-execution.mdx)
+- [Daytona MCP Server](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/mcp.mdx)
+- [CLI MCP README](https://github.com/daytonaio/daytona/blob/main/apps/cli/mcp/README.md)
+- [Environment Configuration](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/configuration.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [HAPI Tutorial](../hapi-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Claude Flow Tutorial](../claude-flow-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Configuration, API, and Deployment Models`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Configuration, API, and Deployment Models
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Configuration, API, and Deployment Models` as an operating subsystem inside **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Configuration, API, and Deployment Models` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Daytona Repository](https://github.com/daytonaio/daytona)
+  Why it matters: authoritative reference on `Daytona Repository` (github.com).
+- [README](https://github.com/daytonaio/daytona/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Getting Started](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/getting-started.mdx)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Sandboxes](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/sandboxes.mdx)
+  Why it matters: authoritative reference on `Sandboxes` (github.com).
+- [Process and Code Execution](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/process-code-execution.mdx)
+  Why it matters: authoritative reference on `Process and Code Execution` (github.com).
+- [Daytona MCP Server](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/mcp.mdx)
+  Why it matters: authoritative reference on `Daytona MCP Server` (github.com).
+- [CLI MCP README](https://github.com/daytonaio/daytona/blob/main/apps/cli/mcp/README.md)
+  Why it matters: authoritative reference on `CLI MCP README` (github.com).
+- [Environment Configuration](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/configuration.mdx)
+  Why it matters: authoritative reference on `Environment Configuration` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: MCP Agent Integration and Tooling](05-mcp-agent-integration-and-tooling.md)
+- [Next Chapter: Chapter 7: Limits, Network Controls, and Security](07-limits-network-controls-and-security.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/daytona-tutorial/07-limits-network-controls-and-security.md b/tutorials/daytona-tutorial/07-limits-network-controls-and-security.md
index 62fa600f..c8d62e96 100644
--- a/tutorials/daytona-tutorial/07-limits-network-controls-and-security.md
+++ b/tutorials/daytona-tutorial/07-limits-network-controls-and-security.md
@@ -7,6 +7,9 @@ parent: Daytona Tutorial
 
 # Chapter 7: Limits, Network Controls, and Security
 
+Welcome to **Chapter 7: Limits, Network Controls, and Security**. In this part of **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers resource governance, rate-limit behavior, and network isolation controls.
 
 ## Learning Goals
@@ -31,3 +34,607 @@ Treat quotas and egress policy as first-class architecture constraints. Build re
 You now have a policy framework for scaling usage while constraining abuse and blast radius.
 
 Next: [Chapter 8: Production Operations and Contribution](08-production-operations-and-contribution.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- tutorial slug: **daytona-tutorial**
+- chapter focus: **Chapter 7: Limits, Network Controls, and Security**
+- system context: **Daytona Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Limits, Network Controls, 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
+
+- [Daytona Repository](https://github.com/daytonaio/daytona)
+- [README](https://github.com/daytonaio/daytona/blob/main/README.md)
+- [Getting Started](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/getting-started.mdx)
+- [Sandboxes](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/sandboxes.mdx)
+- [Process and Code Execution](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/process-code-execution.mdx)
+- [Daytona MCP Server](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/mcp.mdx)
+- [CLI MCP README](https://github.com/daytonaio/daytona/blob/main/apps/cli/mcp/README.md)
+- [Environment Configuration](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/configuration.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [HAPI Tutorial](../hapi-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Claude Flow Tutorial](../claude-flow-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Limits, Network Controls, 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: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Limits, Network Controls, and Security
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Limits, Network Controls, and Security` as an operating subsystem inside **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Limits, Network Controls, and Security` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Daytona Repository](https://github.com/daytonaio/daytona)
+  Why it matters: authoritative reference on `Daytona Repository` (github.com).
+- [README](https://github.com/daytonaio/daytona/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Getting Started](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/getting-started.mdx)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Sandboxes](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/sandboxes.mdx)
+  Why it matters: authoritative reference on `Sandboxes` (github.com).
+- [Process and Code Execution](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/process-code-execution.mdx)
+  Why it matters: authoritative reference on `Process and Code Execution` (github.com).
+- [Daytona MCP Server](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/mcp.mdx)
+  Why it matters: authoritative reference on `Daytona MCP Server` (github.com).
+- [CLI MCP README](https://github.com/daytonaio/daytona/blob/main/apps/cli/mcp/README.md)
+  Why it matters: authoritative reference on `CLI MCP README` (github.com).
+- [Environment Configuration](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/configuration.mdx)
+  Why it matters: authoritative reference on `Environment Configuration` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Configuration, API, and Deployment Models](06-configuration-api-and-deployment-models.md)
+- [Next Chapter: Chapter 8: Production Operations and Contribution](08-production-operations-and-contribution.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/daytona-tutorial/08-production-operations-and-contribution.md b/tutorials/daytona-tutorial/08-production-operations-and-contribution.md
index c5ab514b..912b737f 100644
--- a/tutorials/daytona-tutorial/08-production-operations-and-contribution.md
+++ b/tutorials/daytona-tutorial/08-production-operations-and-contribution.md
@@ -7,6 +7,9 @@ parent: Daytona Tutorial
 
 # Chapter 8: Production Operations and Contribution
 
+Welcome to **Chapter 8: Production Operations and Contribution**. In this part of **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter finalizes operational practices for long-lived Daytona adoption.
 
 ## Learning Goals
@@ -33,3 +36,606 @@ This chapter finalizes operational practices for long-lived Daytona adoption.
 ## Summary
 
 You now have an end-to-end blueprint for using Daytona as secure execution infrastructure for agentic coding workflows.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- tutorial slug: **daytona-tutorial**
+- chapter focus: **Chapter 8: Production Operations and Contribution**
+- system context: **Daytona Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Operations and Contribution`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Daytona Repository](https://github.com/daytonaio/daytona)
+- [README](https://github.com/daytonaio/daytona/blob/main/README.md)
+- [Getting Started](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/getting-started.mdx)
+- [Sandboxes](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/sandboxes.mdx)
+- [Process and Code Execution](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/process-code-execution.mdx)
+- [Daytona MCP Server](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/mcp.mdx)
+- [CLI MCP README](https://github.com/daytonaio/daytona/blob/main/apps/cli/mcp/README.md)
+- [Environment Configuration](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/configuration.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [HAPI Tutorial](../hapi-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Claude Flow Tutorial](../claude-flow-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Operations and Contribution`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Production Operations and Contribution
+
+- tutorial context: **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Operations and Contribution` as an operating subsystem inside **Daytona Tutorial: Secure Sandbox Infrastructure for AI-Generated Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Operations and Contribution` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Daytona Repository](https://github.com/daytonaio/daytona)
+  Why it matters: authoritative reference on `Daytona Repository` (github.com).
+- [README](https://github.com/daytonaio/daytona/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Getting Started](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/getting-started.mdx)
+  Why it matters: authoritative reference on `Getting Started` (github.com).
+- [Sandboxes](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/sandboxes.mdx)
+  Why it matters: authoritative reference on `Sandboxes` (github.com).
+- [Process and Code Execution](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/process-code-execution.mdx)
+  Why it matters: authoritative reference on `Process and Code Execution` (github.com).
+- [Daytona MCP Server](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/mcp.mdx)
+  Why it matters: authoritative reference on `Daytona MCP Server` (github.com).
+- [CLI MCP README](https://github.com/daytonaio/daytona/blob/main/apps/cli/mcp/README.md)
+  Why it matters: authoritative reference on `CLI MCP README` (github.com).
+- [Environment Configuration](https://github.com/daytonaio/daytona/blob/main/apps/docs/src/content/docs/en/configuration.mdx)
+  Why it matters: authoritative reference on `Environment Configuration` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Limits, Network Controls, and Security](07-limits-network-controls-and-security.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/deer-flow-tutorial/01-getting-started.md b/tutorials/deer-flow-tutorial/01-getting-started.md
index db9a9b5d..1921e7f3 100644
--- a/tutorials/deer-flow-tutorial/01-getting-started.md
+++ b/tutorials/deer-flow-tutorial/01-getting-started.md
@@ -614,3 +614,58 @@ Ready to create more complex workflows? Let's explore [Chapter 2: Workflow Basic
 5. Explore the API endpoints for automation
 
 *What's the first distributed workflow you want to build?* 🔀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Task`, `deer`, `flow` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Deer Flow` as an operating subsystem inside **Deer Flow Tutorial: Distributed Workflow Orchestration Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `name`, `localhost`, `http` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Deer Flow` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Task`.
+2. **Input normalization**: shape incoming data so `deer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `flow`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Official Documentation](https://github.com/bytedance/deer-flow/tree/main/docs)
+  Why it matters: authoritative reference on `Official Documentation` (github.com).
+- [GitHub Repository](https://github.com/bytedance/deer-flow)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [API Reference](https://github.com/bytedance/deer-flow/blob/main/docs/API.md)
+  Why it matters: authoritative reference on `API Reference` (github.com).
+- [Community & Issues](https://github.com/bytedance/deer-flow/issues)
+  Why it matters: authoritative reference on `Community & Issues` (github.com).
+- [Workflow Examples](https://github.com/bytedance/deer-flow/tree/main/examples)
+  Why it matters: authoritative reference on `Workflow Examples` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Task` and `deer` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Workflow Basics](02-workflow-basics.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/deer-flow-tutorial/02-workflow-basics.md b/tutorials/deer-flow-tutorial/02-workflow-basics.md
index fc8cc0a7..ee2cc4db 100644
--- a/tutorials/deer-flow-tutorial/02-workflow-basics.md
+++ b/tutorials/deer-flow-tutorial/02-workflow-basics.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Workflow Basics
 
+Welcome to **Chapter 2: Workflow Basics**. In this part of **Deer Flow Tutorial: Distributed Workflow Orchestration Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Learn to create and manage basic workflows with Deer Flow's workflow definition system.
 
 ## Overview
@@ -464,3 +467,59 @@ Ready to explore different task types in depth? Let's dive into Chapter 3.
 **Ready for Chapter 3?** [Task Management](03-task-management.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `workflow`, `deerflow`, `python` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Workflow Basics` as an operating subsystem inside **Deer Flow Tutorial: Distributed Workflow Orchestration Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `script`, `config`, `tasks` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Workflow Basics` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `workflow`.
+2. **Input normalization**: shape incoming data so `deerflow` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `python`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Official Documentation](https://github.com/bytedance/deer-flow/tree/main/docs)
+  Why it matters: authoritative reference on `Official Documentation` (github.com).
+- [GitHub Repository](https://github.com/bytedance/deer-flow)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [API Reference](https://github.com/bytedance/deer-flow/blob/main/docs/API.md)
+  Why it matters: authoritative reference on `API Reference` (github.com).
+- [Community & Issues](https://github.com/bytedance/deer-flow/issues)
+  Why it matters: authoritative reference on `Community & Issues` (github.com).
+- [Workflow Examples](https://github.com/bytedance/deer-flow/tree/main/examples)
+  Why it matters: authoritative reference on `Workflow Examples` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `workflow` and `deerflow` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Deer Flow](01-getting-started.md)
+- [Next Chapter: Chapter 3: Task Management](03-task-management.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/deer-flow-tutorial/03-task-management.md b/tutorials/deer-flow-tutorial/03-task-management.md
index ea31eb9e..59417de0 100644
--- a/tutorials/deer-flow-tutorial/03-task-management.md
+++ b/tutorials/deer-flow-tutorial/03-task-management.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Task Management
 
+Welcome to **Chapter 3: Task Management**. In this part of **Deer Flow Tutorial: Distributed Workflow Orchestration Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deep dive into task types, execution modes, and configuration options in Deer Flow.
 
 ## Overview
@@ -498,3 +501,59 @@ Ready to learn about complex task dependencies? Let's explore Chapter 4.
 **Ready for Chapter 4?** [Dependencies](04-dependencies.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `config`, `task`, `deerflow` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Task Management` as an operating subsystem inside **Deer Flow Tutorial: Distributed Workflow Orchestration Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `self`, `context`, `python` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Task Management` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `config`.
+2. **Input normalization**: shape incoming data so `task` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `deerflow`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Official Documentation](https://github.com/bytedance/deer-flow/tree/main/docs)
+  Why it matters: authoritative reference on `Official Documentation` (github.com).
+- [GitHub Repository](https://github.com/bytedance/deer-flow)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [API Reference](https://github.com/bytedance/deer-flow/blob/main/docs/API.md)
+  Why it matters: authoritative reference on `API Reference` (github.com).
+- [Community & Issues](https://github.com/bytedance/deer-flow/issues)
+  Why it matters: authoritative reference on `Community & Issues` (github.com).
+- [Workflow Examples](https://github.com/bytedance/deer-flow/tree/main/examples)
+  Why it matters: authoritative reference on `Workflow Examples` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `config` and `task` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Workflow Basics](02-workflow-basics.md)
+- [Next Chapter: Chapter 4: Dependencies](04-dependencies.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/deer-flow-tutorial/04-dependencies.md b/tutorials/deer-flow-tutorial/04-dependencies.md
index 6feb27e9..f661389e 100644
--- a/tutorials/deer-flow-tutorial/04-dependencies.md
+++ b/tutorials/deer-flow-tutorial/04-dependencies.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Dependencies
 
+Welcome to **Chapter 4: Dependencies**. In this part of **Deer Flow Tutorial: Distributed Workflow Orchestration Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master complex dependency relationships, conditional execution, and data flow between tasks.
 
 ## Overview
@@ -448,3 +451,59 @@ Ready to learn about error handling and fault tolerance? Let's explore Chapter 5
 **Ready for Chapter 5?** [Error Handling](05-error-handling.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `depends_on`, `config`, `workflow` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Dependencies` as an operating subsystem inside **Deer Flow Tutorial: Distributed Workflow Orchestration Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `tasks`, `python`, `script` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Dependencies` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `depends_on`.
+2. **Input normalization**: shape incoming data so `config` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `workflow`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Official Documentation](https://github.com/bytedance/deer-flow/tree/main/docs)
+  Why it matters: authoritative reference on `Official Documentation` (github.com).
+- [GitHub Repository](https://github.com/bytedance/deer-flow)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [API Reference](https://github.com/bytedance/deer-flow/blob/main/docs/API.md)
+  Why it matters: authoritative reference on `API Reference` (github.com).
+- [Community & Issues](https://github.com/bytedance/deer-flow/issues)
+  Why it matters: authoritative reference on `Community & Issues` (github.com).
+- [Workflow Examples](https://github.com/bytedance/deer-flow/tree/main/examples)
+  Why it matters: authoritative reference on `Workflow Examples` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `depends_on` and `config` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Task Management](03-task-management.md)
+- [Next Chapter: Chapter 5: Error Handling](05-error-handling.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/deer-flow-tutorial/05-error-handling.md b/tutorials/deer-flow-tutorial/05-error-handling.md
index 5dfc2420..75a0900e 100644
--- a/tutorials/deer-flow-tutorial/05-error-handling.md
+++ b/tutorials/deer-flow-tutorial/05-error-handling.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Error Handling
 
+Welcome to **Chapter 5: Error Handling**. In this part of **Deer Flow Tutorial: Distributed Workflow Orchestration Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Implement fault-tolerant workflows with retries, fallbacks, and recovery mechanisms.
 
 ## Overview
@@ -500,3 +503,59 @@ Ready to scale your workflows? Let's explore distributed execution in Chapter 6.
 **Ready for Chapter 6?** [Scaling](06-scaling.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `workflow`, `task`, `context` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Error Handling` as an operating subsystem inside **Deer Flow Tutorial: Distributed Workflow Orchestration Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `config`, `Workflow`, `name` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Error Handling` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `workflow`.
+2. **Input normalization**: shape incoming data so `task` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `context`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Official Documentation](https://github.com/bytedance/deer-flow/tree/main/docs)
+  Why it matters: authoritative reference on `Official Documentation` (github.com).
+- [GitHub Repository](https://github.com/bytedance/deer-flow)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [API Reference](https://github.com/bytedance/deer-flow/blob/main/docs/API.md)
+  Why it matters: authoritative reference on `API Reference` (github.com).
+- [Community & Issues](https://github.com/bytedance/deer-flow/issues)
+  Why it matters: authoritative reference on `Community & Issues` (github.com).
+- [Workflow Examples](https://github.com/bytedance/deer-flow/tree/main/examples)
+  Why it matters: authoritative reference on `Workflow Examples` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `workflow` and `task` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Dependencies](04-dependencies.md)
+- [Next Chapter: Chapter 6: Scaling](06-scaling.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/deer-flow-tutorial/06-scaling.md b/tutorials/deer-flow-tutorial/06-scaling.md
index e76918ac..b78da6c9 100644
--- a/tutorials/deer-flow-tutorial/06-scaling.md
+++ b/tutorials/deer-flow-tutorial/06-scaling.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Scaling
 
+Welcome to **Chapter 6: Scaling**. In this part of **Deer Flow Tutorial: Distributed Workflow Orchestration Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Scale Deer Flow across distributed systems with horizontal scaling, load balancing, and resource management.
 
 ## Overview
@@ -510,3 +513,59 @@ Ready to monitor and observe your workflows? Let's explore Chapter 7.
 **Ready for Chapter 7?** [Monitoring](07-monitoring.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `deerflow`, `name`, `worker` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Scaling` as an operating subsystem inside **Deer Flow Tutorial: Distributed Workflow Orchestration Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `scheduler`, `yaml`, `memory` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Scaling` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `deerflow`.
+2. **Input normalization**: shape incoming data so `name` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `worker`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Official Documentation](https://github.com/bytedance/deer-flow/tree/main/docs)
+  Why it matters: authoritative reference on `Official Documentation` (github.com).
+- [GitHub Repository](https://github.com/bytedance/deer-flow)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [API Reference](https://github.com/bytedance/deer-flow/blob/main/docs/API.md)
+  Why it matters: authoritative reference on `API Reference` (github.com).
+- [Community & Issues](https://github.com/bytedance/deer-flow/issues)
+  Why it matters: authoritative reference on `Community & Issues` (github.com).
+- [Workflow Examples](https://github.com/bytedance/deer-flow/tree/main/examples)
+  Why it matters: authoritative reference on `Workflow Examples` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `deerflow` and `name` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Error Handling](05-error-handling.md)
+- [Next Chapter: Chapter 7: Monitoring](07-monitoring.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/deer-flow-tutorial/07-monitoring.md b/tutorials/deer-flow-tutorial/07-monitoring.md
index 5fa5d78a..10c318bb 100644
--- a/tutorials/deer-flow-tutorial/07-monitoring.md
+++ b/tutorials/deer-flow-tutorial/07-monitoring.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Monitoring
 
+Welcome to **Chapter 7: Monitoring**. In this part of **Deer Flow Tutorial: Distributed Workflow Orchestration Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Implement comprehensive monitoring and observability for Deer Flow workflows.
 
 ## Overview
@@ -529,3 +532,59 @@ Ready to explore advanced orchestration patterns? Let's dive into Chapter 8.
 **Ready for Chapter 8?** [Advanced Patterns](08-advanced-patterns.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `context`, `workflow`, `deerflow` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Monitoring` as an operating subsystem inside **Deer Flow Tutorial: Distributed Workflow Orchestration Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `task`, `status`, `rate` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Monitoring` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `context`.
+2. **Input normalization**: shape incoming data so `workflow` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `deerflow`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Official Documentation](https://github.com/bytedance/deer-flow/tree/main/docs)
+  Why it matters: authoritative reference on `Official Documentation` (github.com).
+- [GitHub Repository](https://github.com/bytedance/deer-flow)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [API Reference](https://github.com/bytedance/deer-flow/blob/main/docs/API.md)
+  Why it matters: authoritative reference on `API Reference` (github.com).
+- [Community & Issues](https://github.com/bytedance/deer-flow/issues)
+  Why it matters: authoritative reference on `Community & Issues` (github.com).
+- [Workflow Examples](https://github.com/bytedance/deer-flow/tree/main/examples)
+  Why it matters: authoritative reference on `Workflow Examples` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `context` and `workflow` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Scaling](06-scaling.md)
+- [Next Chapter: Chapter 8: Advanced Patterns](08-advanced-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/deer-flow-tutorial/08-advanced-patterns.md b/tutorials/deer-flow-tutorial/08-advanced-patterns.md
index 1045ef02..c3c78329 100644
--- a/tutorials/deer-flow-tutorial/08-advanced-patterns.md
+++ b/tutorials/deer-flow-tutorial/08-advanced-patterns.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Advanced Patterns
 
+Welcome to **Chapter 8: Advanced Patterns**. In this part of **Deer Flow Tutorial: Distributed Workflow Orchestration Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master sophisticated orchestration patterns for complex workflow scenarios.
 
 ## Overview
@@ -482,3 +485,58 @@ Congratulations! You've completed the Deer Flow tutorial. You now have the knowl
 ---
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `context`, `workflow`, `Workflow` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Advanced Patterns` as an operating subsystem inside **Deer Flow Tutorial: Distributed Workflow Orchestration Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `name`, `task`, `order` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Advanced Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `context`.
+2. **Input normalization**: shape incoming data so `workflow` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Workflow`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Official Documentation](https://github.com/bytedance/deer-flow/tree/main/docs)
+  Why it matters: authoritative reference on `Official Documentation` (github.com).
+- [GitHub Repository](https://github.com/bytedance/deer-flow)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [API Reference](https://github.com/bytedance/deer-flow/blob/main/docs/API.md)
+  Why it matters: authoritative reference on `API Reference` (github.com).
+- [Community & Issues](https://github.com/bytedance/deer-flow/issues)
+  Why it matters: authoritative reference on `Community & Issues` (github.com).
+- [Workflow Examples](https://github.com/bytedance/deer-flow/tree/main/examples)
+  Why it matters: authoritative reference on `Workflow Examples` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `context` and `workflow` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Monitoring](07-monitoring.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dify-platform-deep-dive/01-system-overview.md b/tutorials/dify-platform-deep-dive/01-system-overview.md
index 64f95a6f..e4c01f6b 100644
--- a/tutorials/dify-platform-deep-dive/01-system-overview.md
+++ b/tutorials/dify-platform-deep-dive/01-system-overview.md
@@ -8,6 +8,9 @@ parent: "Dify Platform Deep Dive"
 
 # Chapter 1: Dify System Overview
 
+Welcome to **Chapter 1: Dify System Overview**. In this part of **Dify Platform: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Understanding Dify's place in the modern LLM application ecosystem
 
 ## 🎯 Learning Objectives
@@ -249,4 +252,49 @@ This chapter provided the foundation for understanding Dify's role and capabilit
 
 ---
 
-**Ready to dive deeper?** Continue to [Chapter 2: Core Architecture](02-core-architecture.md) to understand how Dify's components work together.
\ No newline at end of file
+**Ready to dive deeper?** Continue to [Chapter 2: Core Architecture](02-core-architecture.md) to understand how Dify's components work together.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `dify`, `subgraph`, `Layer` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Dify System Overview` as an operating subsystem inside **Dify Platform: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Dify`, `https`, `install` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Dify System Overview` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `dify`.
+2. **Input normalization**: shape incoming data so `subgraph` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Layer`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Dify](https://github.com/langgenius/dify)
+  Why it matters: authoritative reference on `Dify` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `dify` and `subgraph` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Core Architecture](02-core-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dify-platform-deep-dive/02-core-architecture.md b/tutorials/dify-platform-deep-dive/02-core-architecture.md
index 007a8c1e..54040d52 100644
--- a/tutorials/dify-platform-deep-dive/02-core-architecture.md
+++ b/tutorials/dify-platform-deep-dive/02-core-architecture.md
@@ -8,6 +8,9 @@ parent: "Dify Platform Deep Dive"
 
 # Chapter 2: Core Architecture
 
+Welcome to **Chapter 2: Core Architecture**. In this part of **Dify Platform: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Understanding how Dify's components work together to power LLM applications
 
 ## 🎯 Learning Objectives
@@ -425,4 +428,50 @@ Understanding Dify's architecture prepares you for diving into the **Workflow En
 
 ---
 
-**Ready to build workflows?** Continue to [Chapter 3: Workflow Engine](03-workflow-engine.md)
\ No newline at end of file
+**Ready to build workflows?** Continue to [Chapter 3: Workflow Engine](03-workflow-engine.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `workflow`, `Workflow` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Core Architecture` as an operating subsystem inside **Dify Platform: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Node`, `node`, `workflow_id` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Core Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `workflow` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Workflow`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Dify](https://github.com/langgenius/dify)
+  Why it matters: authoritative reference on `Dify` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `workflow` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Dify System Overview](01-system-overview.md)
+- [Next Chapter: Chapter 3: Workflow Engine](03-workflow-engine.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dify-platform-deep-dive/03-workflow-engine.md b/tutorials/dify-platform-deep-dive/03-workflow-engine.md
index 856a80a9..d22332ff 100644
--- a/tutorials/dify-platform-deep-dive/03-workflow-engine.md
+++ b/tutorials/dify-platform-deep-dive/03-workflow-engine.md
@@ -8,6 +8,9 @@ parent: "Dify Platform Deep Dive"
 
 # Chapter 3: Workflow Engine
 
+Welcome to **Chapter 3: Workflow Engine**. In this part of **Dify Platform: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Building complex LLM workflows with Dify's visual node system
 
 ## 🎯 Learning Objectives
@@ -556,4 +559,50 @@ With a solid understanding of the workflow engine, we're ready to explore **RAG
 
 ---
 
-**Ready to add intelligence?** Continue to [Chapter 4: RAG Implementation](04-rag-implementation.md)
\ No newline at end of file
+**Ready to add intelligence?** Continue to [Chapter 4: RAG Implementation](04-rag-implementation.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `nodes`, `config` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Workflow Engine` as an operating subsystem inside **Dify Platform: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `workflow_id`, `Node`, `topic` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Workflow Engine` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `nodes` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `config`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Dify](https://github.com/langgenius/dify)
+  Why it matters: authoritative reference on `Dify` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `nodes` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Core Architecture](02-core-architecture.md)
+- [Next Chapter: Chapter 4: RAG Implementation](04-rag-implementation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dify-platform-deep-dive/04-rag-implementation.md b/tutorials/dify-platform-deep-dive/04-rag-implementation.md
index c18ce143..5db66ead 100644
--- a/tutorials/dify-platform-deep-dive/04-rag-implementation.md
+++ b/tutorials/dify-platform-deep-dive/04-rag-implementation.md
@@ -8,6 +8,9 @@ parent: "Dify Platform Deep Dive"
 
 # Chapter 4: RAG Implementation
 
+Welcome to **Chapter 4: RAG Implementation**. In this part of **Dify Platform: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Building retrieval-augmented generation systems with Dify's document processing and vector search capabilities
 
 ## 🎯 Learning Objectives
@@ -645,4 +648,50 @@ With RAG fundamentals mastered, we're ready to explore **Agent Framework** in th
 
 ---
 
-**Ready for autonomous agents?** Continue to [Chapter 5: Agent Framework](05-agent-framework.md)
\ No newline at end of file
+**Ready for autonomous agents?** Continue to [Chapter 5: Agent Framework](05-agent-framework.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `query`, `context` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: RAG Implementation` as an operating subsystem inside **Dify Platform: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `documents`, `relevant`, `retrieved` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: RAG Implementation` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `query` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `context`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Dify](https://github.com/langgenius/dify)
+  Why it matters: authoritative reference on `Dify` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `query` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Workflow Engine](03-workflow-engine.md)
+- [Next Chapter: Chapter 5: Agent Framework](05-agent-framework.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dify-platform-deep-dive/05-agent-framework.md b/tutorials/dify-platform-deep-dive/05-agent-framework.md
index e638375b..b53885ed 100644
--- a/tutorials/dify-platform-deep-dive/05-agent-framework.md
+++ b/tutorials/dify-platform-deep-dive/05-agent-framework.md
@@ -8,6 +8,9 @@ parent: "Dify Platform Deep Dive"
 
 # Chapter 5: Agent Framework
 
+Welcome to **Chapter 5: Agent Framework**. In this part of **Dify Platform: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Building autonomous AI agents with tool integration and reasoning capabilities in Dify
 
 ## 🎯 Learning Objectives
@@ -613,4 +616,50 @@ With agent fundamentals established, we're ready to explore **Custom Nodes** in
 
 ---
 
-**Ready to extend Dify?** Continue to [Chapter 6: Custom Nodes](06-custom-nodes.md)
\ No newline at end of file
+**Ready to extend Dify?** Continue to [Chapter 6: Custom Nodes](06-custom-nodes.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `action`, `result` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Agent Framework` as an operating subsystem inside **Dify Platform: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `task`, `parameters`, `tool_name` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Agent Framework` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `action` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `result`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Dify](https://github.com/langgenius/dify)
+  Why it matters: authoritative reference on `Dify` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `action` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: RAG Implementation](04-rag-implementation.md)
+- [Next Chapter: Chapter 6: Custom Nodes](06-custom-nodes.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dify-platform-deep-dive/06-custom-nodes.md b/tutorials/dify-platform-deep-dive/06-custom-nodes.md
index fbf3da9c..24143359 100644
--- a/tutorials/dify-platform-deep-dive/06-custom-nodes.md
+++ b/tutorials/dify-platform-deep-dive/06-custom-nodes.md
@@ -8,6 +8,9 @@ parent: "Dify Platform Deep Dive"
 
 # Chapter 6: Custom Nodes
 
+Welcome to **Chapter 6: Custom Nodes**. In this part of **Dify Platform: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Extending Dify's workflow capabilities with custom node development
 
 ## 🎯 Learning Objectives
@@ -790,4 +793,50 @@ With custom node development complete, we're ready to explore **Production Deplo
 
 ---
 
-**Ready for production?** Continue to [Chapter 7: Production Deployment](07-production-deployment.md)
\ No newline at end of file
+**Ready for production?** Continue to [Chapter 7: Production Deployment](07-production-deployment.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `item`, `config` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Custom Nodes` as an operating subsystem inside **Dify Platform: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `node`, `inputs`, `input_data` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Custom Nodes` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `item` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `config`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Dify](https://github.com/langgenius/dify)
+  Why it matters: authoritative reference on `Dify` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `item` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Agent Framework](05-agent-framework.md)
+- [Next Chapter: Chapter 7: Production Deployment](07-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dify-platform-deep-dive/07-production-deployment.md b/tutorials/dify-platform-deep-dive/07-production-deployment.md
index b9d45bbb..a95e93bc 100644
--- a/tutorials/dify-platform-deep-dive/07-production-deployment.md
+++ b/tutorials/dify-platform-deep-dive/07-production-deployment.md
@@ -8,6 +8,9 @@ parent: "Dify Platform Deep Dive"
 
 # Chapter 7: Production Deployment
 
+Welcome to **Chapter 7: Production Deployment**. In this part of **Dify Platform: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploying, scaling, and monitoring Dify applications in production environments
 
 ## 🎯 Learning Objectives
@@ -1095,4 +1098,50 @@ Congratulations! You've completed the comprehensive **Dify Platform Deep Dive**
 
 ---
 
-*Part of the [Awesome Code Docs](../../README.md) collection - transforming complex systems into accessible learning experiences*
\ No newline at end of file
+*Part of the [Awesome Code Docs](../../README.md) collection - transforming complex systems into accessible learning experiences*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `dify`, `name` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Production Deployment` as an operating subsystem inside **Dify Platform: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `status`, `environ`, `results` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `dify` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `name`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Dify](https://github.com/langgenius/dify)
+  Why it matters: authoritative reference on `Dify` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `dify` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Custom Nodes](06-custom-nodes.md)
+- [Next Chapter: Chapter 8: Operations Playbook](08-operations-playbook.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dify-platform-deep-dive/08-operations-playbook.md b/tutorials/dify-platform-deep-dive/08-operations-playbook.md
index b4096f13..8414171e 100644
--- a/tutorials/dify-platform-deep-dive/08-operations-playbook.md
+++ b/tutorials/dify-platform-deep-dive/08-operations-playbook.md
@@ -8,6 +8,9 @@ parent: "Dify Platform Deep Dive"
 
 # Chapter 8: Operations Playbook
 
+Welcome to **Chapter 8: Operations Playbook**. In this part of **Dify Platform: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter consolidates practical operations patterns for running Dify at scale.
 
 ## Runbook Essentials
@@ -36,3 +39,48 @@ You now have full Dify tutorial coverage from architecture through production op
 Related:
 - [Dify Index](index.md)
 - [Setup Guide](docs/setup.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Operations Playbook` as an operating subsystem inside **Dify Platform: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Operations Playbook` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Dify](https://github.com/langgenius/dify)
+  Why it matters: authoritative reference on `Dify` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Operations` and `Playbook` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Production Deployment](07-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dspy-tutorial/01-getting-started.md b/tutorials/dspy-tutorial/01-getting-started.md
index f0a520e5..0c987e42 100644
--- a/tutorials/dspy-tutorial/01-getting-started.md
+++ b/tutorials/dspy-tutorial/01-getting-started.md
@@ -8,6 +8,9 @@ parent: DSPy Tutorial
 
 # Chapter 1: Getting Started with DSPy
 
+Welcome to **Chapter 1: Getting Started with DSPy**. In this part of **DSPy Tutorial: Programming Language Models**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Install DSPy, understand core concepts, and build your first program that automatically optimizes itself.
 
 ## Overview
@@ -430,4 +433,51 @@ Next, we'll dive deep into **signatures** - the foundation of DSPy programming.
 
 **Ready for the next chapter?** [Chapter 2: Signatures](02-signatures.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `dspy`, `question`, `answer` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with DSPy` as an operating subsystem inside **DSPy Tutorial: Programming Language Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `program`, `What`, `model` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with DSPy` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `dspy`.
+2. **Input normalization**: shape incoming data so `question` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `answer`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/stanfordnlp/dspy)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `dspy` and `question` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Signatures - Defining LM Input/Output Behavior](02-signatures.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dspy-tutorial/02-signatures.md b/tutorials/dspy-tutorial/02-signatures.md
index c1401666..3b6d228d 100644
--- a/tutorials/dspy-tutorial/02-signatures.md
+++ b/tutorials/dspy-tutorial/02-signatures.md
@@ -8,6 +8,9 @@ parent: DSPy Tutorial
 
 # Chapter 2: Signatures - Defining LM Input/Output Behavior
 
+Welcome to **Chapter 2: Signatures - Defining LM Input/Output Behavior**. In this part of **DSPy Tutorial: Programming Language Models**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master the art of defining signatures, the fundamental abstraction that specifies what goes into and comes out of your language model calls.
 
 ## Overview
@@ -528,4 +531,52 @@ Next, we'll explore **modules** - the reusable components that implement signatu
 
 **Ready for the next chapter?** [Chapter 3: Modules](03-modules.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `dspy`, `desc`, `OutputField` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Signatures - Defining LM Input/Output Behavior` as an operating subsystem inside **DSPy Tutorial: Programming Language Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `InputField`, `answer`, `Signature` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Signatures - Defining LM Input/Output Behavior` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `dspy`.
+2. **Input normalization**: shape incoming data so `desc` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `OutputField`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/stanfordnlp/dspy)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `dspy` and `desc` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with DSPy](01-getting-started.md)
+- [Next Chapter: Chapter 3: Modules - Reusable DSPy Components](03-modules.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dspy-tutorial/03-modules.md b/tutorials/dspy-tutorial/03-modules.md
index c773eaac..00e034d4 100644
--- a/tutorials/dspy-tutorial/03-modules.md
+++ b/tutorials/dspy-tutorial/03-modules.md
@@ -8,6 +8,9 @@ parent: DSPy Tutorial
 
 # Chapter 3: Modules - Reusable DSPy Components
 
+Welcome to **Chapter 3: Modules - Reusable DSPy Components**. In this part of **DSPy Tutorial: Programming Language Models**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Discover DSPy's built-in modules and learn to create custom modules for complex workflows.
 
 ## Overview
@@ -736,4 +739,52 @@ Next, we'll explore **Retrieval-Augmented Generation (RAG)** - combining retriev
 
 **Ready for the next chapter?** [Chapter 4: Retrieval-Augmented Generation](04-rag.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `dspy`, `self`, `question` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Modules - Reusable DSPy Components` as an operating subsystem inside **DSPy Tutorial: Programming Language Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `result`, `answer`, `desc` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Modules - Reusable DSPy Components` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `dspy`.
+2. **Input normalization**: shape incoming data so `self` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `question`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/stanfordnlp/dspy)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `dspy` and `self` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Signatures - Defining LM Input/Output Behavior](02-signatures.md)
+- [Next Chapter: Chapter 4: Retrieval-Augmented Generation (RAG) with DSPy](04-rag.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dspy-tutorial/04-rag.md b/tutorials/dspy-tutorial/04-rag.md
index d7128342..c83c30f5 100644
--- a/tutorials/dspy-tutorial/04-rag.md
+++ b/tutorials/dspy-tutorial/04-rag.md
@@ -8,6 +8,9 @@ parent: DSPy Tutorial
 
 # Chapter 4: Retrieval-Augmented Generation (RAG) with DSPy
 
+Welcome to **Chapter 4: Retrieval-Augmented Generation (RAG) with DSPy**. In this part of **DSPy Tutorial: Programming Language Models**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Build powerful RAG systems that combine retrieval and generation for accurate, knowledgeable AI responses.
 
 ## Overview
@@ -677,4 +680,52 @@ Next, we'll explore **optimization** - how DSPy automatically improves your prog
 
 **Ready for the next chapter?** [Chapter 5: Optimization](05-optimization.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `dspy`, `self`, `question` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Retrieval-Augmented Generation (RAG) with DSPy` as an operating subsystem inside **DSPy Tutorial: Programming Language Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `answer`, `passages`, `context` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Retrieval-Augmented Generation (RAG) with DSPy` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `dspy`.
+2. **Input normalization**: shape incoming data so `self` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `question`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/stanfordnlp/dspy)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `dspy` and `self` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Modules - Reusable DSPy Components](03-modules.md)
+- [Next Chapter: Chapter 5: Automatic Optimization - DSPy's Superpower](05-optimization.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dspy-tutorial/05-optimization.md b/tutorials/dspy-tutorial/05-optimization.md
index 65b1377d..64c704f1 100644
--- a/tutorials/dspy-tutorial/05-optimization.md
+++ b/tutorials/dspy-tutorial/05-optimization.md
@@ -8,6 +8,9 @@ parent: DSPy Tutorial
 
 # Chapter 5: Automatic Optimization - DSPy's Superpower
 
+Welcome to **Chapter 5: Automatic Optimization - DSPy's Superpower**. In this part of **DSPy Tutorial: Programming Language Models**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Discover how DSPy automatically optimizes your programs through systematic prompt engineering and model selection.
 
 ## Overview
@@ -650,4 +653,52 @@ Next, we'll explore **advanced patterns** - multi-hop reasoning, tool integratio
 
 **Ready for the next chapter?** [Chapter 6: Advanced Patterns](06-advanced-patterns.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `trainset`, `self`, `dspy` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Automatic Optimization - DSPy's Superpower` as an operating subsystem inside **DSPy Tutorial: Programming Language Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `print`, `program`, `score` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Automatic Optimization - DSPy's Superpower` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `trainset`.
+2. **Input normalization**: shape incoming data so `self` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `dspy`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/stanfordnlp/dspy)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `trainset` and `self` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Retrieval-Augmented Generation (RAG) with DSPy](04-rag.md)
+- [Next Chapter: Chapter 6: Advanced Patterns - Multi-Hop Reasoning and Tool Integration](06-advanced-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dspy-tutorial/06-advanced-patterns.md b/tutorials/dspy-tutorial/06-advanced-patterns.md
index edcd08f2..b6b2e363 100644
--- a/tutorials/dspy-tutorial/06-advanced-patterns.md
+++ b/tutorials/dspy-tutorial/06-advanced-patterns.md
@@ -8,6 +8,9 @@ parent: DSPy Tutorial
 
 # Chapter 6: Advanced Patterns - Multi-Hop Reasoning and Tool Integration
 
+Welcome to **Chapter 6: Advanced Patterns - Multi-Hop Reasoning and Tool Integration**. In this part of **DSPy Tutorial: Programming Language Models**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master sophisticated DSPy patterns including multi-hop reasoning, tool integration, and complex agent workflows.
 
 ## Overview
@@ -791,4 +794,52 @@ Next, we'll explore **evaluation and metrics** - systematic evaluation and custo
 
 **Ready for the next chapter?** [Chapter 7: Evaluation & Metrics](07-evaluation.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `dspy`, `self`, `InputField` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Advanced Patterns - Multi-Hop Reasoning and Tool Integration` as an operating subsystem inside **DSPy Tutorial: Programming Language Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `problem`, `task`, `OutputField` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Advanced Patterns - Multi-Hop Reasoning and Tool Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `dspy`.
+2. **Input normalization**: shape incoming data so `self` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `InputField`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/stanfordnlp/dspy)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `dspy` and `self` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Automatic Optimization - DSPy's Superpower](05-optimization.md)
+- [Next Chapter: Chapter 7: Evaluation & Metrics - Systematic Assessment of DSPy Programs](07-evaluation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dspy-tutorial/07-evaluation.md b/tutorials/dspy-tutorial/07-evaluation.md
index ebbbe0f8..cac875ac 100644
--- a/tutorials/dspy-tutorial/07-evaluation.md
+++ b/tutorials/dspy-tutorial/07-evaluation.md
@@ -8,6 +8,9 @@ parent: DSPy Tutorial
 
 # Chapter 7: Evaluation & Metrics - Systematic Assessment of DSPy Programs
 
+Welcome to **Chapter 7: Evaluation & Metrics - Systematic Assessment of DSPy Programs**. In this part of **DSPy Tutorial: Programming Language Models**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Learn to evaluate DSPy programs comprehensively using multiple metrics, statistical analysis, and systematic validation approaches.
 
 ## Overview
@@ -718,4 +721,52 @@ Next, we'll explore **production deployment** - scaling DSPy systems for real-wo
 
 **Ready for the next chapter?** [Chapter 8: Production Deployment](08-production.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `print`, `dspy`, `answer` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Evaluation & Metrics - Systematic Assessment of DSPy Programs` as an operating subsystem inside **DSPy Tutorial: Programming Language Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `program`, `testset`, `example` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Evaluation & Metrics - Systematic Assessment of DSPy Programs` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `print`.
+2. **Input normalization**: shape incoming data so `dspy` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `answer`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/stanfordnlp/dspy)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `print` and `dspy` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Advanced Patterns - Multi-Hop Reasoning and Tool Integration](06-advanced-patterns.md)
+- [Next Chapter: Chapter 8: Production Deployment - Scaling DSPy Systems](08-production.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dspy-tutorial/08-production.md b/tutorials/dspy-tutorial/08-production.md
index 80d36ee2..84c96e75 100644
--- a/tutorials/dspy-tutorial/08-production.md
+++ b/tutorials/dspy-tutorial/08-production.md
@@ -8,6 +8,9 @@ parent: DSPy Tutorial
 
 # Chapter 8: Production Deployment - Scaling DSPy Systems
 
+Welcome to **Chapter 8: Production Deployment - Scaling DSPy Systems**. In this part of **DSPy Tutorial: Programming Language Models**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploy optimized DSPy programs to production with proper scaling, monitoring, and operational best practices.
 
 ## Overview
@@ -878,4 +881,51 @@ Congratulations! You now have the complete knowledge to build, optimize, and dep
 
 ---
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `result`, `dspy` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment - Scaling DSPy Systems` as an operating subsystem inside **DSPy Tutorial: Programming Language Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `model`, `kwargs`, `cache` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment - Scaling DSPy Systems` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `result` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `dspy`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/stanfordnlp/dspy)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `result` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Evaluation & Metrics - Systematic Assessment of DSPy Programs](07-evaluation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dyad-tutorial/01-getting-started.md b/tutorials/dyad-tutorial/01-getting-started.md
index a38e8142..de308f93 100644
--- a/tutorials/dyad-tutorial/01-getting-started.md
+++ b/tutorials/dyad-tutorial/01-getting-started.md
@@ -139,3 +139,499 @@ Congratulations on creating your first AI-generated app! In the next chapter, we
 ---
 
 *Generated by [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)*
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Dyad Tutorial: Local-First AI App Building**
+- tutorial slug: **dyad-tutorial**
+- chapter focus: **Chapter 1: Getting Started with Dyad**
+- system context: **Dyad Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started with Dyad`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Dyad README](https://github.com/dyad-sh/dyad/blob/main/README.md)
+- [Dyad Releases](https://github.com/dyad-sh/dyad/releases)
+- [Dyad Repository](https://github.com/dyad-sh/dyad)
+
+### Cross-Tutorial Connection Map
+
+- [bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [Vercel AI SDK Tutorial](../vercel-ai-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started with Dyad`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with Dyad
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `dyad`, `Create`, `tasks` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Dyad` as an operating subsystem inside **Dyad Tutorial: Local-First AI App Building**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Clone`, `Dyad`, `repository` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Dyad` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `dyad`.
+2. **Input normalization**: shape incoming data so `Create` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `tasks`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Dyad README](https://github.com/dyad-sh/dyad/blob/main/README.md)
+  Why it matters: authoritative reference on `Dyad README` (github.com).
+- [Dyad Releases](https://github.com/dyad-sh/dyad/releases)
+  Why it matters: authoritative reference on `Dyad Releases` (github.com).
+- [Dyad Repository](https://github.com/dyad-sh/dyad)
+  Why it matters: authoritative reference on `Dyad Repository` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `dyad` and `Create` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Natural Language App Building](02-natural-language-building.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dyad-tutorial/02-natural-language-building.md b/tutorials/dyad-tutorial/02-natural-language-building.md
index 9b2ddaa3..c5b9f574 100644
--- a/tutorials/dyad-tutorial/02-natural-language-building.md
+++ b/tutorials/dyad-tutorial/02-natural-language-building.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Natural Language App Building
 
+Welcome to **Chapter 2: Natural Language App Building**. In this part of **Dyad Tutorial: Local-First AI App Building**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Welcome back! Now that you have Dyad up and running, let's dive deeper into the art of crafting effective prompts to build amazing applications. The key to success with Dyad lies in how well you communicate your vision to the AI.
 
 ## The Power of Clear Communication
@@ -197,3 +200,440 @@ You've learned the fundamentals of natural language app building with Dyad. In t
 ---
 
 *Generated by [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)*
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Dyad Tutorial: Local-First AI App Building**
+- tutorial slug: **dyad-tutorial**
+- chapter focus: **Chapter 2: Natural Language App Building**
+- system context: **Dyad Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Natural Language App Building`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Dyad README](https://github.com/dyad-sh/dyad/blob/main/README.md)
+- [Dyad Releases](https://github.com/dyad-sh/dyad/releases)
+- [Dyad Repository](https://github.com/dyad-sh/dyad)
+
+### Cross-Tutorial Connection Map
+
+- [bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [Vercel AI SDK Tutorial](../vercel-ai-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Natural Language App Building`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Natural Language App Building
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Create`, `Build`, `management` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Natural Language App Building` as an operating subsystem inside **Dyad Tutorial: Local-First AI App Building**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `User`, `users`, `categories` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Natural Language App Building` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Create`.
+2. **Input normalization**: shape incoming data so `Build` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `management`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Dyad README](https://github.com/dyad-sh/dyad/blob/main/README.md)
+  Why it matters: authoritative reference on `Dyad README` (github.com).
+- [Dyad Releases](https://github.com/dyad-sh/dyad/releases)
+  Why it matters: authoritative reference on `Dyad Releases` (github.com).
+- [Dyad Repository](https://github.com/dyad-sh/dyad)
+  Why it matters: authoritative reference on `Dyad Repository` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Create` and `Build` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Dyad](01-getting-started.md)
+- [Next Chapter: Chapter 3: Component Integration](03-component-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dyad-tutorial/03-component-integration.md b/tutorials/dyad-tutorial/03-component-integration.md
index 2e979b2b..1e897526 100644
--- a/tutorials/dyad-tutorial/03-component-integration.md
+++ b/tutorials/dyad-tutorial/03-component-integration.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Component Integration
 
+Welcome to **Chapter 3: Component Integration**. In this part of **Dyad Tutorial: Local-First AI App Building**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Now that you can build apps with natural language, let's explore how to enhance them with additional components and functionality. Dyad provides a rich component library that you can integrate into your applications.
 
 ## Understanding Components
@@ -146,3 +149,488 @@ You've learned how to integrate components into your Dyad applications. In the n
 ---
 
 *Generated by [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)*
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Dyad Tutorial: Local-First AI App Building**
+- tutorial slug: **dyad-tutorial**
+- chapter focus: **Chapter 3: Component Integration**
+- system context: **Dyad Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Component Integration`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Dyad README](https://github.com/dyad-sh/dyad/blob/main/README.md)
+- [Dyad Releases](https://github.com/dyad-sh/dyad/releases)
+- [Dyad Repository](https://github.com/dyad-sh/dyad)
+
+### Cross-Tutorial Connection Map
+
+- [bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [Vercel AI SDK Tutorial](../vercel-ai-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Component Integration`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Component Integration
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `components`, `table`, `component` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Component Integration` as an operating subsystem inside **Dyad Tutorial: Local-First AI App Building**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `display`, `user`, `information` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Component Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `components`.
+2. **Input normalization**: shape incoming data so `table` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `component`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Dyad README](https://github.com/dyad-sh/dyad/blob/main/README.md)
+  Why it matters: authoritative reference on `Dyad README` (github.com).
+- [Dyad Releases](https://github.com/dyad-sh/dyad/releases)
+  Why it matters: authoritative reference on `Dyad Releases` (github.com).
+- [Dyad Repository](https://github.com/dyad-sh/dyad)
+  Why it matters: authoritative reference on `Dyad Repository` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `components` and `table` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Natural Language App Building](02-natural-language-building.md)
+- [Next Chapter: Chapter 4: Data Management](04-data-management.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dyad-tutorial/04-data-management.md b/tutorials/dyad-tutorial/04-data-management.md
index 369484ec..829c8621 100644
--- a/tutorials/dyad-tutorial/04-data-management.md
+++ b/tutorials/dyad-tutorial/04-data-management.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Data Management
 
+Welcome to **Chapter 4: Data Management**. In this part of **Dyad Tutorial: Local-First AI App Building**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Data is the lifeblood of modern applications. In this chapter, we'll explore how to connect your Dyad applications to databases and manage data effectively.
 
 ## Database Integration
@@ -126,3 +129,512 @@ You've learned data management fundamentals. Next, we'll explore API integration
 ---
 
 *Generated by [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)*
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Dyad Tutorial: Local-First AI App Building**
+- tutorial slug: **dyad-tutorial**
+- chapter focus: **Chapter 4: Data Management**
+- system context: **Dyad Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Data Management`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Dyad README](https://github.com/dyad-sh/dyad/blob/main/README.md)
+- [Dyad Releases](https://github.com/dyad-sh/dyad/releases)
+- [Dyad Repository](https://github.com/dyad-sh/dyad)
+
+### Cross-Tutorial Connection Map
+
+- [bolt.diy Tutorial](../bolt-diy-tutorial/)
+- [Vercel AI SDK Tutorial](../vercel-ai-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Data Management`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Data Management
+
+- tutorial context: **Dyad Tutorial: Local-First AI App Building**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `user`, `functionality`, `validation` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Data Management` as an operating subsystem inside **Dyad Tutorial: Local-First AI App Building**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Implement`, `fields`, `email` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Data Management` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `user`.
+2. **Input normalization**: shape incoming data so `functionality` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `validation`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Dyad README](https://github.com/dyad-sh/dyad/blob/main/README.md)
+  Why it matters: authoritative reference on `Dyad README` (github.com).
+- [Dyad Releases](https://github.com/dyad-sh/dyad/releases)
+  Why it matters: authoritative reference on `Dyad Releases` (github.com).
+- [Dyad Repository](https://github.com/dyad-sh/dyad)
+  Why it matters: authoritative reference on `Dyad Repository` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `user` and `functionality` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Component Integration](03-component-integration.md)
+- [Next Chapter: Chapter 5: API Integration](05-api-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dyad-tutorial/05-api-integration.md b/tutorials/dyad-tutorial/05-api-integration.md
index 1257e794..c0b5eada 100644
--- a/tutorials/dyad-tutorial/05-api-integration.md
+++ b/tutorials/dyad-tutorial/05-api-integration.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: API Integration
 
+Welcome to **Chapter 5: API Integration**. In this part of **Dyad Tutorial: Local-First AI App Building**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 APIs are the bridges that connect your Dyad applications to the broader ecosystem of services, data sources, and platforms. Whether you need to fetch weather data, process payments, send notifications, or integrate with AI services, mastering API integration is essential for building production-ready applications. In this chapter, we'll explore how Dyad simplifies connecting to external services while giving you full control over request handling, authentication, and error management.
 
 ## How API Integration Works in Dyad
@@ -786,3 +789,53 @@ With your APIs connected, your applications can now communicate with external se
 ---
 
 *Built with insights from the [Dyad](https://github.com/dyad-sh/dyad) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `response`, `Error`, `Promise` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: API Integration` as an operating subsystem inside **Dyad Tutorial: Local-First AI App Building**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `fetch`, `json`, `weather` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: API Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `response`.
+2. **Input normalization**: shape incoming data so `Error` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Promise`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Dyad README](https://github.com/dyad-sh/dyad/blob/main/README.md)
+  Why it matters: authoritative reference on `Dyad README` (github.com).
+- [Dyad Releases](https://github.com/dyad-sh/dyad/releases)
+  Why it matters: authoritative reference on `Dyad Releases` (github.com).
+- [Dyad Repository](https://github.com/dyad-sh/dyad)
+  Why it matters: authoritative reference on `Dyad Repository` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `response` and `Error` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Data Management](04-data-management.md)
+- [Next Chapter: Chapter 6: Customization and Styling](06-customization-styling.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dyad-tutorial/06-customization-styling.md b/tutorials/dyad-tutorial/06-customization-styling.md
index b4159fbb..2bfc411e 100644
--- a/tutorials/dyad-tutorial/06-customization-styling.md
+++ b/tutorials/dyad-tutorial/06-customization-styling.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Customization and Styling
 
+Welcome to **Chapter 6: Customization and Styling**. In this part of **Dyad Tutorial: Local-First AI App Building**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 A great application is not just functional -- it needs to look and feel polished. Dyad generates visually appealing applications out of the box, but the real power comes from customizing themes, layouts, animations, and responsive behavior to match your brand and user expectations. In this chapter, we will explore the full spectrum of styling capabilities available in Dyad, from CSS framework integration to building complete design systems.
 
 ## Styling Architecture in Dyad
@@ -845,3 +848,53 @@ Your applications now look and feel professional. In the next chapter, we will e
 ---
 
 *Built with insights from the [Dyad](https://github.com/dyad-sh/dyad) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `dark`, `gray`, `theme` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Customization and Styling` as an operating subsystem inside **Dyad Tutorial: Local-First AI App Building**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `text`, `className`, `border` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Customization and Styling` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `dark`.
+2. **Input normalization**: shape incoming data so `gray` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `theme`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Dyad README](https://github.com/dyad-sh/dyad/blob/main/README.md)
+  Why it matters: authoritative reference on `Dyad README` (github.com).
+- [Dyad Releases](https://github.com/dyad-sh/dyad/releases)
+  Why it matters: authoritative reference on `Dyad Releases` (github.com).
+- [Dyad Repository](https://github.com/dyad-sh/dyad)
+  Why it matters: authoritative reference on `Dyad Repository` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `dark` and `gray` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: API Integration](05-api-integration.md)
+- [Next Chapter: Chapter 7: Testing and Validation](07-testing-validation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dyad-tutorial/07-testing-validation.md b/tutorials/dyad-tutorial/07-testing-validation.md
index ec606ef8..10e9a40e 100644
--- a/tutorials/dyad-tutorial/07-testing-validation.md
+++ b/tutorials/dyad-tutorial/07-testing-validation.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Testing and Validation
 
+Welcome to **Chapter 7: Testing and Validation**. In this part of **Dyad Tutorial: Local-First AI App Building**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 AI-generated code needs the same rigorous testing as hand-written code -- sometimes more. Because Dyad produces entire components and service layers from natural language prompts, having a comprehensive testing strategy ensures that the generated output behaves correctly, handles edge cases, and remains maintainable as your application evolves. In this chapter, we will cover unit testing, integration testing, end-to-end testing, and validation techniques specifically tailored for Dyad applications.
 
 ## The Testing Pyramid for Dyad Apps
@@ -903,3 +906,53 @@ With a solid testing foundation, your applications are ready for the real world.
 ---
 
 *Built with insights from the [Dyad](https://github.com/dyad-sh/dyad) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `expect`, `name`, `test` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Testing and Validation` as an operating subsystem inside **Dyad Tutorial: Local-First AI App Building**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `email`, `result`, `page` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Testing and Validation` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `expect`.
+2. **Input normalization**: shape incoming data so `name` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `test`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Dyad README](https://github.com/dyad-sh/dyad/blob/main/README.md)
+  Why it matters: authoritative reference on `Dyad README` (github.com).
+- [Dyad Releases](https://github.com/dyad-sh/dyad/releases)
+  Why it matters: authoritative reference on `Dyad Releases` (github.com).
+- [Dyad Repository](https://github.com/dyad-sh/dyad)
+  Why it matters: authoritative reference on `Dyad Repository` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `expect` and `name` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Customization and Styling](06-customization-styling.md)
+- [Next Chapter: Chapter 8: Deployment and Sharing](08-deployment-sharing.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/dyad-tutorial/08-deployment-sharing.md b/tutorials/dyad-tutorial/08-deployment-sharing.md
index e9faac52..9c7aa447 100644
--- a/tutorials/dyad-tutorial/08-deployment-sharing.md
+++ b/tutorials/dyad-tutorial/08-deployment-sharing.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Deployment and Sharing
 
+Welcome to **Chapter 8: Deployment and Sharing**. In this part of **Dyad Tutorial: Local-First AI App Building**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 You have built, styled, and tested your Dyad application -- now it is time to get it in front of users. Deployment transforms your local development project into a live, accessible application. In this final chapter, we will cover the full deployment lifecycle: building optimized production bundles, choosing the right hosting platform, configuring CI/CD pipelines, setting up custom domains, and sharing your projects with collaborators and the world.
 
 ## The Deployment Pipeline
@@ -803,3 +806,52 @@ You have completed the Dyad tutorial. You now have the knowledge and tools to bu
 ---
 
 *Built with insights from the [Dyad](https://github.com/dyad-sh/dyad) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `build`, `name`, `fill` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Deployment and Sharing` as an operating subsystem inside **Dyad Tutorial: Local-First AI App Building**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `uses`, `classDef`, `stroke` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Deployment and Sharing` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `build`.
+2. **Input normalization**: shape incoming data so `name` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `fill`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Dyad README](https://github.com/dyad-sh/dyad/blob/main/README.md)
+  Why it matters: authoritative reference on `Dyad README` (github.com).
+- [Dyad Releases](https://github.com/dyad-sh/dyad/releases)
+  Why it matters: authoritative reference on `Dyad Releases` (github.com).
+- [Dyad Repository](https://github.com/dyad-sh/dyad)
+  Why it matters: authoritative reference on `Dyad Repository` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `build` and `name` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Testing and Validation](07-testing-validation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/elizaos-tutorial/01-getting-started.md b/tutorials/elizaos-tutorial/01-getting-started.md
index 8c9bfb36..b24a13c1 100644
--- a/tutorials/elizaos-tutorial/01-getting-started.md
+++ b/tutorials/elizaos-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ nav_order: 1
 
 # Chapter 1: Getting Started with ElizaOS
 
+Welcome to **Chapter 1: Getting Started with ElizaOS**. In this part of **ElizaOS: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 ElizaOS is a TypeScript framework for building autonomous AI agents. Agents can operate across Discord, Telegram, Slack, X/Twitter, and Web3 platforms. They're configured through character files, extended through plugins, and powered by any LLM backend. This chapter covers installation, project setup, and running your first agent.
@@ -329,3 +332,48 @@ DEBUG=elizaos:runtime,elizaos:memory elizaos dev
 ---
 
 *Built with insights from the [ElizaOS repository](https://github.com/elizaOS/eliza) and community documentation.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `elizaos`, `plugin`, `character` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with ElizaOS` as an operating subsystem inside **ElizaOS: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Runtime`, `participant`, `Connector` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with ElizaOS` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `elizaos`.
+2. **Input normalization**: shape incoming data so `plugin` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `character`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [ElizaOS](https://github.com/elizaOS/eliza)
+  Why it matters: authoritative reference on `ElizaOS` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `elizaos` and `plugin` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Agent Runtime](02-agent-runtime.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/elizaos-tutorial/02-agent-runtime.md b/tutorials/elizaos-tutorial/02-agent-runtime.md
index 3c997acd..61d1f434 100644
--- a/tutorials/elizaos-tutorial/02-agent-runtime.md
+++ b/tutorials/elizaos-tutorial/02-agent-runtime.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Agent Runtime
 
+Welcome to **Chapter 2: Agent Runtime**. In this part of **ElizaOS: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 The `AgentRuntime` is the central engine of ElizaOS. It manages an agent's lifecycle, processes events, coordinates plugins, invokes models, and maintains state. Every agent in ElizaOS is an instance of `AgentRuntime`. This chapter examines its architecture and internals.
@@ -564,3 +567,49 @@ class AgentRuntime {
 ---
 
 *Built with insights from the [ElizaOS repository](https://github.com/elizaOS/eliza) and community documentation.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `plugin`, `message`, `name` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Agent Runtime` as an operating subsystem inside **ElizaOS: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Promise`, `handler`, `action` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Agent Runtime` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `plugin`.
+2. **Input normalization**: shape incoming data so `message` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `name`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [ElizaOS](https://github.com/elizaOS/eliza)
+  Why it matters: authoritative reference on `ElizaOS` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `plugin` and `message` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with ElizaOS](01-getting-started.md)
+- [Next Chapter: Chapter 3: Character System](03-character-system.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/elizaos-tutorial/03-character-system.md b/tutorials/elizaos-tutorial/03-character-system.md
index 20f417ce..c894911a 100644
--- a/tutorials/elizaos-tutorial/03-character-system.md
+++ b/tutorials/elizaos-tutorial/03-character-system.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Character System
 
+Welcome to **Chapter 3: Character System**. In this part of **ElizaOS: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 Characters are the personality layer of ElizaOS agents. A character file defines who an agent is — its name, bio, communication style, knowledge domains, goals, and behavioral rules. This decouples agent identity from code, making it easy to create, share, and iterate on agent personalities.
@@ -439,3 +442,49 @@ function buildSystemPrompt(character: Character, state: State): string {
 ---
 
 *Built with insights from the [ElizaOS repository](https://github.com/elizaOS/eliza) and community documentation.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `plugin`, `elizaos`, `sections` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Character System` as an operating subsystem inside **ElizaOS: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `name`, `push`, `state` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Character System` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `plugin`.
+2. **Input normalization**: shape incoming data so `elizaos` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `sections`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [ElizaOS](https://github.com/elizaOS/eliza)
+  Why it matters: authoritative reference on `ElizaOS` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `plugin` and `elizaos` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Agent Runtime](02-agent-runtime.md)
+- [Next Chapter: Chapter 4: Plugin Architecture](04-plugin-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/elizaos-tutorial/04-plugin-architecture.md b/tutorials/elizaos-tutorial/04-plugin-architecture.md
index 7403cdd2..aa0b712f 100644
--- a/tutorials/elizaos-tutorial/04-plugin-architecture.md
+++ b/tutorials/elizaos-tutorial/04-plugin-architecture.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Plugin Architecture
 
+Welcome to **Chapter 4: Plugin Architecture**. In this part of **ElizaOS: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 ElizaOS is built on a plugin-first architecture. Every capability — model providers, database adapters, platform connectors, custom actions — is delivered as a plugin. The core runtime is intentionally minimal; plugins provide all the functionality. This chapter covers the plugin system in depth.
@@ -543,3 +546,49 @@ The `@elizaos/plugin-bootstrap` is the essential core plugin every agent needs:
 ---
 
 *Built with insights from the [ElizaOS repository](https://github.com/elizaOS/eliza) and community documentation.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `runtime`, `name`, `message` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Plugin Architecture` as an operating subsystem inside **ElizaOS: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `description`, `Promise`, `search` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Plugin Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `runtime`.
+2. **Input normalization**: shape incoming data so `name` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `message`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [ElizaOS](https://github.com/elizaOS/eliza)
+  Why it matters: authoritative reference on `ElizaOS` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `runtime` and `name` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Character System](03-character-system.md)
+- [Next Chapter: Chapter 5: Memory & RAG](05-memory-rag.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/elizaos-tutorial/05-memory-rag.md b/tutorials/elizaos-tutorial/05-memory-rag.md
index 45fc9457..0c875014 100644
--- a/tutorials/elizaos-tutorial/05-memory-rag.md
+++ b/tutorials/elizaos-tutorial/05-memory-rag.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Memory & RAG
 
+Welcome to **Chapter 5: Memory & RAG**. In this part of **ElizaOS: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 ElizaOS provides a sophisticated memory system that combines conversation history, semantic search, and retrieval-augmented generation (RAG). Agents remember past conversations, retrieve relevant knowledge from ingested documents, and maintain context across sessions. This chapter covers the memory architecture, embedding pipeline, and RAG integration.
@@ -583,3 +586,49 @@ class MemoryPruner {
 ---
 
 *Built with insights from the [ElizaOS repository](https://github.com/elizaOS/eliza) and community documentation.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `text`, `chunks`, `Promise` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Memory & RAG` as an operating subsystem inside **ElizaOS: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `params`, `tableName`, `count` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Memory & RAG` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `text`.
+2. **Input normalization**: shape incoming data so `chunks` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Promise`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [ElizaOS](https://github.com/elizaOS/eliza)
+  Why it matters: authoritative reference on `ElizaOS` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `text` and `chunks` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Plugin Architecture](04-plugin-architecture.md)
+- [Next Chapter: Chapter 6: Platform Connectors](06-platform-connectors.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/elizaos-tutorial/06-platform-connectors.md b/tutorials/elizaos-tutorial/06-platform-connectors.md
index 7fdbd30f..8005f68e 100644
--- a/tutorials/elizaos-tutorial/06-platform-connectors.md
+++ b/tutorials/elizaos-tutorial/06-platform-connectors.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Platform Connectors
 
+Welcome to **Chapter 6: Platform Connectors**. In this part of **ElizaOS: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 ElizaOS agents operate across multiple platforms simultaneously — Discord, Telegram, Slack, X/Twitter, and Farcaster. Each platform has its own API, message format, and interaction model. Connectors bridge the gap between platform-specific protocols and ElizaOS's unified event pipeline, allowing agents to be written once and deployed everywhere.
@@ -516,3 +519,49 @@ Configure which platforms an agent connects to:
 ---
 
 *Built with insights from the [ElizaOS repository](https://github.com/elizaOS/eliza) and community documentation.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `text`, `content`, `platform` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Platform Connectors` as an operating subsystem inside **ElizaOS: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `event`, `roomId`, `client` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Platform Connectors` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `text`.
+2. **Input normalization**: shape incoming data so `content` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `platform`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [ElizaOS](https://github.com/elizaOS/eliza)
+  Why it matters: authoritative reference on `ElizaOS` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `text` and `content` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Memory & RAG](05-memory-rag.md)
+- [Next Chapter: Chapter 7: Multi-Agent Orchestration](07-multi-agent.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/elizaos-tutorial/07-multi-agent.md b/tutorials/elizaos-tutorial/07-multi-agent.md
index 1f212a4e..b97bdd48 100644
--- a/tutorials/elizaos-tutorial/07-multi-agent.md
+++ b/tutorials/elizaos-tutorial/07-multi-agent.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Multi-Agent Orchestration
 
+Welcome to **Chapter 7: Multi-Agent Orchestration**. In this part of **ElizaOS: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 ElizaOS natively supports multi-agent systems — groups of specialized agents that collaborate, delegate tasks, share context, and coordinate their actions. This goes beyond running multiple independent agents; ElizaOS provides primitives for inter-agent communication, role assignment, task delegation, and load balancing.
@@ -572,3 +575,49 @@ const predefinedRoles: Record<string, AgentRole> = {
 ---
 
 *Built with insights from the [ElizaOS repository](https://github.com/elizaOS/eliza) and community documentation.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `group`, `agent`, `task` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Multi-Agent Orchestration` as an operating subsystem inside **ElizaOS: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `name`, `agents`, `config` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Multi-Agent Orchestration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `group`.
+2. **Input normalization**: shape incoming data so `agent` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `task`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [ElizaOS](https://github.com/elizaOS/eliza)
+  Why it matters: authoritative reference on `ElizaOS` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `group` and `agent` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Platform Connectors](06-platform-connectors.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/elizaos-tutorial/08-production-deployment.md b/tutorials/elizaos-tutorial/08-production-deployment.md
index 6d6f7df4..614a3c82 100644
--- a/tutorials/elizaos-tutorial/08-production-deployment.md
+++ b/tutorials/elizaos-tutorial/08-production-deployment.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **ElizaOS: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 Deploying ElizaOS agents for reliable, always-on operation requires production-grade infrastructure — containerization, monitoring, database management, security hardening, and scaling strategies. This chapter covers Docker deployment, Web3 integration, monitoring, and operational best practices.
@@ -568,3 +571,48 @@ This concludes the ElizaOS Deep Dive tutorial. You now have a comprehensive unde
 ---
 
 *Built with insights from the [ElizaOS repository](https://github.com/elizaOS/eliza) and community documentation.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `elizaos`, `wallet`, `process` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **ElizaOS: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `configured`, `checks`, `sanitized` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `elizaos`.
+2. **Input normalization**: shape incoming data so `wallet` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `process`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [ElizaOS](https://github.com/elizaOS/eliza)
+  Why it matters: authoritative reference on `ElizaOS` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `elizaos` and `wallet` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Multi-Agent Orchestration](07-multi-agent.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/everything-claude-code-tutorial/01-getting-started.md b/tutorials/everything-claude-code-tutorial/01-getting-started.md
index 479467fa..d026eea0 100644
--- a/tutorials/everything-claude-code-tutorial/01-getting-started.md
+++ b/tutorials/everything-claude-code-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Everything Claude Code Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets the package installed and verifies first workflow execution.
 
 ## Learning Goals
@@ -47,3 +50,589 @@ Then install rules from the repo clone:
 You now have a functioning baseline configuration.
 
 Next: [Chapter 2: Architecture and Component Topology](02-architecture-and-component-topology.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- tutorial slug: **everything-claude-code-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Everything Claude Code Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Everything Claude Code Repository](https://github.com/affaan-m/everything-claude-code)
+- [README](https://github.com/affaan-m/everything-claude-code/blob/main/README.md)
+- [Contributing Guide](https://github.com/affaan-m/everything-claude-code/blob/main/CONTRIBUTING.md)
+- [Rules Guide](https://github.com/affaan-m/everything-claude-code/blob/main/rules/README.md)
+- [OpenCode Support](https://github.com/affaan-m/everything-claude-code/blob/main/.opencode/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Wshobson Agents Tutorial](../wshobson-agents-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `everything`, `claude`, `code` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `plugin`, `install`, `marketplace` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `everything`.
+2. **Input normalization**: shape incoming data so `claude` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `code`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Everything Claude Code Repository](https://github.com/affaan-m/everything-claude-code)
+  Why it matters: authoritative reference on `Everything Claude Code Repository` (github.com).
+- [README](https://github.com/affaan-m/everything-claude-code/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Contributing Guide](https://github.com/affaan-m/everything-claude-code/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Rules Guide](https://github.com/affaan-m/everything-claude-code/blob/main/rules/README.md)
+  Why it matters: authoritative reference on `Rules Guide` (github.com).
+- [OpenCode Support](https://github.com/affaan-m/everything-claude-code/blob/main/.opencode/README.md)
+  Why it matters: authoritative reference on `OpenCode Support` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `everything` and `claude` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Architecture and Component Topology](02-architecture-and-component-topology.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/everything-claude-code-tutorial/02-architecture-and-component-topology.md b/tutorials/everything-claude-code-tutorial/02-architecture-and-component-topology.md
index 0c5fa16e..c022c48a 100644
--- a/tutorials/everything-claude-code-tutorial/02-architecture-and-component-topology.md
+++ b/tutorials/everything-claude-code-tutorial/02-architecture-and-component-topology.md
@@ -7,6 +7,9 @@ parent: Everything Claude Code Tutorial
 
 # Chapter 2: Architecture and Component Topology
 
+Welcome to **Chapter 2: Architecture and Component Topology**. In this part of **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter maps the system into manageable component groups.
 
 ## Learning Goals
@@ -40,3 +43,598 @@ Keep each layer focused and composable; avoid collapsing all behavior into one l
 You now understand the component architecture and boundaries.
 
 Next: [Chapter 3: Installation Modes and Rules Strategy](03-installation-modes-and-rules-strategy.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- tutorial slug: **everything-claude-code-tutorial**
+- chapter focus: **Chapter 2: Architecture and Component Topology**
+- system context: **Everything Claude Code Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Architecture and Component Topology`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Everything Claude Code Repository](https://github.com/affaan-m/everything-claude-code)
+- [README](https://github.com/affaan-m/everything-claude-code/blob/main/README.md)
+- [Contributing Guide](https://github.com/affaan-m/everything-claude-code/blob/main/CONTRIBUTING.md)
+- [Rules Guide](https://github.com/affaan-m/everything-claude-code/blob/main/rules/README.md)
+- [OpenCode Support](https://github.com/affaan-m/everything-claude-code/blob/main/.opencode/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Wshobson Agents Tutorial](../wshobson-agents-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Architecture and Component Topology`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Architecture and Component Topology
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Architecture and Component Topology` as an operating subsystem inside **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Architecture and Component Topology` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Everything Claude Code Repository](https://github.com/affaan-m/everything-claude-code)
+  Why it matters: authoritative reference on `Everything Claude Code Repository` (github.com).
+- [README](https://github.com/affaan-m/everything-claude-code/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Contributing Guide](https://github.com/affaan-m/everything-claude-code/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Rules Guide](https://github.com/affaan-m/everything-claude-code/blob/main/rules/README.md)
+  Why it matters: authoritative reference on `Rules Guide` (github.com).
+- [OpenCode Support](https://github.com/affaan-m/everything-claude-code/blob/main/.opencode/README.md)
+  Why it matters: authoritative reference on `OpenCode Support` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Installation Modes and Rules Strategy](03-installation-modes-and-rules-strategy.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/everything-claude-code-tutorial/03-installation-modes-and-rules-strategy.md b/tutorials/everything-claude-code-tutorial/03-installation-modes-and-rules-strategy.md
index b7f1d37f..bc963941 100644
--- a/tutorials/everything-claude-code-tutorial/03-installation-modes-and-rules-strategy.md
+++ b/tutorials/everything-claude-code-tutorial/03-installation-modes-and-rules-strategy.md
@@ -7,6 +7,9 @@ parent: Everything Claude Code Tutorial
 
 # Chapter 3: Installation Modes and Rules Strategy
 
+Welcome to **Chapter 3: Installation Modes and Rules Strategy**. In this part of **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers installation choices and language-rule management.
 
 ## Learning Goals
@@ -38,3 +41,598 @@ This chapter covers installation choices and language-rule management.
 You now have a reproducible installation strategy.
 
 Next: [Chapter 4: Agents, Skills, and Command Orchestration](04-agents-skills-and-command-orchestration.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- tutorial slug: **everything-claude-code-tutorial**
+- chapter focus: **Chapter 3: Installation Modes and Rules Strategy**
+- system context: **Everything Claude Code Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Installation Modes and Rules Strategy`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Everything Claude Code Repository](https://github.com/affaan-m/everything-claude-code)
+- [README](https://github.com/affaan-m/everything-claude-code/blob/main/README.md)
+- [Contributing Guide](https://github.com/affaan-m/everything-claude-code/blob/main/CONTRIBUTING.md)
+- [Rules Guide](https://github.com/affaan-m/everything-claude-code/blob/main/rules/README.md)
+- [OpenCode Support](https://github.com/affaan-m/everything-claude-code/blob/main/.opencode/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Wshobson Agents Tutorial](../wshobson-agents-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Installation Modes and Rules Strategy`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Installation Modes and Rules Strategy
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Installation Modes and Rules Strategy` as an operating subsystem inside **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Installation Modes and Rules Strategy` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Everything Claude Code Repository](https://github.com/affaan-m/everything-claude-code)
+  Why it matters: authoritative reference on `Everything Claude Code Repository` (github.com).
+- [README](https://github.com/affaan-m/everything-claude-code/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Contributing Guide](https://github.com/affaan-m/everything-claude-code/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Rules Guide](https://github.com/affaan-m/everything-claude-code/blob/main/rules/README.md)
+  Why it matters: authoritative reference on `Rules Guide` (github.com).
+- [OpenCode Support](https://github.com/affaan-m/everything-claude-code/blob/main/.opencode/README.md)
+  Why it matters: authoritative reference on `OpenCode Support` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Architecture and Component Topology](02-architecture-and-component-topology.md)
+- [Next Chapter: Chapter 4: Agents, Skills, and Command Orchestration](04-agents-skills-and-command-orchestration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/everything-claude-code-tutorial/04-agents-skills-and-command-orchestration.md b/tutorials/everything-claude-code-tutorial/04-agents-skills-and-command-orchestration.md
index cfb3d6da..5514dc87 100644
--- a/tutorials/everything-claude-code-tutorial/04-agents-skills-and-command-orchestration.md
+++ b/tutorials/everything-claude-code-tutorial/04-agents-skills-and-command-orchestration.md
@@ -7,6 +7,9 @@ parent: Everything Claude Code Tutorial
 
 # Chapter 4: Agents, Skills, and Command Orchestration
 
+Welcome to **Chapter 4: Agents, Skills, and Command Orchestration**. In this part of **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on day-to-day orchestration patterns.
 
 ## Learning Goals
@@ -38,3 +41,598 @@ This chapter focuses on day-to-day orchestration patterns.
 You now have a practical command/agent orchestration baseline.
 
 Next: [Chapter 5: Hooks, MCP, and Continuous Learning Loops](05-hooks-mcp-and-continuous-learning-loops.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: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- tutorial slug: **everything-claude-code-tutorial**
+- chapter focus: **Chapter 4: Agents, Skills, and Command Orchestration**
+- system context: **Everything Claude Code Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Agents, Skills, and Command Orchestration`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Everything Claude Code Repository](https://github.com/affaan-m/everything-claude-code)
+- [README](https://github.com/affaan-m/everything-claude-code/blob/main/README.md)
+- [Contributing Guide](https://github.com/affaan-m/everything-claude-code/blob/main/CONTRIBUTING.md)
+- [Rules Guide](https://github.com/affaan-m/everything-claude-code/blob/main/rules/README.md)
+- [OpenCode Support](https://github.com/affaan-m/everything-claude-code/blob/main/.opencode/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Wshobson Agents Tutorial](../wshobson-agents-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Agents, Skills, and Command Orchestration`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Agents, Skills, and Command Orchestration
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Agents, Skills, and Command Orchestration` as an operating subsystem inside **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Agents, Skills, and Command Orchestration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Everything Claude Code Repository](https://github.com/affaan-m/everything-claude-code)
+  Why it matters: authoritative reference on `Everything Claude Code Repository` (github.com).
+- [README](https://github.com/affaan-m/everything-claude-code/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Contributing Guide](https://github.com/affaan-m/everything-claude-code/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Rules Guide](https://github.com/affaan-m/everything-claude-code/blob/main/rules/README.md)
+  Why it matters: authoritative reference on `Rules Guide` (github.com).
+- [OpenCode Support](https://github.com/affaan-m/everything-claude-code/blob/main/.opencode/README.md)
+  Why it matters: authoritative reference on `OpenCode Support` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Installation Modes and Rules Strategy](03-installation-modes-and-rules-strategy.md)
+- [Next Chapter: Chapter 5: Hooks, MCP, and Continuous Learning Loops](05-hooks-mcp-and-continuous-learning-loops.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/everything-claude-code-tutorial/05-hooks-mcp-and-continuous-learning-loops.md b/tutorials/everything-claude-code-tutorial/05-hooks-mcp-and-continuous-learning-loops.md
index 44314883..9c6af786 100644
--- a/tutorials/everything-claude-code-tutorial/05-hooks-mcp-and-continuous-learning-loops.md
+++ b/tutorials/everything-claude-code-tutorial/05-hooks-mcp-and-continuous-learning-loops.md
@@ -7,6 +7,9 @@ parent: Everything Claude Code Tutorial
 
 # Chapter 5: Hooks, MCP, and Continuous Learning Loops
 
+Welcome to **Chapter 5: Hooks, MCP, and Continuous Learning Loops**. In this part of **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains automation and feedback loops that improve over time.
 
 ## Learning Goals
@@ -39,3 +42,598 @@ This chapter explains automation and feedback loops that improve over time.
 You now understand how to run automated feedback loops with controlled risk.
 
 Next: [Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)](06-cross-platform-workflows-cursor-and-opencode.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: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- tutorial slug: **everything-claude-code-tutorial**
+- chapter focus: **Chapter 5: Hooks, MCP, and Continuous Learning Loops**
+- system context: **Everything Claude Code Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Hooks, MCP, and Continuous Learning Loops`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Everything Claude Code Repository](https://github.com/affaan-m/everything-claude-code)
+- [README](https://github.com/affaan-m/everything-claude-code/blob/main/README.md)
+- [Contributing Guide](https://github.com/affaan-m/everything-claude-code/blob/main/CONTRIBUTING.md)
+- [Rules Guide](https://github.com/affaan-m/everything-claude-code/blob/main/rules/README.md)
+- [OpenCode Support](https://github.com/affaan-m/everything-claude-code/blob/main/.opencode/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Wshobson Agents Tutorial](../wshobson-agents-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Hooks, MCP, and Continuous Learning Loops`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Hooks, MCP, and Continuous Learning Loops
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Hooks, MCP, and Continuous Learning Loops` as an operating subsystem inside **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Hooks, MCP, and Continuous Learning Loops` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Everything Claude Code Repository](https://github.com/affaan-m/everything-claude-code)
+  Why it matters: authoritative reference on `Everything Claude Code Repository` (github.com).
+- [README](https://github.com/affaan-m/everything-claude-code/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Contributing Guide](https://github.com/affaan-m/everything-claude-code/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Rules Guide](https://github.com/affaan-m/everything-claude-code/blob/main/rules/README.md)
+  Why it matters: authoritative reference on `Rules Guide` (github.com).
+- [OpenCode Support](https://github.com/affaan-m/everything-claude-code/blob/main/.opencode/README.md)
+  Why it matters: authoritative reference on `OpenCode Support` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Agents, Skills, and Command Orchestration](04-agents-skills-and-command-orchestration.md)
+- [Next Chapter: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)](06-cross-platform-workflows-cursor-and-opencode.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/everything-claude-code-tutorial/06-cross-platform-workflows-cursor-and-opencode.md b/tutorials/everything-claude-code-tutorial/06-cross-platform-workflows-cursor-and-opencode.md
index 1d504f4b..f55b60e4 100644
--- a/tutorials/everything-claude-code-tutorial/06-cross-platform-workflows-cursor-and-opencode.md
+++ b/tutorials/everything-claude-code-tutorial/06-cross-platform-workflows-cursor-and-opencode.md
@@ -7,6 +7,9 @@ parent: Everything Claude Code Tutorial
 
 # Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
 
+Welcome to **Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)**. In this part of **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers portability patterns across editor and agent runtimes.
 
 ## Learning Goals
@@ -33,3 +36,598 @@ This chapter covers portability patterns across editor and agent runtimes.
 You now have a practical cross-platform portability model.
 
 Next: [Chapter 7: Testing, Verification, and Troubleshooting](07-testing-verification-and-troubleshooting.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: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- tutorial slug: **everything-claude-code-tutorial**
+- chapter focus: **Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)**
+- system context: **Everything Claude Code Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Everything Claude Code Repository](https://github.com/affaan-m/everything-claude-code)
+- [README](https://github.com/affaan-m/everything-claude-code/blob/main/README.md)
+- [Contributing Guide](https://github.com/affaan-m/everything-claude-code/blob/main/CONTRIBUTING.md)
+- [Rules Guide](https://github.com/affaan-m/everything-claude-code/blob/main/rules/README.md)
+- [OpenCode Support](https://github.com/affaan-m/everything-claude-code/blob/main/.opencode/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Wshobson Agents Tutorial](../wshobson-agents-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)` as an operating subsystem inside **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Everything Claude Code Repository](https://github.com/affaan-m/everything-claude-code)
+  Why it matters: authoritative reference on `Everything Claude Code Repository` (github.com).
+- [README](https://github.com/affaan-m/everything-claude-code/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Contributing Guide](https://github.com/affaan-m/everything-claude-code/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Rules Guide](https://github.com/affaan-m/everything-claude-code/blob/main/rules/README.md)
+  Why it matters: authoritative reference on `Rules Guide` (github.com).
+- [OpenCode Support](https://github.com/affaan-m/everything-claude-code/blob/main/.opencode/README.md)
+  Why it matters: authoritative reference on `OpenCode Support` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Hooks, MCP, and Continuous Learning Loops](05-hooks-mcp-and-continuous-learning-loops.md)
+- [Next Chapter: Chapter 7: Testing, Verification, and Troubleshooting](07-testing-verification-and-troubleshooting.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/everything-claude-code-tutorial/07-testing-verification-and-troubleshooting.md b/tutorials/everything-claude-code-tutorial/07-testing-verification-and-troubleshooting.md
index fdbcd901..257acafa 100644
--- a/tutorials/everything-claude-code-tutorial/07-testing-verification-and-troubleshooting.md
+++ b/tutorials/everything-claude-code-tutorial/07-testing-verification-and-troubleshooting.md
@@ -7,6 +7,9 @@ parent: Everything Claude Code Tutorial
 
 # Chapter 7: Testing, Verification, and Troubleshooting
 
+Welcome to **Chapter 7: Testing, Verification, and Troubleshooting**. In this part of **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter provides quality and incident-response routines.
 
 ## Learning Goals
@@ -34,3 +37,598 @@ This chapter provides quality and incident-response routines.
 You now have a reliability playbook for daily operations.
 
 Next: [Chapter 8: Contribution Workflow and Governance](08-contribution-workflow-and-governance.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- tutorial slug: **everything-claude-code-tutorial**
+- chapter focus: **Chapter 7: Testing, Verification, and Troubleshooting**
+- system context: **Everything Claude Code Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Testing, Verification, and Troubleshooting`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Everything Claude Code Repository](https://github.com/affaan-m/everything-claude-code)
+- [README](https://github.com/affaan-m/everything-claude-code/blob/main/README.md)
+- [Contributing Guide](https://github.com/affaan-m/everything-claude-code/blob/main/CONTRIBUTING.md)
+- [Rules Guide](https://github.com/affaan-m/everything-claude-code/blob/main/rules/README.md)
+- [OpenCode Support](https://github.com/affaan-m/everything-claude-code/blob/main/.opencode/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Wshobson Agents Tutorial](../wshobson-agents-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Testing, Verification, and Troubleshooting`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Testing, Verification, and Troubleshooting
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Testing, Verification, and Troubleshooting` as an operating subsystem inside **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Testing, Verification, and Troubleshooting` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Everything Claude Code Repository](https://github.com/affaan-m/everything-claude-code)
+  Why it matters: authoritative reference on `Everything Claude Code Repository` (github.com).
+- [README](https://github.com/affaan-m/everything-claude-code/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Contributing Guide](https://github.com/affaan-m/everything-claude-code/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Rules Guide](https://github.com/affaan-m/everything-claude-code/blob/main/rules/README.md)
+  Why it matters: authoritative reference on `Rules Guide` (github.com).
+- [OpenCode Support](https://github.com/affaan-m/everything-claude-code/blob/main/.opencode/README.md)
+  Why it matters: authoritative reference on `OpenCode Support` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Cross-Platform Workflows (Cursor and OpenCode)](06-cross-platform-workflows-cursor-and-opencode.md)
+- [Next Chapter: Chapter 8: Contribution Workflow and Governance](08-contribution-workflow-and-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/everything-claude-code-tutorial/08-contribution-workflow-and-governance.md b/tutorials/everything-claude-code-tutorial/08-contribution-workflow-and-governance.md
index 409f01a3..1621c3cb 100644
--- a/tutorials/everything-claude-code-tutorial/08-contribution-workflow-and-governance.md
+++ b/tutorials/everything-claude-code-tutorial/08-contribution-workflow-and-governance.md
@@ -7,6 +7,9 @@ parent: Everything Claude Code Tutorial
 
 # Chapter 8: Contribution Workflow and Governance
 
+Welcome to **Chapter 8: Contribution Workflow and Governance**. In this part of **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains how to contribute new components while preserving quality and consistency.
 
 ## Learning Goals
@@ -45,3 +48,585 @@ Next steps:
 - establish a team baseline command/skill stack
 - codify verification gates for all workflow changes
 - contribute one focused component with tests and docs
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- tutorial slug: **everything-claude-code-tutorial**
+- chapter focus: **Chapter 8: Contribution Workflow and Governance**
+- system context: **Everything Claude Code Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Contribution Workflow and Governance`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Everything Claude Code Repository](https://github.com/affaan-m/everything-claude-code)
+- [README](https://github.com/affaan-m/everything-claude-code/blob/main/README.md)
+- [Contributing Guide](https://github.com/affaan-m/everything-claude-code/blob/main/CONTRIBUTING.md)
+- [Rules Guide](https://github.com/affaan-m/everything-claude-code/blob/main/rules/README.md)
+- [OpenCode Support](https://github.com/affaan-m/everything-claude-code/blob/main/.opencode/README.md)
+
+### Cross-Tutorial Connection Map
+
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Wshobson Agents Tutorial](../wshobson-agents-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Contribution Workflow and Governance`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Contribution Workflow and Governance
+
+- tutorial context: **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Contribution Workflow and Governance` as an operating subsystem inside **Everything Claude Code Tutorial: Production Configuration Patterns for Claude Code**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Contribution Workflow and Governance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Everything Claude Code Repository](https://github.com/affaan-m/everything-claude-code)
+  Why it matters: authoritative reference on `Everything Claude Code Repository` (github.com).
+- [README](https://github.com/affaan-m/everything-claude-code/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Contributing Guide](https://github.com/affaan-m/everything-claude-code/blob/main/CONTRIBUTING.md)
+  Why it matters: authoritative reference on `Contributing Guide` (github.com).
+- [Rules Guide](https://github.com/affaan-m/everything-claude-code/blob/main/rules/README.md)
+  Why it matters: authoritative reference on `Rules Guide` (github.com).
+- [OpenCode Support](https://github.com/affaan-m/everything-claude-code/blob/main/.opencode/README.md)
+  Why it matters: authoritative reference on `OpenCode Support` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Testing, Verification, and Troubleshooting](07-testing-verification-and-troubleshooting.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fabric-tutorial/01-getting-started.md b/tutorials/fabric-tutorial/01-getting-started.md
index eccd0716..519d3e8e 100644
--- a/tutorials/fabric-tutorial/01-getting-started.md
+++ b/tutorials/fabric-tutorial/01-getting-started.md
@@ -517,3 +517,54 @@ Ready to explore patterns? Let's dive into [Chapter 2: Pattern System](02-patter
 5. Set up logging and monitoring
 
 *What type of task are you most excited to augment with Fabric?* 🤖
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `fabric`, `patterns`, `summarize` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Fabric` as an operating subsystem inside **Fabric Tutorial: Open-Source Framework for Augmenting Humans with AI**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `output`, `extract_wisdom`, `config` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Fabric` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `fabric`.
+2. **Input normalization**: shape incoming data so `patterns` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `summarize`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/danielmiessler/Fabric)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [Pattern Library](https://github.com/danielmiessler/fabric/tree/main/data/patterns)
+  Why it matters: authoritative reference on `Pattern Library` (github.com).
+- [Community Patterns](https://github.com/danielmiessler/Fabric#community-patterns)
+  Why it matters: authoritative reference on `Community Patterns` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `fabric` and `patterns` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Pattern System](02-pattern-system.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fabric-tutorial/02-pattern-system.md b/tutorials/fabric-tutorial/02-pattern-system.md
index a7dc52de..fe56e2b7 100644
--- a/tutorials/fabric-tutorial/02-pattern-system.md
+++ b/tutorials/fabric-tutorial/02-pattern-system.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Pattern System
 
+Welcome to **Chapter 2: Pattern System**. In this part of **Fabric Tutorial: Open-Source Framework for Augmenting Humans with AI**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Understand Fabric's modular pattern architecture for creating reusable AI-powered cognitive workflows.
 
 ## Overview
@@ -436,3 +439,55 @@ Now that you understand the pattern system, let's explore basic usage workflows
 **Ready for Chapter 3?** [Basic Usage](03-basic-usage.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `fabric`, `summarize`, `input` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Pattern System` as an operating subsystem inside **Fabric Tutorial: Open-Source Framework for Augmenting Humans with AI**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `content`, `patterns`, `summary` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Pattern System` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `fabric`.
+2. **Input normalization**: shape incoming data so `summarize` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `input`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/danielmiessler/Fabric)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [Pattern Library](https://github.com/danielmiessler/fabric/tree/main/data/patterns)
+  Why it matters: authoritative reference on `Pattern Library` (github.com).
+- [Community Patterns](https://github.com/danielmiessler/Fabric#community-patterns)
+  Why it matters: authoritative reference on `Community Patterns` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `fabric` and `summarize` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Fabric](01-getting-started.md)
+- [Next Chapter: Chapter 3: Basic Usage](03-basic-usage.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fabric-tutorial/03-basic-usage.md b/tutorials/fabric-tutorial/03-basic-usage.md
index f55ad0c7..00d969e7 100644
--- a/tutorials/fabric-tutorial/03-basic-usage.md
+++ b/tutorials/fabric-tutorial/03-basic-usage.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Basic Usage
 
+Welcome to **Chapter 3: Basic Usage**. In this part of **Fabric Tutorial: Open-Source Framework for Augmenting Humans with AI**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master core commands and workflows for everyday cognitive augmentation with Fabric.
 
 ## Overview
@@ -399,3 +402,55 @@ Ready to explore advanced pattern usage? Let's dive into Chapter 4.
 **Ready for Chapter 4?** [Advanced Patterns](04-advanced-patterns.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `fabric`, `summarize`, `input` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Basic Usage` as an operating subsystem inside **Fabric Tutorial: Open-Source Framework for Augmenting Humans with AI**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `model`, `article`, `file` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Basic Usage` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `fabric`.
+2. **Input normalization**: shape incoming data so `summarize` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `input`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/danielmiessler/Fabric)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [Pattern Library](https://github.com/danielmiessler/fabric/tree/main/data/patterns)
+  Why it matters: authoritative reference on `Pattern Library` (github.com).
+- [Community Patterns](https://github.com/danielmiessler/Fabric#community-patterns)
+  Why it matters: authoritative reference on `Community Patterns` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `fabric` and `summarize` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Pattern System](02-pattern-system.md)
+- [Next Chapter: Chapter 4: Advanced Patterns](04-advanced-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fabric-tutorial/04-advanced-patterns.md b/tutorials/fabric-tutorial/04-advanced-patterns.md
index 2761dbf0..43c4673b 100644
--- a/tutorials/fabric-tutorial/04-advanced-patterns.md
+++ b/tutorials/fabric-tutorial/04-advanced-patterns.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Advanced Patterns
 
+Welcome to **Chapter 4: Advanced Patterns**. In this part of **Fabric Tutorial: Open-Source Framework for Augmenting Humans with AI**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master sophisticated pattern techniques for complex cognitive tasks and specialized domains.
 
 ## Overview
@@ -490,3 +493,55 @@ Ready to learn about composing complex workflows with Stitches? Let's explore Ch
 **Ready for Chapter 5?** [Stitch Composition](05-stitch-composition.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `fabric`, `input`, `Pattern` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Advanced Patterns` as an operating subsystem inside **Fabric Tutorial: Open-Source Framework for Augmenting Humans with AI**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `ANALYSIS`, `content`, `IDENTITY` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Advanced Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `fabric`.
+2. **Input normalization**: shape incoming data so `input` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Pattern`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/danielmiessler/Fabric)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [Pattern Library](https://github.com/danielmiessler/fabric/tree/main/data/patterns)
+  Why it matters: authoritative reference on `Pattern Library` (github.com).
+- [Community Patterns](https://github.com/danielmiessler/Fabric#community-patterns)
+  Why it matters: authoritative reference on `Community Patterns` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `fabric` and `input` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Basic Usage](03-basic-usage.md)
+- [Next Chapter: Chapter 5: Stitch Composition](05-stitch-composition.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fabric-tutorial/05-stitch-composition.md b/tutorials/fabric-tutorial/05-stitch-composition.md
index 2e11abc0..124ac9e3 100644
--- a/tutorials/fabric-tutorial/05-stitch-composition.md
+++ b/tutorials/fabric-tutorial/05-stitch-composition.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Stitch Composition
 
+Welcome to **Chapter 5: Stitch Composition**. In this part of **Fabric Tutorial: Open-Source Framework for Augmenting Humans with AI**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Create sophisticated AI workflows by composing patterns into reusable Stitches.
 
 ## Overview
@@ -486,3 +489,55 @@ Ready to create your own custom patterns? Let's dive into Chapter 6.
 **Ready for Chapter 6?** [Custom Patterns](06-custom-patterns.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `input`, `name`, `output` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Stitch Composition` as an operating subsystem inside **Fabric Tutorial: Open-Source Framework for Augmenting Humans with AI**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `pattern`, `steps`, `stitches` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Stitch Composition` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `input`.
+2. **Input normalization**: shape incoming data so `name` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `output`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/danielmiessler/Fabric)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [Pattern Library](https://github.com/danielmiessler/fabric/tree/main/data/patterns)
+  Why it matters: authoritative reference on `Pattern Library` (github.com).
+- [Community Patterns](https://github.com/danielmiessler/Fabric#community-patterns)
+  Why it matters: authoritative reference on `Community Patterns` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `input` and `name` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Advanced Patterns](04-advanced-patterns.md)
+- [Next Chapter: Chapter 6: Custom Patterns](06-custom-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fabric-tutorial/06-custom-patterns.md b/tutorials/fabric-tutorial/06-custom-patterns.md
index 4c2acd6d..8dce8fe4 100644
--- a/tutorials/fabric-tutorial/06-custom-patterns.md
+++ b/tutorials/fabric-tutorial/06-custom-patterns.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Custom Patterns
 
+Welcome to **Chapter 6: Custom Patterns**. In this part of **Fabric Tutorial: Open-Source Framework for Augmenting Humans with AI**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Design and implement custom patterns tailored to your specific cognitive tasks and domains.
 
 ## Overview
@@ -553,3 +556,55 @@ Ready to integrate Fabric with external systems via API? Let's explore Chapter 7
 **Ready for Chapter 7?** [Integration & API](07-integration-api.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Test`, `fabric`, `echo` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Custom Patterns` as an operating subsystem inside **Fabric Tutorial: Open-Source Framework for Augmenting Humans with AI**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `patterns`, `OUTPUT`, `content` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Custom Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Test`.
+2. **Input normalization**: shape incoming data so `fabric` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `echo`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/danielmiessler/Fabric)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [Pattern Library](https://github.com/danielmiessler/fabric/tree/main/data/patterns)
+  Why it matters: authoritative reference on `Pattern Library` (github.com).
+- [Community Patterns](https://github.com/danielmiessler/Fabric#community-patterns)
+  Why it matters: authoritative reference on `Community Patterns` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Test` and `fabric` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Stitch Composition](05-stitch-composition.md)
+- [Next Chapter: Chapter 7: Integration & API](07-integration-api.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fabric-tutorial/07-integration-api.md b/tutorials/fabric-tutorial/07-integration-api.md
index c1596147..dbbf1a11 100644
--- a/tutorials/fabric-tutorial/07-integration-api.md
+++ b/tutorials/fabric-tutorial/07-integration-api.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Integration & API
 
+Welcome to **Chapter 7: Integration & API**. In this part of **Fabric Tutorial: Open-Source Framework for Augmenting Humans with AI**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Integrate Fabric into applications, automate workflows, and build custom tools using Fabric's API.
 
 ## Overview
@@ -516,3 +519,55 @@ Ready to deploy Fabric for enterprise use? Let's explore Chapter 8.
 **Ready for Chapter 8?** [Enterprise Deployment](08-enterprise-deployment.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `pattern`, `fabric`, `input` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Integration & API` as an operating subsystem inside **Fabric Tutorial: Open-Source Framework for Augmenting Humans with AI**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Fabric`, `result`, `content` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Integration & API` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `pattern`.
+2. **Input normalization**: shape incoming data so `fabric` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `input`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/danielmiessler/Fabric)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [Pattern Library](https://github.com/danielmiessler/fabric/tree/main/data/patterns)
+  Why it matters: authoritative reference on `Pattern Library` (github.com).
+- [Community Patterns](https://github.com/danielmiessler/Fabric#community-patterns)
+  Why it matters: authoritative reference on `Community Patterns` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `pattern` and `fabric` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Custom Patterns](06-custom-patterns.md)
+- [Next Chapter: Chapter 8: Enterprise Deployment](08-enterprise-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fabric-tutorial/08-enterprise-deployment.md b/tutorials/fabric-tutorial/08-enterprise-deployment.md
index 3c4c1a47..a0efb412 100644
--- a/tutorials/fabric-tutorial/08-enterprise-deployment.md
+++ b/tutorials/fabric-tutorial/08-enterprise-deployment.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Enterprise Deployment
 
+Welcome to **Chapter 8: Enterprise Deployment**. In this part of **Fabric Tutorial: Open-Source Framework for Augmenting Humans with AI**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploy Fabric at scale with security, compliance, and team collaboration features.
 
 ## Overview
@@ -618,3 +621,54 @@ Congratulations! You've completed the Fabric tutorial. You now have the knowledg
 ---
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `fabric`, `patterns`, `self` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Enterprise Deployment` as an operating subsystem inside **Fabric Tutorial: Open-Source Framework for Augmenting Humans with AI**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `pattern`, `name`, `model` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Enterprise Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `fabric`.
+2. **Input normalization**: shape incoming data so `patterns` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `self`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub Repository](https://github.com/danielmiessler/Fabric)
+  Why it matters: authoritative reference on `GitHub Repository` (github.com).
+- [Pattern Library](https://github.com/danielmiessler/fabric/tree/main/data/patterns)
+  Why it matters: authoritative reference on `Pattern Library` (github.com).
+- [Community Patterns](https://github.com/danielmiessler/Fabric#community-patterns)
+  Why it matters: authoritative reference on `Community Patterns` (github.com).
+- [AI Codebase Knowledge Builder](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `AI Codebase Knowledge Builder` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `fabric` and `patterns` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Integration & API](07-integration-api.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fastmcp-tutorial/01-getting-started.md b/tutorials/fastmcp-tutorial/01-getting-started.md
index 7b5cc61d..85aba7e6 100644
--- a/tutorials/fastmcp-tutorial/01-getting-started.md
+++ b/tutorials/fastmcp-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: FastMCP Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gives you a quick path from installation to a working FastMCP server and first client call.
 
 ## Learning Goals
@@ -34,3 +37,606 @@ This chapter gives you a quick path from installation to a working FastMCP serve
 You now have a reliable baseline for expanding FastMCP servers beyond toy examples.
 
 Next: [Chapter 2: Core Abstractions: Components, Providers, Transforms](02-core-abstractions-components-providers-transforms.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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- tutorial slug: **fastmcp-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Fastmcp 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
+
+- [FastMCP Repository](https://github.com/jlowin/fastmcp)
+- [README](https://github.com/jlowin/fastmcp/blob/main/README.md)
+- [Installation Guide](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/installation.mdx)
+- [Quickstart](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/quickstart.mdx)
+- [Running Your Server](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/running-server.mdx)
+- [Client Guide](https://github.com/jlowin/fastmcp/blob/main/docs/clients/client.mdx)
+- [Project Configuration](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/server-configuration.mdx)
+- [Contributing](https://github.com/jlowin/fastmcp/blob/main/docs/development/contributing.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Awesome MCP Servers Tutorial](../awesome-mcp-servers-tutorial/)
+- [Composio Tutorial](../composio-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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [FastMCP Repository](https://github.com/jlowin/fastmcp)
+  Why it matters: authoritative reference on `FastMCP Repository` (github.com).
+- [README](https://github.com/jlowin/fastmcp/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Installation Guide](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/installation.mdx)
+  Why it matters: authoritative reference on `Installation Guide` (github.com).
+- [Quickstart](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/quickstart.mdx)
+  Why it matters: authoritative reference on `Quickstart` (github.com).
+- [Running Your Server](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/running-server.mdx)
+  Why it matters: authoritative reference on `Running Your Server` (github.com).
+- [Client Guide](https://github.com/jlowin/fastmcp/blob/main/docs/clients/client.mdx)
+  Why it matters: authoritative reference on `Client Guide` (github.com).
+- [Project Configuration](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/server-configuration.mdx)
+  Why it matters: authoritative reference on `Project Configuration` (github.com).
+- [Contributing](https://github.com/jlowin/fastmcp/blob/main/docs/development/contributing.mdx)
+  Why it matters: authoritative reference on `Contributing` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Core Abstractions: Components, Providers, Transforms](02-core-abstractions-components-providers-transforms.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fastmcp-tutorial/02-core-abstractions-components-providers-transforms.md b/tutorials/fastmcp-tutorial/02-core-abstractions-components-providers-transforms.md
index c85afa8e..4d7e659b 100644
--- a/tutorials/fastmcp-tutorial/02-core-abstractions-components-providers-transforms.md
+++ b/tutorials/fastmcp-tutorial/02-core-abstractions-components-providers-transforms.md
@@ -7,6 +7,9 @@ parent: FastMCP Tutorial
 
 # Chapter 2: Core Abstractions: Components, Providers, Transforms
 
+Welcome to **Chapter 2: Core Abstractions: Components, Providers, Transforms**. In this part of **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains FastMCP's core abstraction model and how to use it to keep systems composable.
 
 ## Learning Goals
@@ -38,3 +41,607 @@ Treat each component set as a product surface with explicit ownership, versionin
 You now have a design vocabulary for building maintainable FastMCP surfaces.
 
 Next: [Chapter 3: Server Runtime and Transports](03-server-runtime-and-transports.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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- tutorial slug: **fastmcp-tutorial**
+- chapter focus: **Chapter 2: Core Abstractions: Components, Providers, Transforms**
+- system context: **Fastmcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Core Abstractions: Components, Providers, Transforms`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [FastMCP Repository](https://github.com/jlowin/fastmcp)
+- [README](https://github.com/jlowin/fastmcp/blob/main/README.md)
+- [Installation Guide](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/installation.mdx)
+- [Quickstart](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/quickstart.mdx)
+- [Running Your Server](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/running-server.mdx)
+- [Client Guide](https://github.com/jlowin/fastmcp/blob/main/docs/clients/client.mdx)
+- [Project Configuration](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/server-configuration.mdx)
+- [Contributing](https://github.com/jlowin/fastmcp/blob/main/docs/development/contributing.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Awesome MCP Servers Tutorial](../awesome-mcp-servers-tutorial/)
+- [Composio Tutorial](../composio-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Core Abstractions: Components, Providers, Transforms`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Core Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Core Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Core Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Core Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Core Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Core Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Core Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Core Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Core Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Core Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Core Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Core Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Core Abstractions: Components, Providers, Transforms
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Core Abstractions: Components, Providers, Transforms` as an operating subsystem inside **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Core Abstractions: Components, Providers, Transforms` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [FastMCP Repository](https://github.com/jlowin/fastmcp)
+  Why it matters: authoritative reference on `FastMCP Repository` (github.com).
+- [README](https://github.com/jlowin/fastmcp/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Installation Guide](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/installation.mdx)
+  Why it matters: authoritative reference on `Installation Guide` (github.com).
+- [Quickstart](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/quickstart.mdx)
+  Why it matters: authoritative reference on `Quickstart` (github.com).
+- [Running Your Server](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/running-server.mdx)
+  Why it matters: authoritative reference on `Running Your Server` (github.com).
+- [Client Guide](https://github.com/jlowin/fastmcp/blob/main/docs/clients/client.mdx)
+  Why it matters: authoritative reference on `Client Guide` (github.com).
+- [Project Configuration](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/server-configuration.mdx)
+  Why it matters: authoritative reference on `Project Configuration` (github.com).
+- [Contributing](https://github.com/jlowin/fastmcp/blob/main/docs/development/contributing.mdx)
+  Why it matters: authoritative reference on `Contributing` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Server Runtime and Transports](03-server-runtime-and-transports.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fastmcp-tutorial/03-server-runtime-and-transports.md b/tutorials/fastmcp-tutorial/03-server-runtime-and-transports.md
index ce71e1e8..427fc7ca 100644
--- a/tutorials/fastmcp-tutorial/03-server-runtime-and-transports.md
+++ b/tutorials/fastmcp-tutorial/03-server-runtime-and-transports.md
@@ -7,6 +7,9 @@ parent: FastMCP Tutorial
 
 # Chapter 3: Server Runtime and Transports
 
+Welcome to **Chapter 3: Server Runtime and Transports**. In this part of **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers runtime behavior and transport selection across local and networked contexts.
 
 ## Learning Goals
@@ -40,3 +43,607 @@ This chapter covers runtime behavior and transport selection across local and ne
 You now have a transport selection framework that aligns with operational reality.
 
 Next: [Chapter 4: Client Architecture and Transport Patterns](04-client-architecture-and-transport-patterns.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- tutorial slug: **fastmcp-tutorial**
+- chapter focus: **Chapter 3: Server Runtime and Transports**
+- system context: **Fastmcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Server Runtime and Transports`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [FastMCP Repository](https://github.com/jlowin/fastmcp)
+- [README](https://github.com/jlowin/fastmcp/blob/main/README.md)
+- [Installation Guide](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/installation.mdx)
+- [Quickstart](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/quickstart.mdx)
+- [Running Your Server](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/running-server.mdx)
+- [Client Guide](https://github.com/jlowin/fastmcp/blob/main/docs/clients/client.mdx)
+- [Project Configuration](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/server-configuration.mdx)
+- [Contributing](https://github.com/jlowin/fastmcp/blob/main/docs/development/contributing.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Awesome MCP Servers Tutorial](../awesome-mcp-servers-tutorial/)
+- [Composio Tutorial](../composio-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Server Runtime and Transports`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Server Runtime and Transports
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Server Runtime and Transports` as an operating subsystem inside **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Server Runtime and Transports` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [FastMCP Repository](https://github.com/jlowin/fastmcp)
+  Why it matters: authoritative reference on `FastMCP Repository` (github.com).
+- [README](https://github.com/jlowin/fastmcp/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Installation Guide](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/installation.mdx)
+  Why it matters: authoritative reference on `Installation Guide` (github.com).
+- [Quickstart](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/quickstart.mdx)
+  Why it matters: authoritative reference on `Quickstart` (github.com).
+- [Running Your Server](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/running-server.mdx)
+  Why it matters: authoritative reference on `Running Your Server` (github.com).
+- [Client Guide](https://github.com/jlowin/fastmcp/blob/main/docs/clients/client.mdx)
+  Why it matters: authoritative reference on `Client Guide` (github.com).
+- [Project Configuration](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/server-configuration.mdx)
+  Why it matters: authoritative reference on `Project Configuration` (github.com).
+- [Contributing](https://github.com/jlowin/fastmcp/blob/main/docs/development/contributing.mdx)
+  Why it matters: authoritative reference on `Contributing` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Core Abstractions: Components, Providers, Transforms](02-core-abstractions-components-providers-transforms.md)
+- [Next Chapter: Chapter 4: Client Architecture and Transport Patterns](04-client-architecture-and-transport-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fastmcp-tutorial/04-client-architecture-and-transport-patterns.md b/tutorials/fastmcp-tutorial/04-client-architecture-and-transport-patterns.md
index 1a9d6740..c7a87f99 100644
--- a/tutorials/fastmcp-tutorial/04-client-architecture-and-transport-patterns.md
+++ b/tutorials/fastmcp-tutorial/04-client-architecture-and-transport-patterns.md
@@ -7,6 +7,9 @@ parent: FastMCP Tutorial
 
 # Chapter 4: Client Architecture and Transport Patterns
 
+Welcome to **Chapter 4: Client Architecture and Transport Patterns**. In this part of **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on client-side design patterns for reliable server communication.
 
 ## Learning Goals
@@ -35,3 +38,607 @@ This chapter focuses on client-side design patterns for reliable server communic
 You now have a client architecture baseline for robust FastMCP integrations.
 
 Next: [Chapter 5: Integrations: Claude Code, Cursor, and Tooling](05-integrations-claude-code-cursor-and-tooling.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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- tutorial slug: **fastmcp-tutorial**
+- chapter focus: **Chapter 4: Client Architecture and Transport Patterns**
+- system context: **Fastmcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Client Architecture and Transport 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
+
+- [FastMCP Repository](https://github.com/jlowin/fastmcp)
+- [README](https://github.com/jlowin/fastmcp/blob/main/README.md)
+- [Installation Guide](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/installation.mdx)
+- [Quickstart](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/quickstart.mdx)
+- [Running Your Server](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/running-server.mdx)
+- [Client Guide](https://github.com/jlowin/fastmcp/blob/main/docs/clients/client.mdx)
+- [Project Configuration](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/server-configuration.mdx)
+- [Contributing](https://github.com/jlowin/fastmcp/blob/main/docs/development/contributing.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Awesome MCP Servers Tutorial](../awesome-mcp-servers-tutorial/)
+- [Composio Tutorial](../composio-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Client Architecture and Transport Patterns`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Client Architecture and Transport Patterns
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Client Architecture and Transport Patterns` as an operating subsystem inside **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Client Architecture and Transport Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [FastMCP Repository](https://github.com/jlowin/fastmcp)
+  Why it matters: authoritative reference on `FastMCP Repository` (github.com).
+- [README](https://github.com/jlowin/fastmcp/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Installation Guide](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/installation.mdx)
+  Why it matters: authoritative reference on `Installation Guide` (github.com).
+- [Quickstart](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/quickstart.mdx)
+  Why it matters: authoritative reference on `Quickstart` (github.com).
+- [Running Your Server](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/running-server.mdx)
+  Why it matters: authoritative reference on `Running Your Server` (github.com).
+- [Client Guide](https://github.com/jlowin/fastmcp/blob/main/docs/clients/client.mdx)
+  Why it matters: authoritative reference on `Client Guide` (github.com).
+- [Project Configuration](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/server-configuration.mdx)
+  Why it matters: authoritative reference on `Project Configuration` (github.com).
+- [Contributing](https://github.com/jlowin/fastmcp/blob/main/docs/development/contributing.mdx)
+  Why it matters: authoritative reference on `Contributing` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Server Runtime and Transports](03-server-runtime-and-transports.md)
+- [Next Chapter: Chapter 5: Integrations: Claude Code, Cursor, and Tooling](05-integrations-claude-code-cursor-and-tooling.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fastmcp-tutorial/05-integrations-claude-code-cursor-and-tooling.md b/tutorials/fastmcp-tutorial/05-integrations-claude-code-cursor-and-tooling.md
index ad1b2950..d1399825 100644
--- a/tutorials/fastmcp-tutorial/05-integrations-claude-code-cursor-and-tooling.md
+++ b/tutorials/fastmcp-tutorial/05-integrations-claude-code-cursor-and-tooling.md
@@ -7,6 +7,9 @@ parent: FastMCP Tutorial
 
 # Chapter 5: Integrations: Claude Code, Cursor, and Tooling
 
+Welcome to **Chapter 5: Integrations: Claude Code, Cursor, and Tooling**. In this part of **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains host integration workflows for coding assistants and local IDE tooling.
 
 ## Learning Goals
@@ -34,3 +37,607 @@ This chapter explains host integration workflows for coding assistants and local
 You now have practical host integration patterns for daily coding workflows.
 
 Next: [Chapter 6: Configuration, Auth, and Deployment](06-configuration-auth-and-deployment.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- tutorial slug: **fastmcp-tutorial**
+- chapter focus: **Chapter 5: Integrations: Claude Code, Cursor, and Tooling**
+- system context: **Fastmcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Integrations: Claude Code, Cursor, and Tooling`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [FastMCP Repository](https://github.com/jlowin/fastmcp)
+- [README](https://github.com/jlowin/fastmcp/blob/main/README.md)
+- [Installation Guide](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/installation.mdx)
+- [Quickstart](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/quickstart.mdx)
+- [Running Your Server](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/running-server.mdx)
+- [Client Guide](https://github.com/jlowin/fastmcp/blob/main/docs/clients/client.mdx)
+- [Project Configuration](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/server-configuration.mdx)
+- [Contributing](https://github.com/jlowin/fastmcp/blob/main/docs/development/contributing.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Awesome MCP Servers Tutorial](../awesome-mcp-servers-tutorial/)
+- [Composio Tutorial](../composio-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Integrations: Claude Code, Cursor, and Tooling`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Integrations: Claude Code, Cursor, and Tooling
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Integrations: Claude Code, Cursor, and Tooling` as an operating subsystem inside **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Integrations: Claude Code, Cursor, and Tooling` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [FastMCP Repository](https://github.com/jlowin/fastmcp)
+  Why it matters: authoritative reference on `FastMCP Repository` (github.com).
+- [README](https://github.com/jlowin/fastmcp/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Installation Guide](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/installation.mdx)
+  Why it matters: authoritative reference on `Installation Guide` (github.com).
+- [Quickstart](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/quickstart.mdx)
+  Why it matters: authoritative reference on `Quickstart` (github.com).
+- [Running Your Server](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/running-server.mdx)
+  Why it matters: authoritative reference on `Running Your Server` (github.com).
+- [Client Guide](https://github.com/jlowin/fastmcp/blob/main/docs/clients/client.mdx)
+  Why it matters: authoritative reference on `Client Guide` (github.com).
+- [Project Configuration](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/server-configuration.mdx)
+  Why it matters: authoritative reference on `Project Configuration` (github.com).
+- [Contributing](https://github.com/jlowin/fastmcp/blob/main/docs/development/contributing.mdx)
+  Why it matters: authoritative reference on `Contributing` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Client Architecture and Transport Patterns](04-client-architecture-and-transport-patterns.md)
+- [Next Chapter: Chapter 6: Configuration, Auth, and Deployment](06-configuration-auth-and-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fastmcp-tutorial/06-configuration-auth-and-deployment.md b/tutorials/fastmcp-tutorial/06-configuration-auth-and-deployment.md
index 4f9fb64e..56fc14be 100644
--- a/tutorials/fastmcp-tutorial/06-configuration-auth-and-deployment.md
+++ b/tutorials/fastmcp-tutorial/06-configuration-auth-and-deployment.md
@@ -7,6 +7,9 @@ parent: FastMCP Tutorial
 
 # Chapter 6: Configuration, Auth, and Deployment
 
+Welcome to **Chapter 6: Configuration, Auth, and Deployment**. In this part of **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers standardized project configuration, auth controls, and deployment choices.
 
 ## Learning Goals
@@ -34,3 +37,607 @@ This chapter covers standardized project configuration, auth controls, and deplo
 You now have a deployment-ready configuration and auth approach for FastMCP systems.
 
 Next: [Chapter 7: Testing, Contributing, and Upgrade Strategy](07-testing-contributing-and-upgrade-strategy.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- tutorial slug: **fastmcp-tutorial**
+- chapter focus: **Chapter 6: Configuration, Auth, and Deployment**
+- system context: **Fastmcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Configuration, Auth, and 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
+
+- [FastMCP Repository](https://github.com/jlowin/fastmcp)
+- [README](https://github.com/jlowin/fastmcp/blob/main/README.md)
+- [Installation Guide](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/installation.mdx)
+- [Quickstart](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/quickstart.mdx)
+- [Running Your Server](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/running-server.mdx)
+- [Client Guide](https://github.com/jlowin/fastmcp/blob/main/docs/clients/client.mdx)
+- [Project Configuration](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/server-configuration.mdx)
+- [Contributing](https://github.com/jlowin/fastmcp/blob/main/docs/development/contributing.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Awesome MCP Servers Tutorial](../awesome-mcp-servers-tutorial/)
+- [Composio Tutorial](../composio-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Configuration, Auth, and Deployment`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Configuration, Auth, and Deployment
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Configuration, Auth, and Deployment` as an operating subsystem inside **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Configuration, Auth, and Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [FastMCP Repository](https://github.com/jlowin/fastmcp)
+  Why it matters: authoritative reference on `FastMCP Repository` (github.com).
+- [README](https://github.com/jlowin/fastmcp/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Installation Guide](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/installation.mdx)
+  Why it matters: authoritative reference on `Installation Guide` (github.com).
+- [Quickstart](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/quickstart.mdx)
+  Why it matters: authoritative reference on `Quickstart` (github.com).
+- [Running Your Server](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/running-server.mdx)
+  Why it matters: authoritative reference on `Running Your Server` (github.com).
+- [Client Guide](https://github.com/jlowin/fastmcp/blob/main/docs/clients/client.mdx)
+  Why it matters: authoritative reference on `Client Guide` (github.com).
+- [Project Configuration](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/server-configuration.mdx)
+  Why it matters: authoritative reference on `Project Configuration` (github.com).
+- [Contributing](https://github.com/jlowin/fastmcp/blob/main/docs/development/contributing.mdx)
+  Why it matters: authoritative reference on `Contributing` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Integrations: Claude Code, Cursor, and Tooling](05-integrations-claude-code-cursor-and-tooling.md)
+- [Next Chapter: Chapter 7: Testing, Contributing, and Upgrade Strategy](07-testing-contributing-and-upgrade-strategy.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fastmcp-tutorial/07-testing-contributing-and-upgrade-strategy.md b/tutorials/fastmcp-tutorial/07-testing-contributing-and-upgrade-strategy.md
index 0eda5e43..923ffd53 100644
--- a/tutorials/fastmcp-tutorial/07-testing-contributing-and-upgrade-strategy.md
+++ b/tutorials/fastmcp-tutorial/07-testing-contributing-and-upgrade-strategy.md
@@ -7,6 +7,9 @@ parent: FastMCP Tutorial
 
 # Chapter 7: Testing, Contributing, and Upgrade Strategy
 
+Welcome to **Chapter 7: Testing, Contributing, and Upgrade Strategy**. In this part of **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers change safety: test strategy, contributor workflow, and version migration.
 
 ## Learning Goals
@@ -36,3 +39,607 @@ This chapter covers change safety: test strategy, contributor workflow, and vers
 You now have a safer maintenance model for evolving FastMCP server/client systems.
 
 Next: [Chapter 8: Production Operations and Governance](08-production-operations-and-governance.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- tutorial slug: **fastmcp-tutorial**
+- chapter focus: **Chapter 7: Testing, Contributing, and Upgrade Strategy**
+- system context: **Fastmcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Testing, Contributing, and Upgrade Strategy`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [FastMCP Repository](https://github.com/jlowin/fastmcp)
+- [README](https://github.com/jlowin/fastmcp/blob/main/README.md)
+- [Installation Guide](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/installation.mdx)
+- [Quickstart](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/quickstart.mdx)
+- [Running Your Server](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/running-server.mdx)
+- [Client Guide](https://github.com/jlowin/fastmcp/blob/main/docs/clients/client.mdx)
+- [Project Configuration](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/server-configuration.mdx)
+- [Contributing](https://github.com/jlowin/fastmcp/blob/main/docs/development/contributing.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Awesome MCP Servers Tutorial](../awesome-mcp-servers-tutorial/)
+- [Composio Tutorial](../composio-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Testing, Contributing, and Upgrade Strategy`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Testing, Contributing, and Upgrade Strategy
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Testing, Contributing, and Upgrade Strategy` as an operating subsystem inside **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Testing, Contributing, and Upgrade Strategy` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [FastMCP Repository](https://github.com/jlowin/fastmcp)
+  Why it matters: authoritative reference on `FastMCP Repository` (github.com).
+- [README](https://github.com/jlowin/fastmcp/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Installation Guide](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/installation.mdx)
+  Why it matters: authoritative reference on `Installation Guide` (github.com).
+- [Quickstart](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/quickstart.mdx)
+  Why it matters: authoritative reference on `Quickstart` (github.com).
+- [Running Your Server](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/running-server.mdx)
+  Why it matters: authoritative reference on `Running Your Server` (github.com).
+- [Client Guide](https://github.com/jlowin/fastmcp/blob/main/docs/clients/client.mdx)
+  Why it matters: authoritative reference on `Client Guide` (github.com).
+- [Project Configuration](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/server-configuration.mdx)
+  Why it matters: authoritative reference on `Project Configuration` (github.com).
+- [Contributing](https://github.com/jlowin/fastmcp/blob/main/docs/development/contributing.mdx)
+  Why it matters: authoritative reference on `Contributing` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Configuration, Auth, and Deployment](06-configuration-auth-and-deployment.md)
+- [Next Chapter: Chapter 8: Production Operations and Governance](08-production-operations-and-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fastmcp-tutorial/08-production-operations-and-governance.md b/tutorials/fastmcp-tutorial/08-production-operations-and-governance.md
index 268c1e49..948662a5 100644
--- a/tutorials/fastmcp-tutorial/08-production-operations-and-governance.md
+++ b/tutorials/fastmcp-tutorial/08-production-operations-and-governance.md
@@ -7,6 +7,9 @@ parent: FastMCP Tutorial
 
 # Chapter 8: Production Operations and Governance
 
+Welcome to **Chapter 8: Production Operations and Governance**. In this part of **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter consolidates day-2 operations, governance, and reliability practices for team-scale FastMCP deployments.
 
 ## Learning Goals
@@ -32,3 +35,606 @@ This chapter consolidates day-2 operations, governance, and reliability practice
 ## Summary
 
 You now have an end-to-end framework for designing, integrating, and operating FastMCP systems in production.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- tutorial slug: **fastmcp-tutorial**
+- chapter focus: **Chapter 8: Production Operations and Governance**
+- system context: **Fastmcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Operations and Governance`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [FastMCP Repository](https://github.com/jlowin/fastmcp)
+- [README](https://github.com/jlowin/fastmcp/blob/main/README.md)
+- [Installation Guide](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/installation.mdx)
+- [Quickstart](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/quickstart.mdx)
+- [Running Your Server](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/running-server.mdx)
+- [Client Guide](https://github.com/jlowin/fastmcp/blob/main/docs/clients/client.mdx)
+- [Project Configuration](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/server-configuration.mdx)
+- [Contributing](https://github.com/jlowin/fastmcp/blob/main/docs/development/contributing.mdx)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Awesome MCP Servers Tutorial](../awesome-mcp-servers-tutorial/)
+- [Composio Tutorial](../composio-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Operations and Governance`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Production Operations and Governance
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Production Operations and Governance
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Production Operations and Governance
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Production Operations and Governance
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Production Operations and Governance
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Operations and Governance
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Operations and Governance
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Production Operations and Governance
+
+- tutorial context: **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Operations and Governance` as an operating subsystem inside **FastMCP Tutorial: Building and Operating MCP Servers with Pythonic Control**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Operations and Governance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [FastMCP Repository](https://github.com/jlowin/fastmcp)
+  Why it matters: authoritative reference on `FastMCP Repository` (github.com).
+- [README](https://github.com/jlowin/fastmcp/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Installation Guide](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/installation.mdx)
+  Why it matters: authoritative reference on `Installation Guide` (github.com).
+- [Quickstart](https://github.com/jlowin/fastmcp/blob/main/docs/getting-started/quickstart.mdx)
+  Why it matters: authoritative reference on `Quickstart` (github.com).
+- [Running Your Server](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/running-server.mdx)
+  Why it matters: authoritative reference on `Running Your Server` (github.com).
+- [Client Guide](https://github.com/jlowin/fastmcp/blob/main/docs/clients/client.mdx)
+  Why it matters: authoritative reference on `Client Guide` (github.com).
+- [Project Configuration](https://github.com/jlowin/fastmcp/blob/main/docs/deployment/server-configuration.mdx)
+  Why it matters: authoritative reference on `Project Configuration` (github.com).
+- [Contributing](https://github.com/jlowin/fastmcp/blob/main/docs/development/contributing.mdx)
+  Why it matters: authoritative reference on `Contributing` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Testing, Contributing, and Upgrade Strategy](07-testing-contributing-and-upgrade-strategy.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/figma-context-mcp-tutorial/01-getting-started.md b/tutorials/figma-context-mcp-tutorial/01-getting-started.md
index bbccd4d7..b7e4b957 100644
--- a/tutorials/figma-context-mcp-tutorial/01-getting-started.md
+++ b/tutorials/figma-context-mcp-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Figma Context MCP Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets Figma Context MCP connected to your coding client with a working token and first design fetch.
 
 ## Learning Goals
@@ -39,3 +42,598 @@ This chapter gets Figma Context MCP connected to your coding client with a worki
 You now have a working MCP bridge between Figma and your coding assistant.
 
 Next: [Chapter 2: Architecture and Context Translation](02-architecture-and-context-translation.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: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- tutorial slug: **figma-context-mcp-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Figma Context Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Figma Context MCP Repository](https://github.com/GLips/Figma-Context-MCP)
+- [Figma Context MCP Releases](https://github.com/GLips/Figma-Context-MCP/releases)
+- [Framelink Quickstart](https://www.framelink.ai/docs/quickstart)
+- [Model Context Protocol Intro](https://modelcontextprotocol.io/introduction)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `figma`, `mcpServers`, `Framelink` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Figma`, `command`, `args` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `figma`.
+2. **Input normalization**: shape incoming data so `mcpServers` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Framelink`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Figma Context MCP Repository](https://github.com/GLips/Figma-Context-MCP)
+  Why it matters: authoritative reference on `Figma Context MCP Repository` (github.com).
+- [Figma Context MCP Releases](https://github.com/GLips/Figma-Context-MCP/releases)
+  Why it matters: authoritative reference on `Figma Context MCP Releases` (github.com).
+- [Framelink Quickstart](https://www.framelink.ai/docs/quickstart)
+  Why it matters: authoritative reference on `Framelink Quickstart` (www.framelink.ai).
+- [Model Context Protocol Intro](https://modelcontextprotocol.io/introduction)
+  Why it matters: authoritative reference on `Model Context Protocol Intro` (modelcontextprotocol.io).
+
+Suggested trace strategy:
+- search upstream code for `figma` and `mcpServers` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Architecture and Context Translation](02-architecture-and-context-translation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/figma-context-mcp-tutorial/02-architecture-and-context-translation.md b/tutorials/figma-context-mcp-tutorial/02-architecture-and-context-translation.md
index 4c960e69..8367e95f 100644
--- a/tutorials/figma-context-mcp-tutorial/02-architecture-and-context-translation.md
+++ b/tutorials/figma-context-mcp-tutorial/02-architecture-and-context-translation.md
@@ -7,6 +7,9 @@ parent: Figma Context MCP Tutorial
 
 # Chapter 2: Architecture and Context Translation
 
+Welcome to **Chapter 2: Architecture and Context Translation**. In this part of **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 The server's core value is context translation: raw Figma API responses are simplified before being sent to the model.
 
 ## Translation Pipeline
@@ -33,3 +36,611 @@ flowchart LR
 You now understand the transformation layer that makes MCP design context effective for coding agents.
 
 Next: [Chapter 3: Frame Targeting and Context Scope](03-frame-targeting-and-context-scope.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- tutorial slug: **figma-context-mcp-tutorial**
+- chapter focus: **Chapter 2: Architecture and Context Translation**
+- system context: **Figma Context Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Architecture and Context 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
+
+- [Figma Context MCP Repository](https://github.com/GLips/Figma-Context-MCP)
+- [Figma Context MCP Releases](https://github.com/GLips/Figma-Context-MCP/releases)
+- [Framelink Quickstart](https://www.framelink.ai/docs/quickstart)
+- [Model Context Protocol Intro](https://modelcontextprotocol.io/introduction)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Architecture and Context 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 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 2: Architecture and Context Translation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Context`, `flowchart`, `Figma` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Architecture and Context Translation` as an operating subsystem inside **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Simplification`, `Relevant`, `Layout` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Architecture and Context Translation` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Context`.
+2. **Input normalization**: shape incoming data so `flowchart` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Figma`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Figma Context MCP Repository](https://github.com/GLips/Figma-Context-MCP)
+  Why it matters: authoritative reference on `Figma Context MCP Repository` (github.com).
+- [Figma Context MCP Releases](https://github.com/GLips/Figma-Context-MCP/releases)
+  Why it matters: authoritative reference on `Figma Context MCP Releases` (github.com).
+- [Framelink Quickstart](https://www.framelink.ai/docs/quickstart)
+  Why it matters: authoritative reference on `Framelink Quickstart` (www.framelink.ai).
+- [Model Context Protocol Intro](https://modelcontextprotocol.io/introduction)
+  Why it matters: authoritative reference on `Model Context Protocol Intro` (modelcontextprotocol.io).
+
+Suggested trace strategy:
+- search upstream code for `Context` and `flowchart` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Frame Targeting and Context Scope](03-frame-targeting-and-context-scope.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/figma-context-mcp-tutorial/03-frame-targeting-and-context-scope.md b/tutorials/figma-context-mcp-tutorial/03-frame-targeting-and-context-scope.md
index f33c7545..24b214eb 100644
--- a/tutorials/figma-context-mcp-tutorial/03-frame-targeting-and-context-scope.md
+++ b/tutorials/figma-context-mcp-tutorial/03-frame-targeting-and-context-scope.md
@@ -7,6 +7,9 @@ parent: Figma Context MCP Tutorial
 
 # Chapter 3: Frame Targeting and Context Scope
 
+Welcome to **Chapter 3: Frame Targeting and Context Scope**. In this part of **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Accurate implementation depends on precise frame selection and scoped context extraction.
 
 ## Scope Rules
@@ -22,3 +25,607 @@ Accurate implementation depends on precise frame selection and scoped context ex
 You now have practical scoping techniques that improve one-shot implementation quality.
 
 Next: [Chapter 4: Prompt Patterns for One-Shot UI Implementation](04-prompt-patterns-for-one-shot-ui-implementation.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: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- tutorial slug: **figma-context-mcp-tutorial**
+- chapter focus: **Chapter 3: Frame Targeting and Context Scope**
+- system context: **Figma Context Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Frame Targeting and Context Scope`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Figma Context MCP Repository](https://github.com/GLips/Figma-Context-MCP)
+- [Figma Context MCP Releases](https://github.com/GLips/Figma-Context-MCP/releases)
+- [Framelink Quickstart](https://www.framelink.ai/docs/quickstart)
+- [Model Context Protocol Intro](https://modelcontextprotocol.io/introduction)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Frame Targeting and Context Scope`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 3: Frame Targeting and Context Scope
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Frame Targeting and Context Scope` as an operating subsystem inside **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Frame Targeting and Context Scope` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Figma Context MCP Repository](https://github.com/GLips/Figma-Context-MCP)
+  Why it matters: authoritative reference on `Figma Context MCP Repository` (github.com).
+- [Figma Context MCP Releases](https://github.com/GLips/Figma-Context-MCP/releases)
+  Why it matters: authoritative reference on `Figma Context MCP Releases` (github.com).
+- [Framelink Quickstart](https://www.framelink.ai/docs/quickstart)
+  Why it matters: authoritative reference on `Framelink Quickstart` (www.framelink.ai).
+- [Model Context Protocol Intro](https://modelcontextprotocol.io/introduction)
+  Why it matters: authoritative reference on `Model Context Protocol Intro` (modelcontextprotocol.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Architecture and Context Translation](02-architecture-and-context-translation.md)
+- [Next Chapter: Chapter 4: Prompt Patterns for One-Shot UI Implementation](04-prompt-patterns-for-one-shot-ui-implementation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/figma-context-mcp-tutorial/04-prompt-patterns-for-one-shot-ui-implementation.md b/tutorials/figma-context-mcp-tutorial/04-prompt-patterns-for-one-shot-ui-implementation.md
index 76c81734..c8f8a5fd 100644
--- a/tutorials/figma-context-mcp-tutorial/04-prompt-patterns-for-one-shot-ui-implementation.md
+++ b/tutorials/figma-context-mcp-tutorial/04-prompt-patterns-for-one-shot-ui-implementation.md
@@ -7,6 +7,9 @@ parent: Figma Context MCP Tutorial
 
 # Chapter 4: Prompt Patterns for One-Shot UI Implementation
 
+Welcome to **Chapter 4: Prompt Patterns for One-Shot UI Implementation**. In this part of **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Prompt quality is the multiplier for design context quality.
 
 ## High-Value Prompt Structure
@@ -27,3 +30,607 @@ Prompt quality is the multiplier for design context quality.
 You now have prompt patterns that convert design context into higher-fidelity code output.
 
 Next: [Chapter 5: MCP Client Integrations](05-mcp-client-integrations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- tutorial slug: **figma-context-mcp-tutorial**
+- chapter focus: **Chapter 4: Prompt Patterns for One-Shot UI Implementation**
+- system context: **Figma Context Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Prompt Patterns for One-Shot UI Implementation`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Figma Context MCP Repository](https://github.com/GLips/Figma-Context-MCP)
+- [Figma Context MCP Releases](https://github.com/GLips/Figma-Context-MCP/releases)
+- [Framelink Quickstart](https://www.framelink.ai/docs/quickstart)
+- [Model Context Protocol Intro](https://modelcontextprotocol.io/introduction)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Prompt Patterns for One-Shot UI Implementation`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 4: Prompt Patterns for One-Shot UI Implementation
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Prompt Patterns for One-Shot UI Implementation` as an operating subsystem inside **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Prompt Patterns for One-Shot UI Implementation` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Figma Context MCP Repository](https://github.com/GLips/Figma-Context-MCP)
+  Why it matters: authoritative reference on `Figma Context MCP Repository` (github.com).
+- [Figma Context MCP Releases](https://github.com/GLips/Figma-Context-MCP/releases)
+  Why it matters: authoritative reference on `Figma Context MCP Releases` (github.com).
+- [Framelink Quickstart](https://www.framelink.ai/docs/quickstart)
+  Why it matters: authoritative reference on `Framelink Quickstart` (www.framelink.ai).
+- [Model Context Protocol Intro](https://modelcontextprotocol.io/introduction)
+  Why it matters: authoritative reference on `Model Context Protocol Intro` (modelcontextprotocol.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Frame Targeting and Context Scope](03-frame-targeting-and-context-scope.md)
+- [Next Chapter: Chapter 5: MCP Client Integrations](05-mcp-client-integrations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/figma-context-mcp-tutorial/05-mcp-client-integrations.md b/tutorials/figma-context-mcp-tutorial/05-mcp-client-integrations.md
index 21d8f3fe..ce46cced 100644
--- a/tutorials/figma-context-mcp-tutorial/05-mcp-client-integrations.md
+++ b/tutorials/figma-context-mcp-tutorial/05-mcp-client-integrations.md
@@ -7,6 +7,9 @@ parent: Figma Context MCP Tutorial
 
 # Chapter 5: MCP Client Integrations
 
+Welcome to **Chapter 5: MCP Client Integrations**. In this part of **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Figma Context MCP can be integrated into Cursor and other MCP-capable clients through standard configuration patterns.
 
 ## Integration Checklist
@@ -21,3 +24,619 @@ Figma Context MCP can be integrated into Cursor and other MCP-capable clients th
 You now know how to operationalize Figma Context MCP across coding-agent clients.
 
 Next: [Chapter 6: Performance and Token Optimization](06-performance-and-token-optimization.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: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- tutorial slug: **figma-context-mcp-tutorial**
+- chapter focus: **Chapter 5: MCP Client Integrations**
+- system context: **Figma Context Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: MCP Client Integrations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Figma Context MCP Repository](https://github.com/GLips/Figma-Context-MCP)
+- [Figma Context MCP Releases](https://github.com/GLips/Figma-Context-MCP/releases)
+- [Framelink Quickstart](https://www.framelink.ai/docs/quickstart)
+- [Model Context Protocol Intro](https://modelcontextprotocol.io/introduction)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: MCP Client Integrations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 5: MCP Client Integrations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: MCP Client Integrations` as an operating subsystem inside **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: MCP Client Integrations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Figma Context MCP Repository](https://github.com/GLips/Figma-Context-MCP)
+  Why it matters: authoritative reference on `Figma Context MCP Repository` (github.com).
+- [Figma Context MCP Releases](https://github.com/GLips/Figma-Context-MCP/releases)
+  Why it matters: authoritative reference on `Figma Context MCP Releases` (github.com).
+- [Framelink Quickstart](https://www.framelink.ai/docs/quickstart)
+  Why it matters: authoritative reference on `Framelink Quickstart` (www.framelink.ai).
+- [Model Context Protocol Intro](https://modelcontextprotocol.io/introduction)
+  Why it matters: authoritative reference on `Model Context Protocol Intro` (modelcontextprotocol.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Prompt Patterns for One-Shot UI Implementation](04-prompt-patterns-for-one-shot-ui-implementation.md)
+- [Next Chapter: Chapter 6: Performance and Token Optimization](06-performance-and-token-optimization.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/figma-context-mcp-tutorial/06-performance-and-token-optimization.md b/tutorials/figma-context-mcp-tutorial/06-performance-and-token-optimization.md
index b8ae4c83..fd805794 100644
--- a/tutorials/figma-context-mcp-tutorial/06-performance-and-token-optimization.md
+++ b/tutorials/figma-context-mcp-tutorial/06-performance-and-token-optimization.md
@@ -7,6 +7,9 @@ parent: Figma Context MCP Tutorial
 
 # Chapter 6: Performance and Token Optimization
 
+Welcome to **Chapter 6: Performance and Token Optimization**. In this part of **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Performance improves when context payloads are scoped and prompts are structured for deterministic outputs.
 
 ## Optimization Levers
@@ -22,3 +25,607 @@ Performance improves when context payloads are scoped and prompts are structured
 You now have token and latency controls for efficient design-to-code workflows.
 
 Next: [Chapter 7: Team Workflows and Design Governance](07-team-workflows-and-design-governance.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- tutorial slug: **figma-context-mcp-tutorial**
+- chapter focus: **Chapter 6: Performance and Token Optimization**
+- system context: **Figma Context Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Performance and Token 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
+
+- [Figma Context MCP Repository](https://github.com/GLips/Figma-Context-MCP)
+- [Figma Context MCP Releases](https://github.com/GLips/Figma-Context-MCP/releases)
+- [Framelink Quickstart](https://www.framelink.ai/docs/quickstart)
+- [Model Context Protocol Intro](https://modelcontextprotocol.io/introduction)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Performance and Token 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 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 6: Performance and Token Optimization
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Performance and Token Optimization` as an operating subsystem inside **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Performance and Token Optimization` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Figma Context MCP Repository](https://github.com/GLips/Figma-Context-MCP)
+  Why it matters: authoritative reference on `Figma Context MCP Repository` (github.com).
+- [Figma Context MCP Releases](https://github.com/GLips/Figma-Context-MCP/releases)
+  Why it matters: authoritative reference on `Figma Context MCP Releases` (github.com).
+- [Framelink Quickstart](https://www.framelink.ai/docs/quickstart)
+  Why it matters: authoritative reference on `Framelink Quickstart` (www.framelink.ai).
+- [Model Context Protocol Intro](https://modelcontextprotocol.io/introduction)
+  Why it matters: authoritative reference on `Model Context Protocol Intro` (modelcontextprotocol.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: MCP Client Integrations](05-mcp-client-integrations.md)
+- [Next Chapter: Chapter 7: Team Workflows and Design Governance](07-team-workflows-and-design-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/figma-context-mcp-tutorial/07-team-workflows-and-design-governance.md b/tutorials/figma-context-mcp-tutorial/07-team-workflows-and-design-governance.md
index e6ea9844..e7f4c685 100644
--- a/tutorials/figma-context-mcp-tutorial/07-team-workflows-and-design-governance.md
+++ b/tutorials/figma-context-mcp-tutorial/07-team-workflows-and-design-governance.md
@@ -7,6 +7,9 @@ parent: Figma Context MCP Tutorial
 
 # Chapter 7: Team Workflows and Design Governance
 
+Welcome to **Chapter 7: Team Workflows and Design Governance**. In this part of **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Teams need standardized patterns for design-to-code workflows to avoid inconsistent outputs.
 
 ## Governance Pattern
@@ -21,3 +24,619 @@ Teams need standardized patterns for design-to-code workflows to avoid inconsist
 You now have a team governance baseline for consistent design-to-code execution.
 
 Next: [Chapter 8: Production Security and Operations](08-production-security-and-operations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- tutorial slug: **figma-context-mcp-tutorial**
+- chapter focus: **Chapter 7: Team Workflows and Design Governance**
+- system context: **Figma Context Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Team Workflows and Design 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
+
+- [Figma Context MCP Repository](https://github.com/GLips/Figma-Context-MCP)
+- [Figma Context MCP Releases](https://github.com/GLips/Figma-Context-MCP/releases)
+- [Framelink Quickstart](https://www.framelink.ai/docs/quickstart)
+- [Model Context Protocol Intro](https://modelcontextprotocol.io/introduction)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Team Workflows and Design Governance`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 7: Team Workflows and Design Governance
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Team Workflows and Design Governance` as an operating subsystem inside **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Team Workflows and Design Governance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Figma Context MCP Repository](https://github.com/GLips/Figma-Context-MCP)
+  Why it matters: authoritative reference on `Figma Context MCP Repository` (github.com).
+- [Figma Context MCP Releases](https://github.com/GLips/Figma-Context-MCP/releases)
+  Why it matters: authoritative reference on `Figma Context MCP Releases` (github.com).
+- [Framelink Quickstart](https://www.framelink.ai/docs/quickstart)
+  Why it matters: authoritative reference on `Framelink Quickstart` (www.framelink.ai).
+- [Model Context Protocol Intro](https://modelcontextprotocol.io/introduction)
+  Why it matters: authoritative reference on `Model Context Protocol Intro` (modelcontextprotocol.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Performance and Token Optimization](06-performance-and-token-optimization.md)
+- [Next Chapter: Chapter 8: Production Security and Operations](08-production-security-and-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/figma-context-mcp-tutorial/08-production-security-and-operations.md b/tutorials/figma-context-mcp-tutorial/08-production-security-and-operations.md
index fb4ee26b..71361d83 100644
--- a/tutorials/figma-context-mcp-tutorial/08-production-security-and-operations.md
+++ b/tutorials/figma-context-mcp-tutorial/08-production-security-and-operations.md
@@ -7,6 +7,9 @@ parent: Figma Context MCP Tutorial
 
 # Chapter 8: Production Security and Operations
 
+Welcome to **Chapter 8: Production Security and Operations**. In this part of **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers secure deployment and operational policies for Figma context pipelines.
 
 ## Security Checklist
@@ -27,3 +30,606 @@ This chapter covers secure deployment and operational policies for Figma context
 ## Summary
 
 You now have the security and operations baseline for running Figma Context MCP in production teams.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- tutorial slug: **figma-context-mcp-tutorial**
+- chapter focus: **Chapter 8: Production Security and Operations**
+- system context: **Figma Context Mcp Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Security and Operations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Figma Context MCP Repository](https://github.com/GLips/Figma-Context-MCP)
+- [Figma Context MCP Releases](https://github.com/GLips/Figma-Context-MCP/releases)
+- [Framelink Quickstart](https://www.framelink.ai/docs/quickstart)
+- [Model Context Protocol Intro](https://modelcontextprotocol.io/introduction)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Security and Operations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 8: Production Security and Operations
+
+- tutorial context: **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Security and Operations` as an operating subsystem inside **Figma Context MCP Tutorial: Design-to-Code Workflows for Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Security and Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Figma Context MCP Repository](https://github.com/GLips/Figma-Context-MCP)
+  Why it matters: authoritative reference on `Figma Context MCP Repository` (github.com).
+- [Figma Context MCP Releases](https://github.com/GLips/Figma-Context-MCP/releases)
+  Why it matters: authoritative reference on `Figma Context MCP Releases` (github.com).
+- [Framelink Quickstart](https://www.framelink.ai/docs/quickstart)
+  Why it matters: authoritative reference on `Framelink Quickstart` (www.framelink.ai).
+- [Model Context Protocol Intro](https://modelcontextprotocol.io/introduction)
+  Why it matters: authoritative reference on `Model Context Protocol Intro` (modelcontextprotocol.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Team Workflows and Design Governance](07-team-workflows-and-design-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/firecrawl-mcp-server-tutorial/01-getting-started-and-core-setup.md b/tutorials/firecrawl-mcp-server-tutorial/01-getting-started-and-core-setup.md
index 8389800d..dc656079 100644
--- a/tutorials/firecrawl-mcp-server-tutorial/01-getting-started-and-core-setup.md
+++ b/tutorials/firecrawl-mcp-server-tutorial/01-getting-started-and-core-setup.md
@@ -7,6 +7,9 @@ parent: Firecrawl MCP Server Tutorial
 
 # Chapter 1: Getting Started and Core Setup
 
+Welcome to **Chapter 1: Getting Started and Core Setup**. In this part of **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets Firecrawl MCP running with minimum viable configuration.
 
 ## Learning Goals
@@ -37,3 +40,598 @@ env FIRECRAWL_API_KEY=fc-YOUR_API_KEY npx -y firecrawl-mcp
 You now have a working Firecrawl MCP baseline.
 
 Next: [Chapter 2: Architecture, Transports, and Versioning](02-architecture-transports-and-versioning.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: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- tutorial slug: **firecrawl-mcp-server-tutorial**
+- chapter focus: **Chapter 1: Getting Started and Core Setup**
+- system context: **Firecrawl Mcp Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started and Core Setup`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Repository](https://github.com/firecrawl/firecrawl-mcp-server)
+- [README](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/README.md)
+- [Changelog](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/CHANGELOG.md)
+- [Versioning Guide](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/VERSIONING.md)
+
+### Cross-Tutorial Connection Map
+
+- [Firecrawl Tutorial](../firecrawl-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [MCP Chrome Tutorial](../mcp-chrome-tutorial/)
+- [MCP Use Tutorial](../mcp-use-tutorial/)
+- [Chapter 1: Getting Started and Core Setup](01-getting-started-and-core-setup.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started and Core Setup`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started and Core Setup
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `YOUR_API_KEY`, `firecrawl` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started and Core Setup` as an operating subsystem inside **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started and Core Setup` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `YOUR_API_KEY`.
+2. **Input normalization**: shape incoming data so `firecrawl` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Repository](https://github.com/firecrawl/firecrawl-mcp-server)
+  Why it matters: authoritative reference on `Repository` (github.com).
+- [README](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Changelog](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/CHANGELOG.md)
+  Why it matters: authoritative reference on `Changelog` (github.com).
+- [Versioning Guide](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/VERSIONING.md)
+  Why it matters: authoritative reference on `Versioning Guide` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `YOUR_API_KEY` and `firecrawl` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Architecture, Transports, and Versioning](02-architecture-transports-and-versioning.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/firecrawl-mcp-server-tutorial/02-architecture-transports-and-versioning.md b/tutorials/firecrawl-mcp-server-tutorial/02-architecture-transports-and-versioning.md
index bdd443ce..67174b38 100644
--- a/tutorials/firecrawl-mcp-server-tutorial/02-architecture-transports-and-versioning.md
+++ b/tutorials/firecrawl-mcp-server-tutorial/02-architecture-transports-and-versioning.md
@@ -7,6 +7,9 @@ parent: Firecrawl MCP Server Tutorial
 
 # Chapter 2: Architecture, Transports, and Versioning
 
+Welcome to **Chapter 2: Architecture, Transports, and Versioning**. In this part of **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Firecrawl MCP supports local transports and cloud-mode versioned endpoints, with V2 as the default modern path.
 
 ## Learning Goals
@@ -38,3 +41,595 @@ Firecrawl MCP supports local transports and cloud-mode versioned endpoints, with
 You now understand the transport and version boundaries that shape deployment architecture.
 
 Next: [Chapter 3: Tool Selection: Scrape, Map, Crawl, Search, Extract](03-tool-selection-scrape-map-crawl-search-extract.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: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- tutorial slug: **firecrawl-mcp-server-tutorial**
+- chapter focus: **Chapter 2: Architecture, Transports, and Versioning**
+- system context: **Firecrawl Mcp Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Architecture, Transports, and Versioning`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Repository](https://github.com/firecrawl/firecrawl-mcp-server)
+- [README](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/README.md)
+- [Changelog](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/CHANGELOG.md)
+- [Versioning Guide](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/VERSIONING.md)
+
+### Cross-Tutorial Connection Map
+
+- [Firecrawl Tutorial](../firecrawl-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [MCP Chrome Tutorial](../mcp-chrome-tutorial/)
+- [MCP Use Tutorial](../mcp-use-tutorial/)
+- [Chapter 1: Getting Started and Core Setup](01-getting-started-and-core-setup.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Architecture, Transports, and Versioning`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Architecture, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Architecture, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Architecture, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Architecture, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Architecture, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Architecture, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Architecture, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Architecture, Transports, and Versioning
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Architecture, Transports, and Versioning` as an operating subsystem inside **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Architecture, Transports, and Versioning` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Repository](https://github.com/firecrawl/firecrawl-mcp-server)
+  Why it matters: authoritative reference on `Repository` (github.com).
+- [README](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Changelog](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/CHANGELOG.md)
+  Why it matters: authoritative reference on `Changelog` (github.com).
+- [Versioning Guide](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/VERSIONING.md)
+  Why it matters: authoritative reference on `Versioning Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started and Core Setup](01-getting-started-and-core-setup.md)
+- [Next Chapter: Chapter 3: Tool Selection: Scrape, Map, Crawl, Search, Extract](03-tool-selection-scrape-map-crawl-search-extract.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/firecrawl-mcp-server-tutorial/03-tool-selection-scrape-map-crawl-search-extract.md b/tutorials/firecrawl-mcp-server-tutorial/03-tool-selection-scrape-map-crawl-search-extract.md
index 776ceb18..acf86d5f 100644
--- a/tutorials/firecrawl-mcp-server-tutorial/03-tool-selection-scrape-map-crawl-search-extract.md
+++ b/tutorials/firecrawl-mcp-server-tutorial/03-tool-selection-scrape-map-crawl-search-extract.md
@@ -7,6 +7,9 @@ parent: Firecrawl MCP Server Tutorial
 
 # Chapter 3: Tool Selection: Scrape, Map, Crawl, Search, Extract
 
+Welcome to **Chapter 3: Tool Selection: Scrape, Map, Crawl, Search, Extract**. In this part of **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Effective Firecrawl usage depends on selecting the right tool for each information-retrieval task.
 
 ## Learning Goals
@@ -35,3 +38,595 @@ Effective Firecrawl usage depends on selecting the right tool for each informati
 You now have a decision framework for tool selection that balances depth, cost, and speed.
 
 Next: [Chapter 4: Client Integrations: Cursor, Claude, Windsurf, VS Code](04-client-integrations-cursor-claude-windsurf-vscode.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: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- tutorial slug: **firecrawl-mcp-server-tutorial**
+- chapter focus: **Chapter 3: Tool Selection: Scrape, Map, Crawl, Search, Extract**
+- system context: **Firecrawl Mcp Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Tool Selection: Scrape, Map, Crawl, Search, Extract`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Repository](https://github.com/firecrawl/firecrawl-mcp-server)
+- [README](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/README.md)
+- [Changelog](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/CHANGELOG.md)
+- [Versioning Guide](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/VERSIONING.md)
+
+### Cross-Tutorial Connection Map
+
+- [Firecrawl Tutorial](../firecrawl-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [MCP Chrome Tutorial](../mcp-chrome-tutorial/)
+- [MCP Use Tutorial](../mcp-use-tutorial/)
+- [Chapter 1: Getting Started and Core Setup](01-getting-started-and-core-setup.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Tool Selection: Scrape, Map, Crawl, Search, Extract`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Tool Selection: Scrape, Map, Crawl, Search, Extract
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Tool Selection: Scrape, Map, Crawl, Search, Extract` as an operating subsystem inside **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Tool Selection: Scrape, Map, Crawl, Search, Extract` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Repository](https://github.com/firecrawl/firecrawl-mcp-server)
+  Why it matters: authoritative reference on `Repository` (github.com).
+- [README](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Changelog](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/CHANGELOG.md)
+  Why it matters: authoritative reference on `Changelog` (github.com).
+- [Versioning Guide](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/VERSIONING.md)
+  Why it matters: authoritative reference on `Versioning Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Architecture, Transports, and Versioning](02-architecture-transports-and-versioning.md)
+- [Next Chapter: Chapter 4: Client Integrations: Cursor, Claude, Windsurf, VS Code](04-client-integrations-cursor-claude-windsurf-vscode.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/firecrawl-mcp-server-tutorial/04-client-integrations-cursor-claude-windsurf-vscode.md b/tutorials/firecrawl-mcp-server-tutorial/04-client-integrations-cursor-claude-windsurf-vscode.md
index acb37482..8dcb2552 100644
--- a/tutorials/firecrawl-mcp-server-tutorial/04-client-integrations-cursor-claude-windsurf-vscode.md
+++ b/tutorials/firecrawl-mcp-server-tutorial/04-client-integrations-cursor-claude-windsurf-vscode.md
@@ -7,6 +7,9 @@ parent: Firecrawl MCP Server Tutorial
 
 # Chapter 4: Client Integrations: Cursor, Claude, Windsurf, VS Code
 
+Welcome to **Chapter 4: Client Integrations: Cursor, Claude, Windsurf, VS Code**. In this part of **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Firecrawl MCP is widely used because it can be configured across major coding-agent clients.
 
 ## Learning Goals
@@ -33,3 +36,607 @@ Firecrawl MCP is widely used because it can be configured across major coding-ag
 You now have a cross-client setup model for consistent Firecrawl MCP usage.
 
 Next: [Chapter 5: Configuration, Retries, and Credit Monitoring](05-configuration-retries-and-credit-monitoring.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: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- tutorial slug: **firecrawl-mcp-server-tutorial**
+- chapter focus: **Chapter 4: Client Integrations: Cursor, Claude, Windsurf, VS Code**
+- system context: **Firecrawl Mcp Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Client Integrations: Cursor, Claude, Windsurf, VS Code`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Repository](https://github.com/firecrawl/firecrawl-mcp-server)
+- [README](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/README.md)
+- [Changelog](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/CHANGELOG.md)
+- [Versioning Guide](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/VERSIONING.md)
+
+### Cross-Tutorial Connection Map
+
+- [Firecrawl Tutorial](../firecrawl-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [MCP Chrome Tutorial](../mcp-chrome-tutorial/)
+- [MCP Use Tutorial](../mcp-use-tutorial/)
+- [Chapter 1: Getting Started and Core Setup](01-getting-started-and-core-setup.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Client Integrations: Cursor, Claude, Windsurf, VS Code`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 4: Client Integrations: Cursor, Claude, Windsurf, VS Code
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Client Integrations: Cursor, Claude, Windsurf, VS Code` as an operating subsystem inside **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Client Integrations: Cursor, Claude, Windsurf, VS Code` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Repository](https://github.com/firecrawl/firecrawl-mcp-server)
+  Why it matters: authoritative reference on `Repository` (github.com).
+- [README](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Changelog](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/CHANGELOG.md)
+  Why it matters: authoritative reference on `Changelog` (github.com).
+- [Versioning Guide](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/VERSIONING.md)
+  Why it matters: authoritative reference on `Versioning Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Tool Selection: Scrape, Map, Crawl, Search, Extract](03-tool-selection-scrape-map-crawl-search-extract.md)
+- [Next Chapter: Chapter 5: Configuration, Retries, and Credit Monitoring](05-configuration-retries-and-credit-monitoring.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/firecrawl-mcp-server-tutorial/05-configuration-retries-and-credit-monitoring.md b/tutorials/firecrawl-mcp-server-tutorial/05-configuration-retries-and-credit-monitoring.md
index ff483af4..1f939b16 100644
--- a/tutorials/firecrawl-mcp-server-tutorial/05-configuration-retries-and-credit-monitoring.md
+++ b/tutorials/firecrawl-mcp-server-tutorial/05-configuration-retries-and-credit-monitoring.md
@@ -7,6 +7,9 @@ parent: Firecrawl MCP Server Tutorial
 
 # Chapter 5: Configuration, Retries, and Credit Monitoring
 
+Welcome to **Chapter 5: Configuration, Retries, and Credit Monitoring**. In this part of **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Production reliability depends on proper retry controls and credit thresholds.
 
 ## Learning Goals
@@ -34,3 +37,595 @@ Production reliability depends on proper retry controls and credit thresholds.
 You now know which controls matter most for resilient Firecrawl MCP operations.
 
 Next: [Chapter 6: Batch Workflows, Deep Research, and API Evolution](06-batch-workflows-deep-research-and-api-evolution.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- tutorial slug: **firecrawl-mcp-server-tutorial**
+- chapter focus: **Chapter 5: Configuration, Retries, and Credit Monitoring**
+- system context: **Firecrawl Mcp Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Configuration, Retries, and Credit Monitoring`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Repository](https://github.com/firecrawl/firecrawl-mcp-server)
+- [README](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/README.md)
+- [Changelog](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/CHANGELOG.md)
+- [Versioning Guide](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/VERSIONING.md)
+
+### Cross-Tutorial Connection Map
+
+- [Firecrawl Tutorial](../firecrawl-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [MCP Chrome Tutorial](../mcp-chrome-tutorial/)
+- [MCP Use Tutorial](../mcp-use-tutorial/)
+- [Chapter 1: Getting Started and Core Setup](01-getting-started-and-core-setup.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Configuration, Retries, and Credit Monitoring`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Configuration, Retries, and Credit Monitoring
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Configuration, Retries, and Credit Monitoring` as an operating subsystem inside **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Configuration, Retries, and Credit Monitoring` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Repository](https://github.com/firecrawl/firecrawl-mcp-server)
+  Why it matters: authoritative reference on `Repository` (github.com).
+- [README](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Changelog](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/CHANGELOG.md)
+  Why it matters: authoritative reference on `Changelog` (github.com).
+- [Versioning Guide](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/VERSIONING.md)
+  Why it matters: authoritative reference on `Versioning Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Client Integrations: Cursor, Claude, Windsurf, VS Code](04-client-integrations-cursor-claude-windsurf-vscode.md)
+- [Next Chapter: Chapter 6: Batch Workflows, Deep Research, and API Evolution](06-batch-workflows-deep-research-and-api-evolution.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/firecrawl-mcp-server-tutorial/06-batch-workflows-deep-research-and-api-evolution.md b/tutorials/firecrawl-mcp-server-tutorial/06-batch-workflows-deep-research-and-api-evolution.md
index c44fc6e8..1cfe7335 100644
--- a/tutorials/firecrawl-mcp-server-tutorial/06-batch-workflows-deep-research-and-api-evolution.md
+++ b/tutorials/firecrawl-mcp-server-tutorial/06-batch-workflows-deep-research-and-api-evolution.md
@@ -7,6 +7,9 @@ parent: Firecrawl MCP Server Tutorial
 
 # Chapter 6: Batch Workflows, Deep Research, and API Evolution
 
+Welcome to **Chapter 6: Batch Workflows, Deep Research, and API Evolution**. In this part of **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Firecrawl MCP has evolved from legacy V1 tooling to modern V2 behavior; teams need to plan around those differences.
 
 ## Learning Goals
@@ -32,3 +35,607 @@ Firecrawl MCP has evolved from legacy V1 tooling to modern V2 behavior; teams ne
 You now have a migration-aware perspective on batch and advanced Firecrawl MCP usage.
 
 Next: [Chapter 7: Reliability, Observability, and Failure Handling](07-reliability-observability-and-failure-handling.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: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- tutorial slug: **firecrawl-mcp-server-tutorial**
+- chapter focus: **Chapter 6: Batch Workflows, Deep Research, and API Evolution**
+- system context: **Firecrawl Mcp Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Batch Workflows, Deep Research, and API Evolution`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Repository](https://github.com/firecrawl/firecrawl-mcp-server)
+- [README](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/README.md)
+- [Changelog](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/CHANGELOG.md)
+- [Versioning Guide](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/VERSIONING.md)
+
+### Cross-Tutorial Connection Map
+
+- [Firecrawl Tutorial](../firecrawl-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [MCP Chrome Tutorial](../mcp-chrome-tutorial/)
+- [MCP Use Tutorial](../mcp-use-tutorial/)
+- [Chapter 1: Getting Started and Core Setup](01-getting-started-and-core-setup.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Batch Workflows, Deep Research, and API Evolution`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 6: Batch Workflows, Deep Research, and API Evolution
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Batch Workflows, Deep Research, and API Evolution` as an operating subsystem inside **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Batch Workflows, Deep Research, and API Evolution` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Repository](https://github.com/firecrawl/firecrawl-mcp-server)
+  Why it matters: authoritative reference on `Repository` (github.com).
+- [README](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Changelog](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/CHANGELOG.md)
+  Why it matters: authoritative reference on `Changelog` (github.com).
+- [Versioning Guide](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/VERSIONING.md)
+  Why it matters: authoritative reference on `Versioning Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Configuration, Retries, and Credit Monitoring](05-configuration-retries-and-credit-monitoring.md)
+- [Next Chapter: Chapter 7: Reliability, Observability, and Failure Handling](07-reliability-observability-and-failure-handling.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/firecrawl-mcp-server-tutorial/07-reliability-observability-and-failure-handling.md b/tutorials/firecrawl-mcp-server-tutorial/07-reliability-observability-and-failure-handling.md
index a405c665..3e0564c5 100644
--- a/tutorials/firecrawl-mcp-server-tutorial/07-reliability-observability-and-failure-handling.md
+++ b/tutorials/firecrawl-mcp-server-tutorial/07-reliability-observability-and-failure-handling.md
@@ -7,6 +7,9 @@ parent: Firecrawl MCP Server Tutorial
 
 # Chapter 7: Reliability, Observability, and Failure Handling
 
+Welcome to **Chapter 7: Reliability, Observability, and Failure Handling**. In this part of **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter turns error handling and operational controls into an explicit runbook.
 
 ## Learning Goals
@@ -32,3 +35,607 @@ This chapter turns error handling and operational controls into an explicit runb
 You now have a reliability checklist for sustained Firecrawl MCP operations.
 
 Next: [Chapter 8: Security, Governance, and Contribution Workflow](08-security-governance-and-contribution-workflow.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- tutorial slug: **firecrawl-mcp-server-tutorial**
+- chapter focus: **Chapter 7: Reliability, Observability, and Failure Handling**
+- system context: **Firecrawl Mcp Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Reliability, Observability, and Failure 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
+
+- [Repository](https://github.com/firecrawl/firecrawl-mcp-server)
+- [README](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/README.md)
+- [Changelog](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/CHANGELOG.md)
+- [Versioning Guide](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/VERSIONING.md)
+
+### Cross-Tutorial Connection Map
+
+- [Firecrawl Tutorial](../firecrawl-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [MCP Chrome Tutorial](../mcp-chrome-tutorial/)
+- [MCP Use Tutorial](../mcp-use-tutorial/)
+- [Chapter 1: Getting Started and Core Setup](01-getting-started-and-core-setup.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Reliability, Observability, and Failure 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 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 7: Reliability, Observability, and Failure Handling
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Reliability, Observability, and Failure Handling` as an operating subsystem inside **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Reliability, Observability, and Failure Handling` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Repository](https://github.com/firecrawl/firecrawl-mcp-server)
+  Why it matters: authoritative reference on `Repository` (github.com).
+- [README](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Changelog](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/CHANGELOG.md)
+  Why it matters: authoritative reference on `Changelog` (github.com).
+- [Versioning Guide](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/VERSIONING.md)
+  Why it matters: authoritative reference on `Versioning Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Batch Workflows, Deep Research, and API Evolution](06-batch-workflows-deep-research-and-api-evolution.md)
+- [Next Chapter: Chapter 8: Security, Governance, and Contribution Workflow](08-security-governance-and-contribution-workflow.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/firecrawl-mcp-server-tutorial/08-security-governance-and-contribution-workflow.md b/tutorials/firecrawl-mcp-server-tutorial/08-security-governance-and-contribution-workflow.md
index 5dacf3e7..c6aa0ee3 100644
--- a/tutorials/firecrawl-mcp-server-tutorial/08-security-governance-and-contribution-workflow.md
+++ b/tutorials/firecrawl-mcp-server-tutorial/08-security-governance-and-contribution-workflow.md
@@ -7,6 +7,9 @@ parent: Firecrawl MCP Server Tutorial
 
 # Chapter 8: Security, Governance, and Contribution Workflow
 
+Welcome to **Chapter 8: Security, Governance, and Contribution Workflow**. In this part of **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter concludes with governance patterns for production use and contribution pathways.
 
 ## Learning Goals
@@ -34,3 +37,594 @@ This chapter concludes with governance patterns for production use and contribut
 You now have an end-to-end model for adopting and operating Firecrawl MCP Server with strong governance.
 
 Next: combine this with [MCP Chrome](../mcp-chrome-tutorial/) and [MCP Inspector](../mcp-inspector-tutorial/) for full browsing-data toolchains.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- tutorial slug: **firecrawl-mcp-server-tutorial**
+- chapter focus: **Chapter 8: Security, Governance, and Contribution Workflow**
+- system context: **Firecrawl Mcp Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Security, Governance, and Contribution Workflow`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Repository](https://github.com/firecrawl/firecrawl-mcp-server)
+- [README](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/README.md)
+- [Changelog](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/CHANGELOG.md)
+- [Versioning Guide](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/VERSIONING.md)
+
+### Cross-Tutorial Connection Map
+
+- [Firecrawl Tutorial](../firecrawl-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [MCP Chrome Tutorial](../mcp-chrome-tutorial/)
+- [MCP Use Tutorial](../mcp-use-tutorial/)
+- [Chapter 1: Getting Started and Core Setup](01-getting-started-and-core-setup.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Security, Governance, and Contribution Workflow`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Security, Governance, and Contribution Workflow
+
+- tutorial context: **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Security, Governance, and Contribution Workflow` as an operating subsystem inside **Firecrawl MCP Server Tutorial: Web Scraping and Search Tools for MCP Clients**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Security, Governance, and Contribution Workflow` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Repository](https://github.com/firecrawl/firecrawl-mcp-server)
+  Why it matters: authoritative reference on `Repository` (github.com).
+- [README](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Changelog](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/CHANGELOG.md)
+  Why it matters: authoritative reference on `Changelog` (github.com).
+- [Versioning Guide](https://github.com/firecrawl/firecrawl-mcp-server/blob/main/VERSIONING.md)
+  Why it matters: authoritative reference on `Versioning Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Reliability, Observability, and Failure Handling](07-reliability-observability-and-failure-handling.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/firecrawl-tutorial/01-getting-started.md b/tutorials/firecrawl-tutorial/01-getting-started.md
index a95f5a25..21d8421b 100644
--- a/tutorials/firecrawl-tutorial/01-getting-started.md
+++ b/tutorials/firecrawl-tutorial/01-getting-started.md
@@ -497,3 +497,48 @@ Now that you can scrape websites effectively, let's explore more advanced extrac
 4. Experiment with different output formats and extraction rules
 
 *What kind of web content would you most like to extract for AI applications?* 🌐
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `result`, `print` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Firecrawl` as an operating subsystem inside **Firecrawl Tutorial: Building LLM-Ready Web Scraping and Data Extraction Systems**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `firecrawl`, `content`, `posts` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Firecrawl` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `result` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `print`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/firecrawl/firecrawl)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `result` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Basic Web Scraping](02-basic-scraping.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/firecrawl-tutorial/02-basic-scraping.md b/tutorials/firecrawl-tutorial/02-basic-scraping.md
index 6e012843..70e53763 100644
--- a/tutorials/firecrawl-tutorial/02-basic-scraping.md
+++ b/tutorials/firecrawl-tutorial/02-basic-scraping.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Basic Web Scraping
 
+Welcome to **Chapter 2: Basic Web Scraping**. In this part of **Firecrawl Tutorial: Building LLM-Ready Web Scraping and Data Extraction Systems**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 1](01-getting-started.md), you installed Firecrawl and ran your first scrape. Now it is time to build a solid foundation in basic web scraping -- learning how to extract content from single pages, batch multiple URLs efficiently, choose the right output format, and handle the errors that inevitably arise when talking to the open web.
 
 ## What You Will Learn
@@ -467,3 +470,49 @@ You can now scrape pages reliably. In [Chapter 3: Advanced Data Extraction](03-a
 ---
 
 *Built with insights from the [Firecrawl](https://github.com/mendableai/firecrawl) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `result`, `https`, `page` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Basic Web Scraping` as an operating subsystem inside **Firecrawl Tutorial: Building LLM-Ready Web Scraping and Data Extraction Systems**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `example`, `print`, `markdown` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Basic Web Scraping` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `result`.
+2. **Input normalization**: shape incoming data so `https` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `page`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/firecrawl/firecrawl)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `result` and `https` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Firecrawl](01-getting-started.md)
+- [Next Chapter: Chapter 3: Advanced Data Extraction](03-advanced-extraction.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/firecrawl-tutorial/03-advanced-extraction.md b/tutorials/firecrawl-tutorial/03-advanced-extraction.md
index 137b2bb8..b396c0ab 100644
--- a/tutorials/firecrawl-tutorial/03-advanced-extraction.md
+++ b/tutorials/firecrawl-tutorial/03-advanced-extraction.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Advanced Data Extraction
 
+Welcome to **Chapter 3: Advanced Data Extraction**. In this part of **Firecrawl Tutorial: Building LLM-Ready Web Scraping and Data Extraction Systems**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 In [Chapter 2](02-basic-scraping.md), you learned how to scrape pages and get back raw content in Markdown, HTML, or JSON. But raw content is only the starting point. Most AI applications need **structured data** -- typed fields like titles, prices, dates, and authors extracted from messy web pages into clean, predictable objects.
 
 Firecrawl's extraction capabilities let you define schemas that describe what you want, and the engine returns exactly that. This chapter covers schema-driven extraction, CSS selector rules, LLM-powered extraction, validation, and building reusable extraction pipelines.
@@ -549,3 +552,49 @@ Now that you can extract structured data, many websites hide their content behin
 ---
 
 *Built with insights from the [Firecrawl](https://github.com/mendableai/firecrawl) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `extract`, `print`, `schema` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Advanced Data Extraction` as an operating subsystem inside **Firecrawl Tutorial: Building LLM-Ready Web Scraping and Data Extraction Systems**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `result`, `title`, `body` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Advanced Data Extraction` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `extract`.
+2. **Input normalization**: shape incoming data so `print` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `schema`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/firecrawl/firecrawl)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `extract` and `print` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Basic Web Scraping](02-basic-scraping.md)
+- [Next Chapter: Chapter 4: JavaScript & Dynamic Content](04-javascript-handling.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/firecrawl-tutorial/04-javascript-handling.md b/tutorials/firecrawl-tutorial/04-javascript-handling.md
index ed76bee2..70968202 100644
--- a/tutorials/firecrawl-tutorial/04-javascript-handling.md
+++ b/tutorials/firecrawl-tutorial/04-javascript-handling.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: JavaScript & Dynamic Content
 
+Welcome to **Chapter 4: JavaScript & Dynamic Content**. In this part of **Firecrawl Tutorial: Building LLM-Ready Web Scraping and Data Extraction Systems**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 The modern web runs on JavaScript. Single-page applications (SPAs), infinite scroll feeds, lazy-loaded images, and Ajax-powered dashboards all render content dynamically in the browser -- meaning that a simple HTTP GET returns an empty shell. If your scraper does not execute JavaScript, you get nothing.
 
 Firecrawl solves this with built-in browser rendering. In this chapter you will learn how to configure rendering options, wait for dynamic content, handle infinite scroll and pagination, intercept Ajax requests, and debug blank-page issues.
@@ -485,3 +488,49 @@ Now that you can extract content from any website -- static or dynamic -- the ne
 ---
 
 *Built with insights from the [Firecrawl](https://github.com/mendableai/firecrawl) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `markdown`, `print`, `result` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: JavaScript & Dynamic Content` as an operating subsystem inside **Firecrawl Tutorial: Building LLM-Ready Web Scraping and Data Extraction Systems**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `waitFor`, `formats`, `https` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: JavaScript & Dynamic Content` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `markdown`.
+2. **Input normalization**: shape incoming data so `print` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `result`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/firecrawl/firecrawl)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `markdown` and `print` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Advanced Data Extraction](03-advanced-extraction.md)
+- [Next Chapter: Chapter 5: Content Cleaning & Processing](05-content-cleaning.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/firecrawl-tutorial/05-content-cleaning.md b/tutorials/firecrawl-tutorial/05-content-cleaning.md
index b98b2f60..77ab41c0 100644
--- a/tutorials/firecrawl-tutorial/05-content-cleaning.md
+++ b/tutorials/firecrawl-tutorial/05-content-cleaning.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Content Cleaning & Processing
 
+Welcome to **Chapter 5: Content Cleaning & Processing**. In this part of **Firecrawl Tutorial: Building LLM-Ready Web Scraping and Data Extraction Systems**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 You have scraped web pages, rendered JavaScript, and extracted structured data. But raw scraped content is still noisy -- full of navigation menus, cookie banners, ad blocks, duplicate paragraphs, broken links, and inconsistent formatting. Before feeding content to an LLM, you need to clean, normalize, and validate it.
 
 This chapter covers the full content cleaning pipeline: removing boilerplate, normalizing text and links, deduplicating content, scoring quality, handling media, and producing output that is optimized for LLM token budgets.
@@ -592,3 +595,49 @@ Your content is now clean and ready for AI consumption. In [Chapter 6: Building
 ---
 
 *Built with insights from the [Firecrawl](https://github.com/mendableai/firecrawl) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `text`, `content`, `markdown` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Content Cleaning & Processing` as an operating subsystem inside **Firecrawl Tutorial: Building LLM-Ready Web Scraping and Data Extraction Systems**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `print`, `paragraphs`, `html_content` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Content Cleaning & Processing` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `text`.
+2. **Input normalization**: shape incoming data so `content` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `markdown`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/firecrawl/firecrawl)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `text` and `content` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: JavaScript & Dynamic Content](04-javascript-handling.md)
+- [Next Chapter: Chapter 6: Building RAG Systems](06-rag-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/firecrawl-tutorial/06-rag-integration.md b/tutorials/firecrawl-tutorial/06-rag-integration.md
index c71212a7..1d3327a3 100644
--- a/tutorials/firecrawl-tutorial/06-rag-integration.md
+++ b/tutorials/firecrawl-tutorial/06-rag-integration.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Building RAG Systems
 
+Welcome to **Chapter 6: Building RAG Systems**. In this part of **Firecrawl Tutorial: Building LLM-Ready Web Scraping and Data Extraction Systems**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Retrieval-Augmented Generation (RAG) is the most impactful application of web scraping for AI. Instead of relying solely on an LLM's training data, RAG retrieves relevant content from your scraped corpus and injects it into the prompt -- giving the model up-to-date, domain-specific knowledge without fine-tuning.
 
 In this chapter you will build a complete RAG pipeline: scrape websites with Firecrawl, chunk content intelligently, generate embeddings, store them in a vector database, and build a question-answering system that grounds its answers in your scraped data.
@@ -643,3 +646,49 @@ Your RAG pipeline works. Now you need to scale it. In [Chapter 7: Scaling & Perf
 ---
 
 *Built with insights from the [Firecrawl](https://github.com/mendableai/firecrawl) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `query`, `content`, `documents` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Building RAG Systems` as an operating subsystem inside **Firecrawl Tutorial: Building LLM-Ready Web Scraping and Data Extraction Systems**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `print`, `chunks`, `embeddings` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Building RAG Systems` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `query`.
+2. **Input normalization**: shape incoming data so `content` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `documents`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/firecrawl/firecrawl)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `query` and `content` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Content Cleaning & Processing](05-content-cleaning.md)
+- [Next Chapter: Chapter 7: Scaling & Performance](07-scaling-performance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/firecrawl-tutorial/07-scaling-performance.md b/tutorials/firecrawl-tutorial/07-scaling-performance.md
index 48f209bc..e12127b6 100644
--- a/tutorials/firecrawl-tutorial/07-scaling-performance.md
+++ b/tutorials/firecrawl-tutorial/07-scaling-performance.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Scaling & Performance
 
+Welcome to **Chapter 7: Scaling & Performance**. In this part of **Firecrawl Tutorial: Building LLM-Ready Web Scraping and Data Extraction Systems**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Scraping ten pages is easy. Scraping ten thousand pages -- reliably, efficiently, and without getting blocked -- requires a fundamentally different approach. This chapter covers the architecture and engineering practices needed to scale Firecrawl-based scraping and RAG pipelines from prototype to production.
 
 You will learn how to distribute work across multiple workers, control concurrency and rate limits, build intelligent caching layers, implement job queues, and monitor performance to identify bottlenecks before they become outages.
@@ -708,3 +711,49 @@ Your scraping system can now handle thousands of pages reliably. In [Chapter 8:
 ---
 
 *Built with insights from the [Firecrawl](https://github.com/mendableai/firecrawl) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `time`, `redis` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Scaling & Performance` as an operating subsystem inside **Firecrawl Tutorial: Building LLM-Ready Web Scraping and Data Extraction Systems**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `urls`, `result`, `json` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Scaling & Performance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `time` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `redis`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/firecrawl/firecrawl)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `time` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Building RAG Systems](06-rag-integration.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/firecrawl-tutorial/08-production-deployment.md b/tutorials/firecrawl-tutorial/08-production-deployment.md
index 056f9cc8..91f6c427 100644
--- a/tutorials/firecrawl-tutorial/08-production-deployment.md
+++ b/tutorials/firecrawl-tutorial/08-production-deployment.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **Firecrawl Tutorial: Building LLM-Ready Web Scraping and Data Extraction Systems**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 You have built a scraping and RAG pipeline that works on your laptop. Now it is time to deploy it to production -- where it must run 24/7 with high reliability, proper security, automated recovery, and full observability. This chapter covers everything from containerization to Kubernetes deployment, from secrets management to disaster recovery.
 
 Production deployment is where the rubber meets the road. A system that works in development but fails in production provides zero value. This chapter ensures your Firecrawl-based pipeline survives the real world.
@@ -857,3 +860,48 @@ From here, consider these advanced topics:
 ---
 
 *Built with insights from the [Firecrawl](https://github.com/mendableai/firecrawl) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `worker`, `name`, `self` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **Firecrawl Tutorial: Building LLM-Ready Web Scraping and Data Extraction Systems**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `info`, `scrape`, `record` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `worker`.
+2. **Input normalization**: shape incoming data so `name` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `self`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/firecrawl/firecrawl)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `worker` and `name` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Scaling & Performance](07-scaling-performance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fireproof-tutorial/01-getting-started.md b/tutorials/fireproof-tutorial/01-getting-started.md
index f431a60f..80ea2af2 100644
--- a/tutorials/fireproof-tutorial/01-getting-started.md
+++ b/tutorials/fireproof-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Fireproof Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets Fireproof running with both React-hook and core API entry points.
 
 ## Quick Start
@@ -46,3 +49,589 @@ const doc = await db.get("beyonce");
 You now have Fireproof running with a minimal document lifecycle.
 
 Next: [Chapter 2: Core Document API and Query Lifecycle](02-core-document-api-and-query-lifecycle.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- tutorial slug: **fireproof-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Fireproof 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
+
+- [Fireproof Repository](https://github.com/fireproof-storage/fireproof)
+- [Fireproof README](https://github.com/fireproof-storage/fireproof/blob/main/README.md)
+- [Database implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/database.ts)
+- [Ledger implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/ledger.ts)
+- [IndexedDB gateway](https://github.com/fireproof-storage/fireproof/blob/main/core/gateways/indexeddb/gateway-impl.ts)
+
+### Cross-Tutorial Connection Map
+
+- [Supabase Tutorial](../supabase-tutorial/)
+- [RagFlow Tutorial](../ragflow-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `fireproof`, `install`, `core` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `beyonce`, `music`, `name` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `fireproof`.
+2. **Input normalization**: shape incoming data so `install` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `core`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Fireproof Repository](https://github.com/fireproof-storage/fireproof)
+  Why it matters: authoritative reference on `Fireproof Repository` (github.com).
+- [Fireproof README](https://github.com/fireproof-storage/fireproof/blob/main/README.md)
+  Why it matters: authoritative reference on `Fireproof README` (github.com).
+- [Database implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/database.ts)
+  Why it matters: authoritative reference on `Database implementation` (github.com).
+- [Ledger implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/ledger.ts)
+  Why it matters: authoritative reference on `Ledger implementation` (github.com).
+- [IndexedDB gateway](https://github.com/fireproof-storage/fireproof/blob/main/core/gateways/indexeddb/gateway-impl.ts)
+  Why it matters: authoritative reference on `IndexedDB gateway` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `fireproof` and `install` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Core Document API and Query Lifecycle](02-core-document-api-and-query-lifecycle.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fireproof-tutorial/02-core-document-api-and-query-lifecycle.md b/tutorials/fireproof-tutorial/02-core-document-api-and-query-lifecycle.md
index 29c30ce6..b87f576f 100644
--- a/tutorials/fireproof-tutorial/02-core-document-api-and-query-lifecycle.md
+++ b/tutorials/fireproof-tutorial/02-core-document-api-and-query-lifecycle.md
@@ -7,6 +7,9 @@ parent: Fireproof Tutorial
 
 # Chapter 2: Core Document API and Query Lifecycle
 
+Welcome to **Chapter 2: Core Document API and Query Lifecycle**. In this part of **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Fireproof exposes familiar document-database operations with explicit support for change streams and query indexes.
 
 ## Core Operations
@@ -37,3 +40,598 @@ Use `changes` or subscriptions to avoid full reload loops when building reactive
 You now understand the document lifecycle and read/query semantics.
 
 Next: [Chapter 3: React Hooks and Live Local-First UX](03-react-hooks-and-live-local-first-ux.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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- tutorial slug: **fireproof-tutorial**
+- chapter focus: **Chapter 2: Core Document API and Query Lifecycle**
+- system context: **Fireproof Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Core Document API and Query Lifecycle`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Fireproof Repository](https://github.com/fireproof-storage/fireproof)
+- [Fireproof README](https://github.com/fireproof-storage/fireproof/blob/main/README.md)
+- [Database implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/database.ts)
+- [Ledger implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/ledger.ts)
+- [IndexedDB gateway](https://github.com/fireproof-storage/fireproof/blob/main/core/gateways/indexeddb/gateway-impl.ts)
+
+### Cross-Tutorial Connection Map
+
+- [Supabase Tutorial](../supabase-tutorial/)
+- [RagFlow Tutorial](../ragflow-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Core Document API and Query Lifecycle`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Core Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Core Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Core Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Core Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Core Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Core Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Core Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Core Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Core Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Core Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Core Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Core Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Core Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Core Document API and Query Lifecycle
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Core Document API and Query Lifecycle` as an operating subsystem inside **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Core Document API and Query Lifecycle` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Fireproof Repository](https://github.com/fireproof-storage/fireproof)
+  Why it matters: authoritative reference on `Fireproof Repository` (github.com).
+- [Fireproof README](https://github.com/fireproof-storage/fireproof/blob/main/README.md)
+  Why it matters: authoritative reference on `Fireproof README` (github.com).
+- [Database implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/database.ts)
+  Why it matters: authoritative reference on `Database implementation` (github.com).
+- [Ledger implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/ledger.ts)
+  Why it matters: authoritative reference on `Ledger implementation` (github.com).
+- [IndexedDB gateway](https://github.com/fireproof-storage/fireproof/blob/main/core/gateways/indexeddb/gateway-impl.ts)
+  Why it matters: authoritative reference on `IndexedDB gateway` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: React Hooks and Live Local-First UX](03-react-hooks-and-live-local-first-ux.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fireproof-tutorial/03-react-hooks-and-live-local-first-ux.md b/tutorials/fireproof-tutorial/03-react-hooks-and-live-local-first-ux.md
index 4e1254ee..cd77426d 100644
--- a/tutorials/fireproof-tutorial/03-react-hooks-and-live-local-first-ux.md
+++ b/tutorials/fireproof-tutorial/03-react-hooks-and-live-local-first-ux.md
@@ -7,6 +7,9 @@ parent: Fireproof Tutorial
 
 # Chapter 3: React Hooks and Live Local-First UX
 
+Welcome to **Chapter 3: React Hooks and Live Local-First UX**. In this part of **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Fireproof provides React hooks so local writes and query updates stay synchronized in UI state.
 
 ## Hook Roles
@@ -35,3 +38,598 @@ This model removes much of the manual cache invalidation and loading-state orche
 You now have the React mental model for real-time local-first Fireproof UIs.
 
 Next: [Chapter 4: Ledger, CRDT, and Causal Consistency](04-ledger-crdt-and-causal-consistency.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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- tutorial slug: **fireproof-tutorial**
+- chapter focus: **Chapter 3: React Hooks and Live Local-First UX**
+- system context: **Fireproof Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: React Hooks and Live Local-First UX`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Fireproof Repository](https://github.com/fireproof-storage/fireproof)
+- [Fireproof README](https://github.com/fireproof-storage/fireproof/blob/main/README.md)
+- [Database implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/database.ts)
+- [Ledger implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/ledger.ts)
+- [IndexedDB gateway](https://github.com/fireproof-storage/fireproof/blob/main/core/gateways/indexeddb/gateway-impl.ts)
+
+### Cross-Tutorial Connection Map
+
+- [Supabase Tutorial](../supabase-tutorial/)
+- [RagFlow Tutorial](../ragflow-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: React Hooks and Live Local-First UX`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: React Hooks and Live Local-First UX
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: React Hooks and Live Local-First UX` as an operating subsystem inside **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: React Hooks and Live Local-First UX` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Fireproof Repository](https://github.com/fireproof-storage/fireproof)
+  Why it matters: authoritative reference on `Fireproof Repository` (github.com).
+- [Fireproof README](https://github.com/fireproof-storage/fireproof/blob/main/README.md)
+  Why it matters: authoritative reference on `Fireproof README` (github.com).
+- [Database implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/database.ts)
+  Why it matters: authoritative reference on `Database implementation` (github.com).
+- [Ledger implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/ledger.ts)
+  Why it matters: authoritative reference on `Ledger implementation` (github.com).
+- [IndexedDB gateway](https://github.com/fireproof-storage/fireproof/blob/main/core/gateways/indexeddb/gateway-impl.ts)
+  Why it matters: authoritative reference on `IndexedDB gateway` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Core Document API and Query Lifecycle](02-core-document-api-and-query-lifecycle.md)
+- [Next Chapter: Chapter 4: Ledger, CRDT, and Causal Consistency](04-ledger-crdt-and-causal-consistency.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fireproof-tutorial/04-ledger-crdt-and-causal-consistency.md b/tutorials/fireproof-tutorial/04-ledger-crdt-and-causal-consistency.md
index c6eeee06..d6e34dae 100644
--- a/tutorials/fireproof-tutorial/04-ledger-crdt-and-causal-consistency.md
+++ b/tutorials/fireproof-tutorial/04-ledger-crdt-and-causal-consistency.md
@@ -7,6 +7,9 @@ parent: Fireproof Tutorial
 
 # Chapter 4: Ledger, CRDT, and Causal Consistency
 
+Welcome to **Chapter 4: Ledger, CRDT, and Causal Consistency**. In this part of **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Fireproof uses ledger and CRDT structures to preserve causal order and integrity across concurrent writers.
 
 ## Integrity Model
@@ -30,3 +33,610 @@ In collaborative or AI-assisted editing scenarios, this reduces the chance of si
 You now understand the core consistency model behind Fireproof write and merge behavior.
 
 Next: [Chapter 5: Storage Gateways and Sync Topology](05-storage-gateways-and-sync-topology.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- tutorial slug: **fireproof-tutorial**
+- chapter focus: **Chapter 4: Ledger, CRDT, and Causal Consistency**
+- system context: **Fireproof Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Ledger, CRDT, and Causal Consistency`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Fireproof Repository](https://github.com/fireproof-storage/fireproof)
+- [Fireproof README](https://github.com/fireproof-storage/fireproof/blob/main/README.md)
+- [Database implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/database.ts)
+- [Ledger implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/ledger.ts)
+- [IndexedDB gateway](https://github.com/fireproof-storage/fireproof/blob/main/core/gateways/indexeddb/gateway-impl.ts)
+
+### Cross-Tutorial Connection Map
+
+- [Supabase Tutorial](../supabase-tutorial/)
+- [RagFlow Tutorial](../ragflow-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Ledger, CRDT, and Causal Consistency`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 4: Ledger, CRDT, and Causal Consistency
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Ledger, CRDT, and Causal Consistency` as an operating subsystem inside **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Ledger, CRDT, and Causal Consistency` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Fireproof Repository](https://github.com/fireproof-storage/fireproof)
+  Why it matters: authoritative reference on `Fireproof Repository` (github.com).
+- [Fireproof README](https://github.com/fireproof-storage/fireproof/blob/main/README.md)
+  Why it matters: authoritative reference on `Fireproof README` (github.com).
+- [Database implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/database.ts)
+  Why it matters: authoritative reference on `Database implementation` (github.com).
+- [Ledger implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/ledger.ts)
+  Why it matters: authoritative reference on `Ledger implementation` (github.com).
+- [IndexedDB gateway](https://github.com/fireproof-storage/fireproof/blob/main/core/gateways/indexeddb/gateway-impl.ts)
+  Why it matters: authoritative reference on `IndexedDB gateway` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: React Hooks and Live Local-First UX](03-react-hooks-and-live-local-first-ux.md)
+- [Next Chapter: Chapter 5: Storage Gateways and Sync Topology](05-storage-gateways-and-sync-topology.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fireproof-tutorial/05-storage-gateways-and-sync-topology.md b/tutorials/fireproof-tutorial/05-storage-gateways-and-sync-topology.md
index 618832e5..14e7d5a9 100644
--- a/tutorials/fireproof-tutorial/05-storage-gateways-and-sync-topology.md
+++ b/tutorials/fireproof-tutorial/05-storage-gateways-and-sync-topology.md
@@ -7,6 +7,9 @@ parent: Fireproof Tutorial
 
 # Chapter 5: Storage Gateways and Sync Topology
 
+Welcome to **Chapter 5: Storage Gateways and Sync Topology**. In this part of **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Fireproof supports multiple storage gateways and environment-aware persistence paths.
 
 ## Gateway Landscape
@@ -34,3 +37,598 @@ Fireproof supports multiple storage gateways and environment-aware persistence p
 You now have a storage and sync topology model for different deployment targets.
 
 Next: [Chapter 6: Files, Attachments, and Rich Data Flows](06-files-attachments-and-rich-data-flows.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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- tutorial slug: **fireproof-tutorial**
+- chapter focus: **Chapter 5: Storage Gateways and Sync Topology**
+- system context: **Fireproof Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Storage Gateways and Sync Topology`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Fireproof Repository](https://github.com/fireproof-storage/fireproof)
+- [Fireproof README](https://github.com/fireproof-storage/fireproof/blob/main/README.md)
+- [Database implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/database.ts)
+- [Ledger implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/ledger.ts)
+- [IndexedDB gateway](https://github.com/fireproof-storage/fireproof/blob/main/core/gateways/indexeddb/gateway-impl.ts)
+
+### Cross-Tutorial Connection Map
+
+- [Supabase Tutorial](../supabase-tutorial/)
+- [RagFlow Tutorial](../ragflow-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Storage Gateways and Sync Topology`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Storage Gateways and Sync Topology
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Storage Gateways and Sync Topology` as an operating subsystem inside **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Storage Gateways and Sync Topology` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Fireproof Repository](https://github.com/fireproof-storage/fireproof)
+  Why it matters: authoritative reference on `Fireproof Repository` (github.com).
+- [Fireproof README](https://github.com/fireproof-storage/fireproof/blob/main/README.md)
+  Why it matters: authoritative reference on `Fireproof README` (github.com).
+- [Database implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/database.ts)
+  Why it matters: authoritative reference on `Database implementation` (github.com).
+- [Ledger implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/ledger.ts)
+  Why it matters: authoritative reference on `Ledger implementation` (github.com).
+- [IndexedDB gateway](https://github.com/fireproof-storage/fireproof/blob/main/core/gateways/indexeddb/gateway-impl.ts)
+  Why it matters: authoritative reference on `IndexedDB gateway` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Ledger, CRDT, and Causal Consistency](04-ledger-crdt-and-causal-consistency.md)
+- [Next Chapter: Chapter 6: Files, Attachments, and Rich Data Flows](06-files-attachments-and-rich-data-flows.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fireproof-tutorial/06-files-attachments-and-rich-data-flows.md b/tutorials/fireproof-tutorial/06-files-attachments-and-rich-data-flows.md
index 7db790ae..da0ee6c4 100644
--- a/tutorials/fireproof-tutorial/06-files-attachments-and-rich-data-flows.md
+++ b/tutorials/fireproof-tutorial/06-files-attachments-and-rich-data-flows.md
@@ -7,6 +7,9 @@ parent: Fireproof Tutorial
 
 # Chapter 6: Files, Attachments, and Rich Data Flows
 
+Welcome to **Chapter 6: Files, Attachments, and Rich Data Flows**. In this part of **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Fireproof supports richer document payloads through `_files` patterns and helper components like `ImgFile`.
 
 ## Rich Data Pattern
@@ -31,3 +34,610 @@ Fireproof supports richer document payloads through `_files` patterns and helper
 You now understand how to model and render rich media payloads in Fireproof documents.
 
 Next: [Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge](07-runtime-coverage-browser-node-deno-and-edge.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: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- tutorial slug: **fireproof-tutorial**
+- chapter focus: **Chapter 6: Files, Attachments, and Rich Data Flows**
+- system context: **Fireproof Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Files, Attachments, and Rich Data Flows`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Fireproof Repository](https://github.com/fireproof-storage/fireproof)
+- [Fireproof README](https://github.com/fireproof-storage/fireproof/blob/main/README.md)
+- [Database implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/database.ts)
+- [Ledger implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/ledger.ts)
+- [IndexedDB gateway](https://github.com/fireproof-storage/fireproof/blob/main/core/gateways/indexeddb/gateway-impl.ts)
+
+### Cross-Tutorial Connection Map
+
+- [Supabase Tutorial](../supabase-tutorial/)
+- [RagFlow Tutorial](../ragflow-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Files, Attachments, and Rich Data Flows`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 6: Files, Attachments, and Rich Data Flows
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Files, Attachments, and Rich Data Flows` as an operating subsystem inside **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Files, Attachments, and Rich Data Flows` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Fireproof Repository](https://github.com/fireproof-storage/fireproof)
+  Why it matters: authoritative reference on `Fireproof Repository` (github.com).
+- [Fireproof README](https://github.com/fireproof-storage/fireproof/blob/main/README.md)
+  Why it matters: authoritative reference on `Fireproof README` (github.com).
+- [Database implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/database.ts)
+  Why it matters: authoritative reference on `Database implementation` (github.com).
+- [Ledger implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/ledger.ts)
+  Why it matters: authoritative reference on `Ledger implementation` (github.com).
+- [IndexedDB gateway](https://github.com/fireproof-storage/fireproof/blob/main/core/gateways/indexeddb/gateway-impl.ts)
+  Why it matters: authoritative reference on `IndexedDB gateway` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Storage Gateways and Sync Topology](05-storage-gateways-and-sync-topology.md)
+- [Next Chapter: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge](07-runtime-coverage-browser-node-deno-and-edge.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fireproof-tutorial/07-runtime-coverage-browser-node-deno-and-edge.md b/tutorials/fireproof-tutorial/07-runtime-coverage-browser-node-deno-and-edge.md
index 338e2a4e..324a4fff 100644
--- a/tutorials/fireproof-tutorial/07-runtime-coverage-browser-node-deno-and-edge.md
+++ b/tutorials/fireproof-tutorial/07-runtime-coverage-browser-node-deno-and-edge.md
@@ -7,6 +7,9 @@ parent: Fireproof Tutorial
 
 # Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
 
+Welcome to **Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge**. In this part of **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Fireproof is designed for broad JavaScript runtime portability with one API shape.
 
 ## Runtime Strategy
@@ -34,3 +37,598 @@ Fireproof is designed for broad JavaScript runtime portability with one API shap
 You now have a portability model for deploying Fireproof across browser and server contexts.
 
 Next: [Chapter 8: Production Operations, Security, and Debugging](08-production-operations-security-and-debugging.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- tutorial slug: **fireproof-tutorial**
+- chapter focus: **Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge**
+- system context: **Fireproof Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Fireproof Repository](https://github.com/fireproof-storage/fireproof)
+- [Fireproof README](https://github.com/fireproof-storage/fireproof/blob/main/README.md)
+- [Database implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/database.ts)
+- [Ledger implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/ledger.ts)
+- [IndexedDB gateway](https://github.com/fireproof-storage/fireproof/blob/main/core/gateways/indexeddb/gateway-impl.ts)
+
+### Cross-Tutorial Connection Map
+
+- [Supabase Tutorial](../supabase-tutorial/)
+- [RagFlow Tutorial](../ragflow-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge` as an operating subsystem inside **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Fireproof Repository](https://github.com/fireproof-storage/fireproof)
+  Why it matters: authoritative reference on `Fireproof Repository` (github.com).
+- [Fireproof README](https://github.com/fireproof-storage/fireproof/blob/main/README.md)
+  Why it matters: authoritative reference on `Fireproof README` (github.com).
+- [Database implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/database.ts)
+  Why it matters: authoritative reference on `Database implementation` (github.com).
+- [Ledger implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/ledger.ts)
+  Why it matters: authoritative reference on `Ledger implementation` (github.com).
+- [IndexedDB gateway](https://github.com/fireproof-storage/fireproof/blob/main/core/gateways/indexeddb/gateway-impl.ts)
+  Why it matters: authoritative reference on `IndexedDB gateway` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Files, Attachments, and Rich Data Flows](06-files-attachments-and-rich-data-flows.md)
+- [Next Chapter: Chapter 8: Production Operations, Security, and Debugging](08-production-operations-security-and-debugging.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/fireproof-tutorial/08-production-operations-security-and-debugging.md b/tutorials/fireproof-tutorial/08-production-operations-security-and-debugging.md
index b2a3e6f1..a0436846 100644
--- a/tutorials/fireproof-tutorial/08-production-operations-security-and-debugging.md
+++ b/tutorials/fireproof-tutorial/08-production-operations-security-and-debugging.md
@@ -7,6 +7,9 @@ parent: Fireproof Tutorial
 
 # Chapter 8: Production Operations, Security, and Debugging
 
+Welcome to **Chapter 8: Production Operations, Security, and Debugging**. In this part of **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Production Fireproof deployments need explicit practices for observability, key handling, and test discipline.
 
 ## Operations Checklist
@@ -36,3 +39,597 @@ Production Fireproof deployments need explicit practices for observability, key
 ## Summary
 
 You now have a practical baseline for operating Fireproof in production-grade app workflows.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- tutorial slug: **fireproof-tutorial**
+- chapter focus: **Chapter 8: Production Operations, Security, and Debugging**
+- system context: **Fireproof Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Operations, Security, 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
+
+- [Fireproof Repository](https://github.com/fireproof-storage/fireproof)
+- [Fireproof README](https://github.com/fireproof-storage/fireproof/blob/main/README.md)
+- [Database implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/database.ts)
+- [Ledger implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/ledger.ts)
+- [IndexedDB gateway](https://github.com/fireproof-storage/fireproof/blob/main/core/gateways/indexeddb/gateway-impl.ts)
+
+### Cross-Tutorial Connection Map
+
+- [Supabase Tutorial](../supabase-tutorial/)
+- [RagFlow Tutorial](../ragflow-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Plandex Tutorial](../plandex-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Operations, Security, 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 8: Production Operations, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Production Operations, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Operations, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Operations, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Production Operations, Security, and Debugging
+
+- tutorial context: **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Operations, Security, and Debugging` as an operating subsystem inside **Fireproof Tutorial: Local-First Document Database for AI-Native Apps**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Operations, Security, and Debugging` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Fireproof Repository](https://github.com/fireproof-storage/fireproof)
+  Why it matters: authoritative reference on `Fireproof Repository` (github.com).
+- [Fireproof README](https://github.com/fireproof-storage/fireproof/blob/main/README.md)
+  Why it matters: authoritative reference on `Fireproof README` (github.com).
+- [Database implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/database.ts)
+  Why it matters: authoritative reference on `Database implementation` (github.com).
+- [Ledger implementation](https://github.com/fireproof-storage/fireproof/blob/main/core/base/ledger.ts)
+  Why it matters: authoritative reference on `Ledger implementation` (github.com).
+- [IndexedDB gateway](https://github.com/fireproof-storage/fireproof/blob/main/core/gateways/indexeddb/gateway-impl.ts)
+  Why it matters: authoritative reference on `IndexedDB gateway` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Runtime Coverage: Browser, Node, Deno, and Edge](07-runtime-coverage-browser-node-deno-and-edge.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/flowise-llm-orchestration/01-system-overview.md b/tutorials/flowise-llm-orchestration/01-system-overview.md
index 6e1dd4a8..1c12fe89 100644
--- a/tutorials/flowise-llm-orchestration/01-system-overview.md
+++ b/tutorials/flowise-llm-orchestration/01-system-overview.md
@@ -8,6 +8,9 @@ parent: "Flowise LLM Orchestration"
 
 # Chapter 1: Flowise System Overview
 
+Welcome to **Chapter 1: Flowise System Overview**. In this part of **Flowise LLM Orchestration: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Understanding Flowise's visual LLM workflow orchestration platform
 
 ## 🎯 Learning Objectives
@@ -514,4 +517,49 @@ This chapter provided the foundation for understanding Flowise's visual workflow
 
 ---
 
-**Ready to build workflows?** Continue to [Chapter 2: Workflow Engine](02-workflow-engine.md)
\ No newline at end of file
+**Ready to build workflows?** Continue to [Chapter 2: Workflow Engine](02-workflow-engine.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `workflow`, `graph`, `Flowise` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Flowise System Overview` as an operating subsystem inside **Flowise LLM Orchestration: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `participant`, `Response`, `style` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Flowise System Overview` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `workflow`.
+2. **Input normalization**: shape incoming data so `graph` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Flowise`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Flowise](https://github.com/FlowiseAI/Flowise)
+  Why it matters: authoritative reference on `Flowise` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `workflow` and `graph` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Workflow Engine](02-workflow-engine.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/flowise-llm-orchestration/02-workflow-engine.md b/tutorials/flowise-llm-orchestration/02-workflow-engine.md
index 025b543b..d822d93f 100644
--- a/tutorials/flowise-llm-orchestration/02-workflow-engine.md
+++ b/tutorials/flowise-llm-orchestration/02-workflow-engine.md
@@ -8,6 +8,9 @@ parent: "Flowise LLM Orchestration"
 
 # Chapter 2: Workflow Engine
 
+Welcome to **Chapter 2: Workflow Engine**. In this part of **Flowise LLM Orchestration: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deep dive into Flowise's workflow execution architecture and processing model
 
 ## 🎯 Learning Objectives
@@ -708,4 +711,50 @@ interface WorkflowMetrics {
 
 ---
 
-**Ready to create custom nodes?** Continue to [Chapter 3: Node Development](03-node-development.md)
\ No newline at end of file
+**Ready to create custom nodes?** Continue to [Chapter 3: Node Development](03-node-development.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `context`, `nodeId`, `input` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Workflow Engine` as an operating subsystem inside **Flowise LLM Orchestration: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `node`, `nodes`, `workflow` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Workflow Engine` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `context`.
+2. **Input normalization**: shape incoming data so `nodeId` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `input`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Flowise](https://github.com/FlowiseAI/Flowise)
+  Why it matters: authoritative reference on `Flowise` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `context` and `nodeId` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Flowise System Overview](01-system-overview.md)
+- [Next Chapter: Chapter 3: Node Development](03-node-development.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/flowise-llm-orchestration/03-node-development.md b/tutorials/flowise-llm-orchestration/03-node-development.md
index b1776b3a..5cdec508 100644
--- a/tutorials/flowise-llm-orchestration/03-node-development.md
+++ b/tutorials/flowise-llm-orchestration/03-node-development.md
@@ -8,6 +8,9 @@ parent: "Flowise LLM Orchestration"
 
 # Chapter 3: Node Development
 
+Welcome to **Chapter 3: Node Development**. In this part of **Flowise LLM Orchestration: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Creating custom nodes and extending Flowise's capabilities
 
 ## 🎯 Learning Objectives
@@ -921,4 +924,50 @@ interface DebugLog {
 
 ---
 
-**Ready for advanced integrations?** Continue to [Chapter 4: Advanced Integrations](04-advanced-integrations.md)
\ No newline at end of file
+**Ready for advanced integrations?** Continue to [Chapter 4: Advanced Integrations](04-advanced-integrations.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `name`, `label`, `nodeData` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Node Development` as an operating subsystem inside **Flowise LLM Orchestration: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `inputs`, `model`, `node` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Node Development` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `name`.
+2. **Input normalization**: shape incoming data so `label` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `nodeData`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Flowise](https://github.com/FlowiseAI/Flowise)
+  Why it matters: authoritative reference on `Flowise` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `name` and `label` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Workflow Engine](02-workflow-engine.md)
+- [Next Chapter: Chapter 4: Advanced Integrations](04-advanced-integrations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/flowise-llm-orchestration/04-advanced-integrations.md b/tutorials/flowise-llm-orchestration/04-advanced-integrations.md
index 1d4262ff..8c66931c 100644
--- a/tutorials/flowise-llm-orchestration/04-advanced-integrations.md
+++ b/tutorials/flowise-llm-orchestration/04-advanced-integrations.md
@@ -8,6 +8,9 @@ parent: "Flowise LLM Orchestration"
 
 # Chapter 4: Advanced Integrations
 
+Welcome to **Chapter 4: Advanced Integrations**. In this part of **Flowise LLM Orchestration: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Complex multi-provider workflows and enterprise integrations
 
 ## 🎯 Learning Objectives
@@ -1187,4 +1190,50 @@ class EnterpriseAuthNode implements INode {
 
 ---
 
-**Ready for production deployment?** Continue to [Chapter 5: Production Deployment](05-production-deployment.md)
\ No newline at end of file
+**Ready for production deployment?** Continue to [Chapter 5: Production Deployment](05-production-deployment.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `name`, `label`, `case` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Advanced Integrations` as an operating subsystem inside **Flowise LLM Orchestration: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `description`, `requirements`, `provider` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Advanced Integrations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `name`.
+2. **Input normalization**: shape incoming data so `label` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `case`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Flowise](https://github.com/FlowiseAI/Flowise)
+  Why it matters: authoritative reference on `Flowise` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `name` and `label` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Node Development](03-node-development.md)
+- [Next Chapter: Chapter 5: Production Deployment](05-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/flowise-llm-orchestration/05-production-deployment.md b/tutorials/flowise-llm-orchestration/05-production-deployment.md
index b80e06f0..c360f0cf 100644
--- a/tutorials/flowise-llm-orchestration/05-production-deployment.md
+++ b/tutorials/flowise-llm-orchestration/05-production-deployment.md
@@ -8,6 +8,9 @@ parent: "Flowise LLM Orchestration"
 
 # Chapter 5: Production Deployment
 
+Welcome to **Chapter 5: Production Deployment**. In this part of **Flowise LLM Orchestration: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Scaling, monitoring, and maintaining Flowise applications in production
 
 ## 🎯 Learning Objectives
@@ -1104,4 +1107,50 @@ class UsageMonitor {
 - **Advanced Monitoring**: Implement comprehensive observability and alerting
 - **Cost Optimization**: Monitor and optimize AI workflow costs at scale
 
-**Happy orchestrating! 🤖✨**
\ No newline at end of file
+**Happy orchestrating! 🤖✨**
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `flowise`, `name`, `usage` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Production Deployment` as an operating subsystem inside **Flowise LLM Orchestration: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `workflowId`, `production`, `metadata` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `flowise`.
+2. **Input normalization**: shape incoming data so `name` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `usage`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Flowise](https://github.com/FlowiseAI/Flowise)
+  Why it matters: authoritative reference on `Flowise` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `flowise` and `name` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Advanced Integrations](04-advanced-integrations.md)
+- [Next Chapter: Chapter 6: Security and Governance](06-security-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/flowise-llm-orchestration/06-security-governance.md b/tutorials/flowise-llm-orchestration/06-security-governance.md
index 30f94e67..41b62a7c 100644
--- a/tutorials/flowise-llm-orchestration/06-security-governance.md
+++ b/tutorials/flowise-llm-orchestration/06-security-governance.md
@@ -8,6 +8,9 @@ parent: "Flowise LLM Orchestration"
 
 # Chapter 6: Security and Governance
 
+Welcome to **Chapter 6: Security and Governance**. In this part of **Flowise LLM Orchestration: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Flowise workflows combine models, tools, connectors, and credentials. Governance must be explicit.
 
 ## Primary Risk Areas
@@ -55,3 +58,49 @@ Maintain playbooks for:
 You now have a practical security and governance baseline for operating Flowise in production.
 
 Next: [Chapter 7: Observability](07-observability.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Security and Governance` as an operating subsystem inside **Flowise LLM Orchestration: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Security and Governance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Flowise](https://github.com/FlowiseAI/Flowise)
+  Why it matters: authoritative reference on `Flowise` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Security` and `and` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Production Deployment](05-production-deployment.md)
+- [Next Chapter: Chapter 7: Observability](07-observability.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/flowise-llm-orchestration/07-observability.md b/tutorials/flowise-llm-orchestration/07-observability.md
index 64601c2d..5420876c 100644
--- a/tutorials/flowise-llm-orchestration/07-observability.md
+++ b/tutorials/flowise-llm-orchestration/07-observability.md
@@ -8,6 +8,9 @@ parent: "Flowise LLM Orchestration"
 
 # Chapter 7: Observability
 
+Welcome to **Chapter 7: Observability**. In this part of **Flowise LLM Orchestration: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Observability turns visual workflow orchestration into measurable production behavior.
 
 ## Metrics Baseline
@@ -52,3 +55,49 @@ This allows fast root-cause analysis for partial failures.
 You can now instrument Flowise workflows to debug incidents quickly and manage performance/cost predictably.
 
 Next: [Chapter 8: Extension Ecosystem](08-extension-ecosystem.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Observability` as an operating subsystem inside **Flowise LLM Orchestration: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Observability` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Flowise](https://github.com/FlowiseAI/Flowise)
+  Why it matters: authoritative reference on `Flowise` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Observability` and `Observability` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Security and Governance](06-security-governance.md)
+- [Next Chapter: Chapter 8: Extension Ecosystem](08-extension-ecosystem.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/flowise-llm-orchestration/08-extension-ecosystem.md b/tutorials/flowise-llm-orchestration/08-extension-ecosystem.md
index ebec738f..40c5efda 100644
--- a/tutorials/flowise-llm-orchestration/08-extension-ecosystem.md
+++ b/tutorials/flowise-llm-orchestration/08-extension-ecosystem.md
@@ -8,6 +8,9 @@ parent: "Flowise LLM Orchestration"
 
 # Chapter 8: Extension Ecosystem
 
+Welcome to **Chapter 8: Extension Ecosystem**. In this part of **Flowise LLM Orchestration: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 A sustainable extension ecosystem determines whether Flowise remains adaptable as requirements evolve.
 
 ## Extension Design Principles
@@ -45,3 +48,48 @@ You now have a blueprint for building and maintaining a robust Flowise extension
 
 Related:
 - [Flowise Index](index.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Extension Ecosystem` as an operating subsystem inside **Flowise LLM Orchestration: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Extension Ecosystem` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Flowise](https://github.com/FlowiseAI/Flowise)
+  Why it matters: authoritative reference on `Flowise` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Extension` and `Ecosystem` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Observability](07-observability.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gemini-cli-tutorial/01-getting-started.md b/tutorials/gemini-cli-tutorial/01-getting-started.md
index 5ff456a4..98e37536 100644
--- a/tutorials/gemini-cli-tutorial/01-getting-started.md
+++ b/tutorials/gemini-cli-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Gemini CLI Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets Gemini CLI running quickly and validates first successful interactions.
 
 ## Learning Goals
@@ -68,3 +71,577 @@ gemini -p "List top risks in this codebase" --output-format json
 You now have a working Gemini CLI baseline for both interactive and scripted usage.
 
 Next: [Chapter 2: Architecture, Tools, and Agent Loop](02-architecture-tools-and-agent-loop.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- tutorial slug: **gemini-cli-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Gemini Cli 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
+
+- [Gemini CLI Repository](https://github.com/google-gemini/gemini-cli)
+- [Gemini CLI README](https://github.com/google-gemini/gemini-cli/blob/main/README.md)
+- [Gemini CLI Docs](https://geminicli.com/docs)
+- [CLI Commands Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/commands.md)
+- [MCP Integration Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/tools/mcp-server.md)
+
+### Cross-Tutorial Connection Map
+
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Claude Code Tutorial](../claude-code-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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `gemini`, `google`, `install` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `brew`, `Summarize`, `repository` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `gemini`.
+2. **Input normalization**: shape incoming data so `google` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `install`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Gemini CLI Repository](https://github.com/google-gemini/gemini-cli)
+  Why it matters: authoritative reference on `Gemini CLI Repository` (github.com).
+- [Gemini CLI README](https://github.com/google-gemini/gemini-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Gemini CLI README` (github.com).
+- [Gemini CLI Docs](https://geminicli.com/docs)
+  Why it matters: authoritative reference on `Gemini CLI Docs` (geminicli.com).
+- [CLI Commands Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/commands.md)
+  Why it matters: authoritative reference on `CLI Commands Docs` (github.com).
+- [MCP Integration Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/tools/mcp-server.md)
+  Why it matters: authoritative reference on `MCP Integration Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `gemini` and `google` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Architecture, Tools, and Agent Loop](02-architecture-tools-and-agent-loop.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gemini-cli-tutorial/02-architecture-tools-and-agent-loop.md b/tutorials/gemini-cli-tutorial/02-architecture-tools-and-agent-loop.md
index 8c64fefb..6aa84875 100644
--- a/tutorials/gemini-cli-tutorial/02-architecture-tools-and-agent-loop.md
+++ b/tutorials/gemini-cli-tutorial/02-architecture-tools-and-agent-loop.md
@@ -7,6 +7,9 @@ parent: Gemini CLI Tutorial
 
 # Chapter 2: Architecture, Tools, and Agent Loop
 
+Welcome to **Chapter 2: Architecture, Tools, and Agent Loop**. In this part of **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains the core runtime model that turns prompts into tool-backed outputs.
 
 ## Learning Goals
@@ -51,3 +54,590 @@ flowchart TD
 You now have a strong mental model of Gemini CLI execution internals.
 
 Next: [Chapter 3: Authentication and Model Access Strategy](03-authentication-and-model-access-strategy.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- tutorial slug: **gemini-cli-tutorial**
+- chapter focus: **Chapter 2: Architecture, Tools, and Agent Loop**
+- system context: **Gemini Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Architecture, Tools, and Agent Loop`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Gemini CLI Repository](https://github.com/google-gemini/gemini-cli)
+- [Gemini CLI README](https://github.com/google-gemini/gemini-cli/blob/main/README.md)
+- [Gemini CLI Docs](https://geminicli.com/docs)
+- [CLI Commands Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/commands.md)
+- [MCP Integration Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/tools/mcp-server.md)
+
+### Cross-Tutorial Connection Map
+
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Claude Code Tutorial](../claude-code-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: Architecture, Tools, and Agent Loop`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Architecture, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Architecture, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Architecture, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Architecture, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Architecture, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Architecture, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Architecture, Tools, and Agent Loop
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Tool`, `flowchart`, `User` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Architecture, Tools, and Agent Loop` as an operating subsystem inside **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `prompt`, `slash`, `command` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Architecture, Tools, and Agent Loop` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Tool`.
+2. **Input normalization**: shape incoming data so `flowchart` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `User`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Gemini CLI Repository](https://github.com/google-gemini/gemini-cli)
+  Why it matters: authoritative reference on `Gemini CLI Repository` (github.com).
+- [Gemini CLI README](https://github.com/google-gemini/gemini-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Gemini CLI README` (github.com).
+- [Gemini CLI Docs](https://geminicli.com/docs)
+  Why it matters: authoritative reference on `Gemini CLI Docs` (geminicli.com).
+- [CLI Commands Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/commands.md)
+  Why it matters: authoritative reference on `CLI Commands Docs` (github.com).
+- [MCP Integration Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/tools/mcp-server.md)
+  Why it matters: authoritative reference on `MCP Integration Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Tool` and `flowchart` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Authentication and Model Access Strategy](03-authentication-and-model-access-strategy.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gemini-cli-tutorial/03-authentication-and-model-access-strategy.md b/tutorials/gemini-cli-tutorial/03-authentication-and-model-access-strategy.md
index 43964482..e60d82ab 100644
--- a/tutorials/gemini-cli-tutorial/03-authentication-and-model-access-strategy.md
+++ b/tutorials/gemini-cli-tutorial/03-authentication-and-model-access-strategy.md
@@ -7,6 +7,9 @@ parent: Gemini CLI Tutorial
 
 # Chapter 3: Authentication and Model Access Strategy
 
+Welcome to **Chapter 3: Authentication and Model Access Strategy**. In this part of **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter compares available auth paths and helps you choose model-access strategy by team constraints.
 
 ## Learning Goals
@@ -62,3 +65,578 @@ gemini
 You now have a clear and repeatable auth/model-access strategy.
 
 Next: [Chapter 4: Settings, Context, and Custom Commands](04-settings-context-and-custom-commands.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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- tutorial slug: **gemini-cli-tutorial**
+- chapter focus: **Chapter 3: Authentication and Model Access Strategy**
+- system context: **Gemini Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Authentication and Model Access Strategy`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Gemini CLI Repository](https://github.com/google-gemini/gemini-cli)
+- [Gemini CLI README](https://github.com/google-gemini/gemini-cli/blob/main/README.md)
+- [Gemini CLI Docs](https://geminicli.com/docs)
+- [CLI Commands Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/commands.md)
+- [MCP Integration Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/tools/mcp-server.md)
+
+### Cross-Tutorial Connection Map
+
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Claude Code Tutorial](../claude-code-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: Authentication and Model Access Strategy`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Authentication and Model Access Strategy
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `gemini`, `YOUR_API_KEY` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Authentication and Model Access Strategy` as an operating subsystem inside **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Authentication and Model Access Strategy` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `gemini`.
+2. **Input normalization**: shape incoming data so `YOUR_API_KEY` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Gemini CLI Repository](https://github.com/google-gemini/gemini-cli)
+  Why it matters: authoritative reference on `Gemini CLI Repository` (github.com).
+- [Gemini CLI README](https://github.com/google-gemini/gemini-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Gemini CLI README` (github.com).
+- [Gemini CLI Docs](https://geminicli.com/docs)
+  Why it matters: authoritative reference on `Gemini CLI Docs` (geminicli.com).
+- [CLI Commands Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/commands.md)
+  Why it matters: authoritative reference on `CLI Commands Docs` (github.com).
+- [MCP Integration Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/tools/mcp-server.md)
+  Why it matters: authoritative reference on `MCP Integration Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `gemini` and `YOUR_API_KEY` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Architecture, Tools, and Agent Loop](02-architecture-tools-and-agent-loop.md)
+- [Next Chapter: Chapter 4: Settings, Context, and Custom Commands](04-settings-context-and-custom-commands.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gemini-cli-tutorial/04-settings-context-and-custom-commands.md b/tutorials/gemini-cli-tutorial/04-settings-context-and-custom-commands.md
index ef63266f..29af6e10 100644
--- a/tutorials/gemini-cli-tutorial/04-settings-context-and-custom-commands.md
+++ b/tutorials/gemini-cli-tutorial/04-settings-context-and-custom-commands.md
@@ -7,6 +7,9 @@ parent: Gemini CLI Tutorial
 
 # Chapter 4: Settings, Context, and Custom Commands
 
+Welcome to **Chapter 4: Settings, Context, and Custom Commands**. In this part of **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on the highest-leverage configuration surfaces for consistent team behavior.
 
 ## Learning Goals
@@ -49,3 +52,586 @@ Benefits:
 You now know how to codify Gemini CLI behavior with durable settings and commands.
 
 Next: [Chapter 5: MCP, Extensions, and Skills](05-mcp-extensions-and-skills.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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- tutorial slug: **gemini-cli-tutorial**
+- chapter focus: **Chapter 4: Settings, Context, and Custom Commands**
+- system context: **Gemini Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Settings, Context, and Custom Commands`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Gemini CLI Repository](https://github.com/google-gemini/gemini-cli)
+- [Gemini CLI README](https://github.com/google-gemini/gemini-cli/blob/main/README.md)
+- [Gemini CLI Docs](https://geminicli.com/docs)
+- [CLI Commands Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/commands.md)
+- [MCP Integration Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/tools/mcp-server.md)
+
+### Cross-Tutorial Connection Map
+
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Claude Code Tutorial](../claude-code-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: Settings, Context, and Custom Commands`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Settings, Context, and Custom Commands
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Settings, Context, and Custom Commands` as an operating subsystem inside **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Settings, Context, and Custom Commands` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Gemini CLI Repository](https://github.com/google-gemini/gemini-cli)
+  Why it matters: authoritative reference on `Gemini CLI Repository` (github.com).
+- [Gemini CLI README](https://github.com/google-gemini/gemini-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Gemini CLI README` (github.com).
+- [Gemini CLI Docs](https://geminicli.com/docs)
+  Why it matters: authoritative reference on `Gemini CLI Docs` (geminicli.com).
+- [CLI Commands Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/commands.md)
+  Why it matters: authoritative reference on `CLI Commands Docs` (github.com).
+- [MCP Integration Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/tools/mcp-server.md)
+  Why it matters: authoritative reference on `MCP Integration Docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Authentication and Model Access Strategy](03-authentication-and-model-access-strategy.md)
+- [Next Chapter: Chapter 5: MCP, Extensions, and Skills](05-mcp-extensions-and-skills.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gemini-cli-tutorial/05-mcp-extensions-and-skills.md b/tutorials/gemini-cli-tutorial/05-mcp-extensions-and-skills.md
index a41a9c88..86c21f40 100644
--- a/tutorials/gemini-cli-tutorial/05-mcp-extensions-and-skills.md
+++ b/tutorials/gemini-cli-tutorial/05-mcp-extensions-and-skills.md
@@ -7,6 +7,9 @@ parent: Gemini CLI Tutorial
 
 # Chapter 5: MCP, Extensions, and Skills
 
+Welcome to **Chapter 5: MCP, Extensions, and Skills**. In this part of **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers extensibility through MCP servers, extension packs, and skills.
 
 ## Learning Goals
@@ -47,3 +50,586 @@ Configure MCP servers in Gemini settings and verify discovery:
 You now have an extensibility model that balances capability and control.
 
 Next: [Chapter 6: Headless Mode and CI Automation](06-headless-mode-and-ci-automation.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- tutorial slug: **gemini-cli-tutorial**
+- chapter focus: **Chapter 5: MCP, Extensions, and Skills**
+- system context: **Gemini Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: MCP, Extensions, and 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
+
+- [Gemini CLI Repository](https://github.com/google-gemini/gemini-cli)
+- [Gemini CLI README](https://github.com/google-gemini/gemini-cli/blob/main/README.md)
+- [Gemini CLI Docs](https://geminicli.com/docs)
+- [CLI Commands Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/commands.md)
+- [MCP Integration Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/tools/mcp-server.md)
+
+### Cross-Tutorial Connection Map
+
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Claude Code Tutorial](../claude-code-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: MCP, Extensions, and 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: MCP, Extensions, and Skills
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: MCP, Extensions, and Skills` as an operating subsystem inside **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: MCP, Extensions, and Skills` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Gemini CLI Repository](https://github.com/google-gemini/gemini-cli)
+  Why it matters: authoritative reference on `Gemini CLI Repository` (github.com).
+- [Gemini CLI README](https://github.com/google-gemini/gemini-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Gemini CLI README` (github.com).
+- [Gemini CLI Docs](https://geminicli.com/docs)
+  Why it matters: authoritative reference on `Gemini CLI Docs` (geminicli.com).
+- [CLI Commands Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/commands.md)
+  Why it matters: authoritative reference on `CLI Commands Docs` (github.com).
+- [MCP Integration Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/tools/mcp-server.md)
+  Why it matters: authoritative reference on `MCP Integration Docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Settings, Context, and Custom Commands](04-settings-context-and-custom-commands.md)
+- [Next Chapter: Chapter 6: Headless Mode and CI Automation](06-headless-mode-and-ci-automation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gemini-cli-tutorial/06-headless-mode-and-ci-automation.md b/tutorials/gemini-cli-tutorial/06-headless-mode-and-ci-automation.md
index c1e2c6b5..9b979c05 100644
--- a/tutorials/gemini-cli-tutorial/06-headless-mode-and-ci-automation.md
+++ b/tutorials/gemini-cli-tutorial/06-headless-mode-and-ci-automation.md
@@ -7,6 +7,9 @@ parent: Gemini CLI Tutorial
 
 # Chapter 6: Headless Mode and CI Automation
 
+Welcome to **Chapter 6: Headless Mode and CI Automation**. In this part of **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter shows how to run Gemini CLI in deterministic automation loops.
 
 ## Learning Goals
@@ -53,3 +56,590 @@ gemini -p "Run release checklist" --output-format stream-json
 You now have practical patterns for scriptable and CI-safe Gemini CLI execution.
 
 Next: [Chapter 7: Sandboxing, Security, and Troubleshooting](07-sandboxing-security-and-troubleshooting.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: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- tutorial slug: **gemini-cli-tutorial**
+- chapter focus: **Chapter 6: Headless Mode and CI Automation**
+- system context: **Gemini Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Headless Mode and CI Automation`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Gemini CLI Repository](https://github.com/google-gemini/gemini-cli)
+- [Gemini CLI README](https://github.com/google-gemini/gemini-cli/blob/main/README.md)
+- [Gemini CLI Docs](https://geminicli.com/docs)
+- [CLI Commands Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/commands.md)
+- [MCP Integration Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/tools/mcp-server.md)
+
+### Cross-Tutorial Connection Map
+
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Claude Code Tutorial](../claude-code-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: Headless Mode and CI Automation`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Headless Mode and CI Automation
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `gemini`, `output`, `format` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Headless Mode and CI Automation` as an operating subsystem inside **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `json`, `Generate`, `changelog` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Headless Mode and CI Automation` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `gemini`.
+2. **Input normalization**: shape incoming data so `output` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `format`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Gemini CLI Repository](https://github.com/google-gemini/gemini-cli)
+  Why it matters: authoritative reference on `Gemini CLI Repository` (github.com).
+- [Gemini CLI README](https://github.com/google-gemini/gemini-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Gemini CLI README` (github.com).
+- [Gemini CLI Docs](https://geminicli.com/docs)
+  Why it matters: authoritative reference on `Gemini CLI Docs` (geminicli.com).
+- [CLI Commands Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/commands.md)
+  Why it matters: authoritative reference on `CLI Commands Docs` (github.com).
+- [MCP Integration Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/tools/mcp-server.md)
+  Why it matters: authoritative reference on `MCP Integration Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `gemini` and `output` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: MCP, Extensions, and Skills](05-mcp-extensions-and-skills.md)
+- [Next Chapter: Chapter 7: Sandboxing, Security, and Troubleshooting](07-sandboxing-security-and-troubleshooting.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gemini-cli-tutorial/07-sandboxing-security-and-troubleshooting.md b/tutorials/gemini-cli-tutorial/07-sandboxing-security-and-troubleshooting.md
index e0c0923d..0a24812e 100644
--- a/tutorials/gemini-cli-tutorial/07-sandboxing-security-and-troubleshooting.md
+++ b/tutorials/gemini-cli-tutorial/07-sandboxing-security-and-troubleshooting.md
@@ -7,6 +7,9 @@ parent: Gemini CLI Tutorial
 
 # Chapter 7: Sandboxing, Security, and Troubleshooting
 
+Welcome to **Chapter 7: Sandboxing, Security, and Troubleshooting**. In this part of **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on safe execution and common failure recovery.
 
 ## Learning Goals
@@ -48,3 +51,586 @@ Gemini CLI supports host and containerized approaches depending on platform cons
 You now have a reliability and risk-control playbook for Gemini CLI operations.
 
 Next: [Chapter 8: Contribution Workflow and Enterprise Operations](08-contribution-workflow-and-enterprise-operations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- tutorial slug: **gemini-cli-tutorial**
+- chapter focus: **Chapter 7: Sandboxing, Security, and Troubleshooting**
+- system context: **Gemini Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Sandboxing, Security, and Troubleshooting`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Gemini CLI Repository](https://github.com/google-gemini/gemini-cli)
+- [Gemini CLI README](https://github.com/google-gemini/gemini-cli/blob/main/README.md)
+- [Gemini CLI Docs](https://geminicli.com/docs)
+- [CLI Commands Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/commands.md)
+- [MCP Integration Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/tools/mcp-server.md)
+
+### Cross-Tutorial Connection Map
+
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Claude Code Tutorial](../claude-code-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: Sandboxing, Security, and Troubleshooting`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Sandboxing, Security, and Troubleshooting
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Sandboxing, Security, and Troubleshooting` as an operating subsystem inside **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Sandboxing, Security, and Troubleshooting` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Gemini CLI Repository](https://github.com/google-gemini/gemini-cli)
+  Why it matters: authoritative reference on `Gemini CLI Repository` (github.com).
+- [Gemini CLI README](https://github.com/google-gemini/gemini-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Gemini CLI README` (github.com).
+- [Gemini CLI Docs](https://geminicli.com/docs)
+  Why it matters: authoritative reference on `Gemini CLI Docs` (geminicli.com).
+- [CLI Commands Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/commands.md)
+  Why it matters: authoritative reference on `CLI Commands Docs` (github.com).
+- [MCP Integration Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/tools/mcp-server.md)
+  Why it matters: authoritative reference on `MCP Integration Docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Headless Mode and CI Automation](06-headless-mode-and-ci-automation.md)
+- [Next Chapter: Chapter 8: Contribution Workflow and Enterprise Operations](08-contribution-workflow-and-enterprise-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gemini-cli-tutorial/08-contribution-workflow-and-enterprise-operations.md b/tutorials/gemini-cli-tutorial/08-contribution-workflow-and-enterprise-operations.md
index fd1236f4..2c0308f1 100644
--- a/tutorials/gemini-cli-tutorial/08-contribution-workflow-and-enterprise-operations.md
+++ b/tutorials/gemini-cli-tutorial/08-contribution-workflow-and-enterprise-operations.md
@@ -7,6 +7,9 @@ parent: Gemini CLI Tutorial
 
 # Chapter 8: Contribution Workflow and Enterprise Operations
 
+Welcome to **Chapter 8: Contribution Workflow and Enterprise Operations**. In this part of **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers contribution mechanics and team-scale operating patterns.
 
 ## Learning Goals
@@ -44,3 +47,597 @@ Next steps:
 - standardize your team settings and command templates
 - run pilot automation in headless mode with strict output contracts
 - contribute one focused improvement with tests and docs
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- tutorial slug: **gemini-cli-tutorial**
+- chapter focus: **Chapter 8: Contribution Workflow and Enterprise Operations**
+- system context: **Gemini Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Contribution Workflow and 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
+
+- [Gemini CLI Repository](https://github.com/google-gemini/gemini-cli)
+- [Gemini CLI README](https://github.com/google-gemini/gemini-cli/blob/main/README.md)
+- [Gemini CLI Docs](https://geminicli.com/docs)
+- [CLI Commands Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/commands.md)
+- [MCP Integration Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/tools/mcp-server.md)
+
+### Cross-Tutorial Connection Map
+
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Claude Code Tutorial](../claude-code-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: Contribution Workflow and 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: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Contribution Workflow and Enterprise Operations
+
+- tutorial context: **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Contribution Workflow and Enterprise Operations` as an operating subsystem inside **Gemini CLI Tutorial: Terminal-First Agent Workflows with Google Gemini**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Contribution Workflow and Enterprise Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Gemini CLI Repository](https://github.com/google-gemini/gemini-cli)
+  Why it matters: authoritative reference on `Gemini CLI Repository` (github.com).
+- [Gemini CLI README](https://github.com/google-gemini/gemini-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Gemini CLI README` (github.com).
+- [Gemini CLI Docs](https://geminicli.com/docs)
+  Why it matters: authoritative reference on `Gemini CLI Docs` (geminicli.com).
+- [CLI Commands Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/cli/commands.md)
+  Why it matters: authoritative reference on `CLI Commands Docs` (github.com).
+- [MCP Integration Docs](https://github.com/google-gemini/gemini-cli/blob/main/docs/tools/mcp-server.md)
+  Why it matters: authoritative reference on `MCP Integration Docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Sandboxing, Security, and Troubleshooting](07-sandboxing-security-and-troubleshooting.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/genai-toolbox-tutorial/01-getting-started.md b/tutorials/genai-toolbox-tutorial/01-getting-started.md
index 1bb4386a..19add24a 100644
--- a/tutorials/genai-toolbox-tutorial/01-getting-started.md
+++ b/tutorials/genai-toolbox-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: GenAI Toolbox Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets a local Toolbox instance running against a real database quickly.
 
 ## Learning Goals
@@ -35,3 +38,606 @@ This chapter gets a local Toolbox instance running against a real database quick
 You now have a validated local loop for running and invoking Toolbox tools.
 
 Next: [Chapter 2: Architecture and Control Plane](02-architecture-and-control-plane.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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- tutorial slug: **genai-toolbox-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Genai Toolbox 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
+
+- [GenAI Toolbox Repository](https://github.com/googleapis/genai-toolbox)
+- [README](https://github.com/googleapis/genai-toolbox/blob/main/README.md)
+- [Configuration Guide](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/configure.md)
+- [Python Local Quickstart](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/local_quickstart.md)
+- [Connect via MCP](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/connect_via_mcp.md)
+- [CLI Reference](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/reference/cli.md)
+- [Deploy with Docker Compose](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/deploy_docker.md)
+- [Developer Guide](https://github.com/googleapis/genai-toolbox/blob/main/DEVELOPER.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [FastMCP Tutorial](../fastmcp-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [awslabs/mcp Tutorial](../awslabs-mcp-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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GenAI Toolbox Repository](https://github.com/googleapis/genai-toolbox)
+  Why it matters: authoritative reference on `GenAI Toolbox Repository` (github.com).
+- [README](https://github.com/googleapis/genai-toolbox/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Configuration Guide](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/configure.md)
+  Why it matters: authoritative reference on `Configuration Guide` (github.com).
+- [Python Local Quickstart](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/local_quickstart.md)
+  Why it matters: authoritative reference on `Python Local Quickstart` (github.com).
+- [Connect via MCP](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/connect_via_mcp.md)
+  Why it matters: authoritative reference on `Connect via MCP` (github.com).
+- [CLI Reference](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/reference/cli.md)
+  Why it matters: authoritative reference on `CLI Reference` (github.com).
+- [Deploy with Docker Compose](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/deploy_docker.md)
+  Why it matters: authoritative reference on `Deploy with Docker Compose` (github.com).
+- [Developer Guide](https://github.com/googleapis/genai-toolbox/blob/main/DEVELOPER.md)
+  Why it matters: authoritative reference on `Developer Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Architecture and Control Plane](02-architecture-and-control-plane.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/genai-toolbox-tutorial/02-architecture-and-control-plane.md b/tutorials/genai-toolbox-tutorial/02-architecture-and-control-plane.md
index 5f093cb6..054d86e5 100644
--- a/tutorials/genai-toolbox-tutorial/02-architecture-and-control-plane.md
+++ b/tutorials/genai-toolbox-tutorial/02-architecture-and-control-plane.md
@@ -7,6 +7,9 @@ parent: GenAI Toolbox Tutorial
 
 # Chapter 2: Architecture and Control Plane
 
+Welcome to **Chapter 2: Architecture and Control Plane**. In this part of **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains how Toolbox sits between agent frameworks and data systems.
 
 ## Learning Goals
@@ -30,3 +33,607 @@ Toolbox centralizes source and tool definitions, then exposes them to clients th
 You now understand how Toolbox provides a reusable orchestration layer for database-aware agents.
 
 Next: [Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts](03-tools-yaml-sources-tools-toolsets-prompts.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: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- tutorial slug: **genai-toolbox-tutorial**
+- chapter focus: **Chapter 2: Architecture and Control Plane**
+- system context: **Genai Toolbox Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Architecture and Control Plane`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [GenAI Toolbox Repository](https://github.com/googleapis/genai-toolbox)
+- [README](https://github.com/googleapis/genai-toolbox/blob/main/README.md)
+- [Configuration Guide](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/configure.md)
+- [Python Local Quickstart](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/local_quickstart.md)
+- [Connect via MCP](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/connect_via_mcp.md)
+- [CLI Reference](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/reference/cli.md)
+- [Deploy with Docker Compose](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/deploy_docker.md)
+- [Developer Guide](https://github.com/googleapis/genai-toolbox/blob/main/DEVELOPER.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [FastMCP Tutorial](../fastmcp-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [awslabs/mcp Tutorial](../awslabs-mcp-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Architecture and Control Plane`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Architecture and Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Architecture and Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Architecture and Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Architecture and Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Architecture and Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Architecture and Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Architecture and Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Architecture and Control Plane
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Architecture and Control Plane` as an operating subsystem inside **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Architecture and Control Plane` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GenAI Toolbox Repository](https://github.com/googleapis/genai-toolbox)
+  Why it matters: authoritative reference on `GenAI Toolbox Repository` (github.com).
+- [README](https://github.com/googleapis/genai-toolbox/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Configuration Guide](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/configure.md)
+  Why it matters: authoritative reference on `Configuration Guide` (github.com).
+- [Python Local Quickstart](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/local_quickstart.md)
+  Why it matters: authoritative reference on `Python Local Quickstart` (github.com).
+- [Connect via MCP](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/connect_via_mcp.md)
+  Why it matters: authoritative reference on `Connect via MCP` (github.com).
+- [CLI Reference](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/reference/cli.md)
+  Why it matters: authoritative reference on `CLI Reference` (github.com).
+- [Deploy with Docker Compose](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/deploy_docker.md)
+  Why it matters: authoritative reference on `Deploy with Docker Compose` (github.com).
+- [Developer Guide](https://github.com/googleapis/genai-toolbox/blob/main/DEVELOPER.md)
+  Why it matters: authoritative reference on `Developer Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts](03-tools-yaml-sources-tools-toolsets-prompts.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/genai-toolbox-tutorial/03-tools-yaml-sources-tools-toolsets-prompts.md b/tutorials/genai-toolbox-tutorial/03-tools-yaml-sources-tools-toolsets-prompts.md
index 87da8c37..911b1876 100644
--- a/tutorials/genai-toolbox-tutorial/03-tools-yaml-sources-tools-toolsets-prompts.md
+++ b/tutorials/genai-toolbox-tutorial/03-tools-yaml-sources-tools-toolsets-prompts.md
@@ -7,6 +7,9 @@ parent: GenAI Toolbox Tutorial
 
 # Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
 
+Welcome to **Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts**. In this part of **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on building maintainable configuration contracts in `tools.yaml`.
 
 ## Learning Goals
@@ -30,3 +33,607 @@ Treat `tools.yaml` as a versioned interface contract. Keep it small, explicit, a
 You can now design `tools.yaml` schemas that stay readable and stable as capabilities grow.
 
 Next: [Chapter 4: MCP Connectivity and Client Integration](04-mcp-connectivity-and-client-integration.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- tutorial slug: **genai-toolbox-tutorial**
+- chapter focus: **Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts**
+- system context: **Genai Toolbox Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [GenAI Toolbox Repository](https://github.com/googleapis/genai-toolbox)
+- [README](https://github.com/googleapis/genai-toolbox/blob/main/README.md)
+- [Configuration Guide](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/configure.md)
+- [Python Local Quickstart](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/local_quickstart.md)
+- [Connect via MCP](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/connect_via_mcp.md)
+- [CLI Reference](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/reference/cli.md)
+- [Deploy with Docker Compose](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/deploy_docker.md)
+- [Developer Guide](https://github.com/googleapis/genai-toolbox/blob/main/DEVELOPER.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [FastMCP Tutorial](../fastmcp-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [awslabs/mcp Tutorial](../awslabs-mcp-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts` as an operating subsystem inside **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GenAI Toolbox Repository](https://github.com/googleapis/genai-toolbox)
+  Why it matters: authoritative reference on `GenAI Toolbox Repository` (github.com).
+- [README](https://github.com/googleapis/genai-toolbox/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Configuration Guide](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/configure.md)
+  Why it matters: authoritative reference on `Configuration Guide` (github.com).
+- [Python Local Quickstart](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/local_quickstart.md)
+  Why it matters: authoritative reference on `Python Local Quickstart` (github.com).
+- [Connect via MCP](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/connect_via_mcp.md)
+  Why it matters: authoritative reference on `Connect via MCP` (github.com).
+- [CLI Reference](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/reference/cli.md)
+  Why it matters: authoritative reference on `CLI Reference` (github.com).
+- [Deploy with Docker Compose](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/deploy_docker.md)
+  Why it matters: authoritative reference on `Deploy with Docker Compose` (github.com).
+- [Developer Guide](https://github.com/googleapis/genai-toolbox/blob/main/DEVELOPER.md)
+  Why it matters: authoritative reference on `Developer Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Architecture and Control Plane](02-architecture-and-control-plane.md)
+- [Next Chapter: Chapter 4: MCP Connectivity and Client Integration](04-mcp-connectivity-and-client-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/genai-toolbox-tutorial/04-mcp-connectivity-and-client-integration.md b/tutorials/genai-toolbox-tutorial/04-mcp-connectivity-and-client-integration.md
index 551fd88c..f0655234 100644
--- a/tutorials/genai-toolbox-tutorial/04-mcp-connectivity-and-client-integration.md
+++ b/tutorials/genai-toolbox-tutorial/04-mcp-connectivity-and-client-integration.md
@@ -7,6 +7,9 @@ parent: GenAI Toolbox Tutorial
 
 # Chapter 4: MCP Connectivity and Client Integration
 
+Welcome to **Chapter 4: MCP Connectivity and Client Integration**. In this part of **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter compares MCP transport options with native SDK integrations.
 
 ## Learning Goals
@@ -31,3 +34,607 @@ Use native Toolbox SDKs when you need Toolbox-specific auth/authorization featur
 You now have a practical framework for choosing and operating Toolbox integration paths.
 
 Next: [Chapter 5: Prebuilt Connectors and Database Patterns](05-prebuilt-connectors-and-database-patterns.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- tutorial slug: **genai-toolbox-tutorial**
+- chapter focus: **Chapter 4: MCP Connectivity and Client Integration**
+- system context: **Genai Toolbox Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: MCP Connectivity and Client Integration`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [GenAI Toolbox Repository](https://github.com/googleapis/genai-toolbox)
+- [README](https://github.com/googleapis/genai-toolbox/blob/main/README.md)
+- [Configuration Guide](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/configure.md)
+- [Python Local Quickstart](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/local_quickstart.md)
+- [Connect via MCP](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/connect_via_mcp.md)
+- [CLI Reference](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/reference/cli.md)
+- [Deploy with Docker Compose](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/deploy_docker.md)
+- [Developer Guide](https://github.com/googleapis/genai-toolbox/blob/main/DEVELOPER.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [FastMCP Tutorial](../fastmcp-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [awslabs/mcp Tutorial](../awslabs-mcp-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: MCP Connectivity and Client Integration`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: MCP Connectivity and Client Integration
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: MCP Connectivity and Client Integration` as an operating subsystem inside **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: MCP Connectivity and Client Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GenAI Toolbox Repository](https://github.com/googleapis/genai-toolbox)
+  Why it matters: authoritative reference on `GenAI Toolbox Repository` (github.com).
+- [README](https://github.com/googleapis/genai-toolbox/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Configuration Guide](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/configure.md)
+  Why it matters: authoritative reference on `Configuration Guide` (github.com).
+- [Python Local Quickstart](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/local_quickstart.md)
+  Why it matters: authoritative reference on `Python Local Quickstart` (github.com).
+- [Connect via MCP](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/connect_via_mcp.md)
+  Why it matters: authoritative reference on `Connect via MCP` (github.com).
+- [CLI Reference](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/reference/cli.md)
+  Why it matters: authoritative reference on `CLI Reference` (github.com).
+- [Deploy with Docker Compose](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/deploy_docker.md)
+  Why it matters: authoritative reference on `Deploy with Docker Compose` (github.com).
+- [Developer Guide](https://github.com/googleapis/genai-toolbox/blob/main/DEVELOPER.md)
+  Why it matters: authoritative reference on `Developer Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: `tools.yaml`: Sources, Tools, Toolsets, Prompts](03-tools-yaml-sources-tools-toolsets-prompts.md)
+- [Next Chapter: Chapter 5: Prebuilt Connectors and Database Patterns](05-prebuilt-connectors-and-database-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/genai-toolbox-tutorial/05-prebuilt-connectors-and-database-patterns.md b/tutorials/genai-toolbox-tutorial/05-prebuilt-connectors-and-database-patterns.md
index 90f8a0db..9e535dc1 100644
--- a/tutorials/genai-toolbox-tutorial/05-prebuilt-connectors-and-database-patterns.md
+++ b/tutorials/genai-toolbox-tutorial/05-prebuilt-connectors-and-database-patterns.md
@@ -7,6 +7,9 @@ parent: GenAI Toolbox Tutorial
 
 # Chapter 5: Prebuilt Connectors and Database Patterns
 
+Welcome to **Chapter 5: Prebuilt Connectors and Database Patterns**. In this part of **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers prebuilt source/tool configurations and connector scaling patterns.
 
 ## Learning Goals
@@ -31,3 +34,607 @@ Start with one production-critical source type, validate latency and reliability
 You now understand how to scale database coverage without losing operational clarity.
 
 Next: [Chapter 6: Deployment and Observability Patterns](06-deployment-and-observability-patterns.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- tutorial slug: **genai-toolbox-tutorial**
+- chapter focus: **Chapter 5: Prebuilt Connectors and Database Patterns**
+- system context: **Genai Toolbox Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Prebuilt Connectors and Database 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
+
+- [GenAI Toolbox Repository](https://github.com/googleapis/genai-toolbox)
+- [README](https://github.com/googleapis/genai-toolbox/blob/main/README.md)
+- [Configuration Guide](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/configure.md)
+- [Python Local Quickstart](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/local_quickstart.md)
+- [Connect via MCP](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/connect_via_mcp.md)
+- [CLI Reference](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/reference/cli.md)
+- [Deploy with Docker Compose](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/deploy_docker.md)
+- [Developer Guide](https://github.com/googleapis/genai-toolbox/blob/main/DEVELOPER.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [FastMCP Tutorial](../fastmcp-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [awslabs/mcp Tutorial](../awslabs-mcp-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Prebuilt Connectors and Database 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 5: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Prebuilt Connectors and Database Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Prebuilt Connectors and Database Patterns` as an operating subsystem inside **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Prebuilt Connectors and Database Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GenAI Toolbox Repository](https://github.com/googleapis/genai-toolbox)
+  Why it matters: authoritative reference on `GenAI Toolbox Repository` (github.com).
+- [README](https://github.com/googleapis/genai-toolbox/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Configuration Guide](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/configure.md)
+  Why it matters: authoritative reference on `Configuration Guide` (github.com).
+- [Python Local Quickstart](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/local_quickstart.md)
+  Why it matters: authoritative reference on `Python Local Quickstart` (github.com).
+- [Connect via MCP](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/connect_via_mcp.md)
+  Why it matters: authoritative reference on `Connect via MCP` (github.com).
+- [CLI Reference](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/reference/cli.md)
+  Why it matters: authoritative reference on `CLI Reference` (github.com).
+- [Deploy with Docker Compose](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/deploy_docker.md)
+  Why it matters: authoritative reference on `Deploy with Docker Compose` (github.com).
+- [Developer Guide](https://github.com/googleapis/genai-toolbox/blob/main/DEVELOPER.md)
+  Why it matters: authoritative reference on `Developer Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: MCP Connectivity and Client Integration](04-mcp-connectivity-and-client-integration.md)
+- [Next Chapter: Chapter 6: Deployment and Observability Patterns](06-deployment-and-observability-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/genai-toolbox-tutorial/06-deployment-and-observability-patterns.md b/tutorials/genai-toolbox-tutorial/06-deployment-and-observability-patterns.md
index 6458a9ed..f96dea18 100644
--- a/tutorials/genai-toolbox-tutorial/06-deployment-and-observability-patterns.md
+++ b/tutorials/genai-toolbox-tutorial/06-deployment-and-observability-patterns.md
@@ -7,6 +7,9 @@ parent: GenAI Toolbox Tutorial
 
 # Chapter 6: Deployment and Observability Patterns
 
+Welcome to **Chapter 6: Deployment and Observability Patterns**. In this part of **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains runtime deployment options and telemetry controls.
 
 ## Learning Goals
@@ -31,3 +34,607 @@ Use pinned image versions, explicit host/origin settings, and telemetry destinat
 You now have a deployment model that balances speed with operational controls.
 
 Next: [Chapter 7: CLI, Testing, and Development Workflow](07-cli-testing-and-development-workflow.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- tutorial slug: **genai-toolbox-tutorial**
+- chapter focus: **Chapter 6: Deployment and Observability Patterns**
+- system context: **Genai Toolbox Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Deployment and Observability 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
+
+- [GenAI Toolbox Repository](https://github.com/googleapis/genai-toolbox)
+- [README](https://github.com/googleapis/genai-toolbox/blob/main/README.md)
+- [Configuration Guide](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/configure.md)
+- [Python Local Quickstart](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/local_quickstart.md)
+- [Connect via MCP](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/connect_via_mcp.md)
+- [CLI Reference](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/reference/cli.md)
+- [Deploy with Docker Compose](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/deploy_docker.md)
+- [Developer Guide](https://github.com/googleapis/genai-toolbox/blob/main/DEVELOPER.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [FastMCP Tutorial](../fastmcp-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [awslabs/mcp Tutorial](../awslabs-mcp-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Deployment and Observability 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Deployment and Observability Patterns
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Deployment and Observability Patterns` as an operating subsystem inside **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Deployment and Observability Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GenAI Toolbox Repository](https://github.com/googleapis/genai-toolbox)
+  Why it matters: authoritative reference on `GenAI Toolbox Repository` (github.com).
+- [README](https://github.com/googleapis/genai-toolbox/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Configuration Guide](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/configure.md)
+  Why it matters: authoritative reference on `Configuration Guide` (github.com).
+- [Python Local Quickstart](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/local_quickstart.md)
+  Why it matters: authoritative reference on `Python Local Quickstart` (github.com).
+- [Connect via MCP](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/connect_via_mcp.md)
+  Why it matters: authoritative reference on `Connect via MCP` (github.com).
+- [CLI Reference](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/reference/cli.md)
+  Why it matters: authoritative reference on `CLI Reference` (github.com).
+- [Deploy with Docker Compose](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/deploy_docker.md)
+  Why it matters: authoritative reference on `Deploy with Docker Compose` (github.com).
+- [Developer Guide](https://github.com/googleapis/genai-toolbox/blob/main/DEVELOPER.md)
+  Why it matters: authoritative reference on `Developer Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Prebuilt Connectors and Database Patterns](05-prebuilt-connectors-and-database-patterns.md)
+- [Next Chapter: Chapter 7: CLI, Testing, and Development Workflow](07-cli-testing-and-development-workflow.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/genai-toolbox-tutorial/07-cli-testing-and-development-workflow.md b/tutorials/genai-toolbox-tutorial/07-cli-testing-and-development-workflow.md
index 3a525f42..dd8985c3 100644
--- a/tutorials/genai-toolbox-tutorial/07-cli-testing-and-development-workflow.md
+++ b/tutorials/genai-toolbox-tutorial/07-cli-testing-and-development-workflow.md
@@ -7,6 +7,9 @@ parent: GenAI Toolbox Tutorial
 
 # Chapter 7: CLI, Testing, and Development Workflow
 
+Welcome to **Chapter 7: CLI, Testing, and Development Workflow**. In this part of **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on iterative development quality gates.
 
 ## Learning Goals
@@ -31,3 +34,607 @@ Treat `go run . --help`, direct tool invocation, and targeted tests as your shor
 You now have a repeatable workflow for shipping Toolbox changes with lower regression risk.
 
 Next: [Chapter 8: Production Governance and Release Strategy](08-production-governance-and-release-strategy.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- tutorial slug: **genai-toolbox-tutorial**
+- chapter focus: **Chapter 7: CLI, Testing, and Development Workflow**
+- system context: **Genai Toolbox Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: CLI, Testing, and Development 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
+
+- [GenAI Toolbox Repository](https://github.com/googleapis/genai-toolbox)
+- [README](https://github.com/googleapis/genai-toolbox/blob/main/README.md)
+- [Configuration Guide](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/configure.md)
+- [Python Local Quickstart](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/local_quickstart.md)
+- [Connect via MCP](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/connect_via_mcp.md)
+- [CLI Reference](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/reference/cli.md)
+- [Deploy with Docker Compose](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/deploy_docker.md)
+- [Developer Guide](https://github.com/googleapis/genai-toolbox/blob/main/DEVELOPER.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [FastMCP Tutorial](../fastmcp-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [awslabs/mcp Tutorial](../awslabs-mcp-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: CLI, Testing, and Development Workflow`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: CLI, Testing, and Development Workflow
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: CLI, Testing, and Development Workflow` as an operating subsystem inside **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: CLI, Testing, and Development Workflow` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GenAI Toolbox Repository](https://github.com/googleapis/genai-toolbox)
+  Why it matters: authoritative reference on `GenAI Toolbox Repository` (github.com).
+- [README](https://github.com/googleapis/genai-toolbox/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Configuration Guide](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/configure.md)
+  Why it matters: authoritative reference on `Configuration Guide` (github.com).
+- [Python Local Quickstart](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/local_quickstart.md)
+  Why it matters: authoritative reference on `Python Local Quickstart` (github.com).
+- [Connect via MCP](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/connect_via_mcp.md)
+  Why it matters: authoritative reference on `Connect via MCP` (github.com).
+- [CLI Reference](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/reference/cli.md)
+  Why it matters: authoritative reference on `CLI Reference` (github.com).
+- [Deploy with Docker Compose](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/deploy_docker.md)
+  Why it matters: authoritative reference on `Deploy with Docker Compose` (github.com).
+- [Developer Guide](https://github.com/googleapis/genai-toolbox/blob/main/DEVELOPER.md)
+  Why it matters: authoritative reference on `Developer Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Deployment and Observability Patterns](06-deployment-and-observability-patterns.md)
+- [Next Chapter: Chapter 8: Production Governance and Release Strategy](08-production-governance-and-release-strategy.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/genai-toolbox-tutorial/08-production-governance-and-release-strategy.md b/tutorials/genai-toolbox-tutorial/08-production-governance-and-release-strategy.md
index c59a7be5..d9f4b0fe 100644
--- a/tutorials/genai-toolbox-tutorial/08-production-governance-and-release-strategy.md
+++ b/tutorials/genai-toolbox-tutorial/08-production-governance-and-release-strategy.md
@@ -7,6 +7,9 @@ parent: GenAI Toolbox Tutorial
 
 # Chapter 8: Production Governance and Release Strategy
 
+Welcome to **Chapter 8: Production Governance and Release Strategy**. In this part of **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter closes with operating discipline for pre-1.0 and post-1.0 evolution.
 
 ## Learning Goals
@@ -33,3 +36,606 @@ This chapter closes with operating discipline for pre-1.0 and post-1.0 evolution
 ## Summary
 
 You now have an operational model for running GenAI Toolbox as production MCP database infrastructure.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- tutorial slug: **genai-toolbox-tutorial**
+- chapter focus: **Chapter 8: Production Governance and Release Strategy**
+- system context: **Genai Toolbox Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Governance and Release Strategy`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [GenAI Toolbox Repository](https://github.com/googleapis/genai-toolbox)
+- [README](https://github.com/googleapis/genai-toolbox/blob/main/README.md)
+- [Configuration Guide](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/configure.md)
+- [Python Local Quickstart](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/local_quickstart.md)
+- [Connect via MCP](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/connect_via_mcp.md)
+- [CLI Reference](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/reference/cli.md)
+- [Deploy with Docker Compose](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/deploy_docker.md)
+- [Developer Guide](https://github.com/googleapis/genai-toolbox/blob/main/DEVELOPER.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [FastMCP Tutorial](../fastmcp-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [awslabs/mcp Tutorial](../awslabs-mcp-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Governance and Release Strategy`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Production Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Production Governance and Release Strategy
+
+- tutorial context: **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Governance and Release Strategy` as an operating subsystem inside **GenAI Toolbox Tutorial: MCP-First Database Tooling with Config-Driven Control Planes**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Governance and Release Strategy` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GenAI Toolbox Repository](https://github.com/googleapis/genai-toolbox)
+  Why it matters: authoritative reference on `GenAI Toolbox Repository` (github.com).
+- [README](https://github.com/googleapis/genai-toolbox/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Configuration Guide](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/configure.md)
+  Why it matters: authoritative reference on `Configuration Guide` (github.com).
+- [Python Local Quickstart](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/getting-started/local_quickstart.md)
+  Why it matters: authoritative reference on `Python Local Quickstart` (github.com).
+- [Connect via MCP](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/connect_via_mcp.md)
+  Why it matters: authoritative reference on `Connect via MCP` (github.com).
+- [CLI Reference](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/reference/cli.md)
+  Why it matters: authoritative reference on `CLI Reference` (github.com).
+- [Deploy with Docker Compose](https://github.com/googleapis/genai-toolbox/blob/main/docs/en/how-to/deploy_docker.md)
+  Why it matters: authoritative reference on `Deploy with Docker Compose` (github.com).
+- [Developer Guide](https://github.com/googleapis/genai-toolbox/blob/main/DEVELOPER.md)
+  Why it matters: authoritative reference on `Developer Guide` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: CLI, Testing, and Development Workflow](07-cli-testing-and-development-workflow.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/github-mcp-server-tutorial/01-getting-started.md b/tutorials/github-mcp-server-tutorial/01-getting-started.md
index 2dcc71e4..c87aad2b 100644
--- a/tutorials/github-mcp-server-tutorial/01-getting-started.md
+++ b/tutorials/github-mcp-server-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: GitHub MCP Server Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets GitHub MCP connected with a minimum-risk initial setup.
 
 ## Learning Goals
@@ -40,3 +43,597 @@ This chapter gets GitHub MCP connected with a minimum-risk initial setup.
 You now have a safe baseline connection to GitHub MCP.
 
 Next: [Chapter 2: Remote vs Local Architecture](02-remote-vs-local-architecture.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- tutorial slug: **github-mcp-server-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Github Mcp Server 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
+
+- [GitHub MCP Server Repository](https://github.com/github/github-mcp-server)
+- [README](https://github.com/github/github-mcp-server/blob/main/README.md)
+- [Remote Server Docs](https://github.com/github/github-mcp-server/blob/main/docs/remote-server.md)
+- [Server Configuration Guide](https://github.com/github/github-mcp-server/blob/main/docs/server-configuration.md)
+- [Policies and Governance](https://github.com/github/github-mcp-server/blob/main/docs/policies-and-governance.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chrome DevTools MCP Tutorial](../chrome-devtools-mcp-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub MCP Server Repository](https://github.com/github/github-mcp-server)
+  Why it matters: authoritative reference on `GitHub MCP Server Repository` (github.com).
+- [README](https://github.com/github/github-mcp-server/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Remote Server Docs](https://github.com/github/github-mcp-server/blob/main/docs/remote-server.md)
+  Why it matters: authoritative reference on `Remote Server Docs` (github.com).
+- [Server Configuration Guide](https://github.com/github/github-mcp-server/blob/main/docs/server-configuration.md)
+  Why it matters: authoritative reference on `Server Configuration Guide` (github.com).
+- [Policies and Governance](https://github.com/github/github-mcp-server/blob/main/docs/policies-and-governance.md)
+  Why it matters: authoritative reference on `Policies and Governance` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Remote vs Local Architecture](02-remote-vs-local-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/github-mcp-server-tutorial/02-remote-vs-local-architecture.md b/tutorials/github-mcp-server-tutorial/02-remote-vs-local-architecture.md
index 963ef400..09d3e18b 100644
--- a/tutorials/github-mcp-server-tutorial/02-remote-vs-local-architecture.md
+++ b/tutorials/github-mcp-server-tutorial/02-remote-vs-local-architecture.md
@@ -7,6 +7,9 @@ parent: GitHub MCP Server Tutorial
 
 # Chapter 2: Remote vs Local Architecture
 
+Welcome to **Chapter 2: Remote vs Local Architecture**. In this part of **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains the tradeoffs between the hosted remote server and self-run local server.
 
 ## Learning Goals
@@ -38,3 +41,598 @@ Use remote mode first when available. Use local mode when host limitations, envi
 You now understand the operational boundaries of remote and local modes.
 
 Next: [Chapter 3: Authentication and Token Strategy](03-authentication-and-token-strategy.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- tutorial slug: **github-mcp-server-tutorial**
+- chapter focus: **Chapter 2: Remote vs Local Architecture**
+- system context: **Github Mcp Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Remote vs Local 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
+
+- [GitHub MCP Server Repository](https://github.com/github/github-mcp-server)
+- [README](https://github.com/github/github-mcp-server/blob/main/README.md)
+- [Remote Server Docs](https://github.com/github/github-mcp-server/blob/main/docs/remote-server.md)
+- [Server Configuration Guide](https://github.com/github/github-mcp-server/blob/main/docs/server-configuration.md)
+- [Policies and Governance](https://github.com/github/github-mcp-server/blob/main/docs/policies-and-governance.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chrome DevTools MCP Tutorial](../chrome-devtools-mcp-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Remote vs Local 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: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Remote vs Local Architecture
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Remote vs Local Architecture` as an operating subsystem inside **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Remote vs Local Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub MCP Server Repository](https://github.com/github/github-mcp-server)
+  Why it matters: authoritative reference on `GitHub MCP Server Repository` (github.com).
+- [README](https://github.com/github/github-mcp-server/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Remote Server Docs](https://github.com/github/github-mcp-server/blob/main/docs/remote-server.md)
+  Why it matters: authoritative reference on `Remote Server Docs` (github.com).
+- [Server Configuration Guide](https://github.com/github/github-mcp-server/blob/main/docs/server-configuration.md)
+  Why it matters: authoritative reference on `Server Configuration Guide` (github.com).
+- [Policies and Governance](https://github.com/github/github-mcp-server/blob/main/docs/policies-and-governance.md)
+  Why it matters: authoritative reference on `Policies and Governance` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Authentication and Token Strategy](03-authentication-and-token-strategy.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/github-mcp-server-tutorial/03-authentication-and-token-strategy.md b/tutorials/github-mcp-server-tutorial/03-authentication-and-token-strategy.md
index a974c05b..504b2359 100644
--- a/tutorials/github-mcp-server-tutorial/03-authentication-and-token-strategy.md
+++ b/tutorials/github-mcp-server-tutorial/03-authentication-and-token-strategy.md
@@ -7,6 +7,9 @@ parent: GitHub MCP Server Tutorial
 
 # Chapter 3: Authentication and Token Strategy
 
+Welcome to **Chapter 3: Authentication and Token Strategy**. In this part of **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers secure authentication choices and scope minimization.
 
 ## Learning Goals
@@ -42,3 +45,598 @@ This chapter covers secure authentication choices and scope minimization.
 You now have an authentication strategy that balances compatibility and risk.
 
 Next: [Chapter 4: Toolsets, Tools, and Dynamic Discovery](04-toolsets-tools-and-dynamic-discovery.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: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- tutorial slug: **github-mcp-server-tutorial**
+- chapter focus: **Chapter 3: Authentication and Token Strategy**
+- system context: **Github Mcp Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Authentication and Token Strategy`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [GitHub MCP Server Repository](https://github.com/github/github-mcp-server)
+- [README](https://github.com/github/github-mcp-server/blob/main/README.md)
+- [Remote Server Docs](https://github.com/github/github-mcp-server/blob/main/docs/remote-server.md)
+- [Server Configuration Guide](https://github.com/github/github-mcp-server/blob/main/docs/server-configuration.md)
+- [Policies and Governance](https://github.com/github/github-mcp-server/blob/main/docs/policies-and-governance.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chrome DevTools MCP Tutorial](../chrome-devtools-mcp-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Authentication and Token Strategy`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Authentication and Token Strategy
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Authentication and Token Strategy` as an operating subsystem inside **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Authentication and Token Strategy` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub MCP Server Repository](https://github.com/github/github-mcp-server)
+  Why it matters: authoritative reference on `GitHub MCP Server Repository` (github.com).
+- [README](https://github.com/github/github-mcp-server/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Remote Server Docs](https://github.com/github/github-mcp-server/blob/main/docs/remote-server.md)
+  Why it matters: authoritative reference on `Remote Server Docs` (github.com).
+- [Server Configuration Guide](https://github.com/github/github-mcp-server/blob/main/docs/server-configuration.md)
+  Why it matters: authoritative reference on `Server Configuration Guide` (github.com).
+- [Policies and Governance](https://github.com/github/github-mcp-server/blob/main/docs/policies-and-governance.md)
+  Why it matters: authoritative reference on `Policies and Governance` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Remote vs Local Architecture](02-remote-vs-local-architecture.md)
+- [Next Chapter: Chapter 4: Toolsets, Tools, and Dynamic Discovery](04-toolsets-tools-and-dynamic-discovery.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/github-mcp-server-tutorial/04-toolsets-tools-and-dynamic-discovery.md b/tutorials/github-mcp-server-tutorial/04-toolsets-tools-and-dynamic-discovery.md
index 0e2e2148..6019d7a2 100644
--- a/tutorials/github-mcp-server-tutorial/04-toolsets-tools-and-dynamic-discovery.md
+++ b/tutorials/github-mcp-server-tutorial/04-toolsets-tools-and-dynamic-discovery.md
@@ -7,6 +7,9 @@ parent: GitHub MCP Server Tutorial
 
 # Chapter 4: Toolsets, Tools, and Dynamic Discovery
 
+Welcome to **Chapter 4: Toolsets, Tools, and Dynamic Discovery**. In this part of **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains how to precisely shape the server capability surface for better reliability and safety.
 
 ## Learning Goals
@@ -37,3 +40,598 @@ This chapter explains how to precisely shape the server capability surface for b
 You now know how to expose just enough capability for each task context.
 
 Next: [Chapter 5: Host Integration Patterns](05-host-integration-patterns.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- tutorial slug: **github-mcp-server-tutorial**
+- chapter focus: **Chapter 4: Toolsets, Tools, and Dynamic Discovery**
+- system context: **Github Mcp Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Toolsets, Tools, and Dynamic Discovery`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [GitHub MCP Server Repository](https://github.com/github/github-mcp-server)
+- [README](https://github.com/github/github-mcp-server/blob/main/README.md)
+- [Remote Server Docs](https://github.com/github/github-mcp-server/blob/main/docs/remote-server.md)
+- [Server Configuration Guide](https://github.com/github/github-mcp-server/blob/main/docs/server-configuration.md)
+- [Policies and Governance](https://github.com/github/github-mcp-server/blob/main/docs/policies-and-governance.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chrome DevTools MCP Tutorial](../chrome-devtools-mcp-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Toolsets, Tools, and Dynamic Discovery`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Toolsets, Tools, and Dynamic Discovery
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Toolsets, Tools, and Dynamic Discovery` as an operating subsystem inside **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Toolsets, Tools, and Dynamic Discovery` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub MCP Server Repository](https://github.com/github/github-mcp-server)
+  Why it matters: authoritative reference on `GitHub MCP Server Repository` (github.com).
+- [README](https://github.com/github/github-mcp-server/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Remote Server Docs](https://github.com/github/github-mcp-server/blob/main/docs/remote-server.md)
+  Why it matters: authoritative reference on `Remote Server Docs` (github.com).
+- [Server Configuration Guide](https://github.com/github/github-mcp-server/blob/main/docs/server-configuration.md)
+  Why it matters: authoritative reference on `Server Configuration Guide` (github.com).
+- [Policies and Governance](https://github.com/github/github-mcp-server/blob/main/docs/policies-and-governance.md)
+  Why it matters: authoritative reference on `Policies and Governance` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Authentication and Token Strategy](03-authentication-and-token-strategy.md)
+- [Next Chapter: Chapter 5: Host Integration Patterns](05-host-integration-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/github-mcp-server-tutorial/05-host-integration-patterns.md b/tutorials/github-mcp-server-tutorial/05-host-integration-patterns.md
index 238d286d..29a23414 100644
--- a/tutorials/github-mcp-server-tutorial/05-host-integration-patterns.md
+++ b/tutorials/github-mcp-server-tutorial/05-host-integration-patterns.md
@@ -7,6 +7,9 @@ parent: GitHub MCP Server Tutorial
 
 # Chapter 5: Host Integration Patterns
 
+Welcome to **Chapter 5: Host Integration Patterns**. In this part of **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter maps integration patterns across major MCP hosts.
 
 ## Learning Goals
@@ -38,3 +41,598 @@ Keep one canonical server policy (toolsets, read-only defaults, auth model), the
 You now have a host-portable integration strategy for GitHub MCP.
 
 Next: [Chapter 6: Security, Governance, and Enterprise Controls](06-security-governance-and-enterprise-controls.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- tutorial slug: **github-mcp-server-tutorial**
+- chapter focus: **Chapter 5: Host Integration Patterns**
+- system context: **Github Mcp Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Host Integration 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
+
+- [GitHub MCP Server Repository](https://github.com/github/github-mcp-server)
+- [README](https://github.com/github/github-mcp-server/blob/main/README.md)
+- [Remote Server Docs](https://github.com/github/github-mcp-server/blob/main/docs/remote-server.md)
+- [Server Configuration Guide](https://github.com/github/github-mcp-server/blob/main/docs/server-configuration.md)
+- [Policies and Governance](https://github.com/github/github-mcp-server/blob/main/docs/policies-and-governance.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chrome DevTools MCP Tutorial](../chrome-devtools-mcp-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Host Integration 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 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Host Integration Patterns
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Host Integration Patterns` as an operating subsystem inside **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Host Integration Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub MCP Server Repository](https://github.com/github/github-mcp-server)
+  Why it matters: authoritative reference on `GitHub MCP Server Repository` (github.com).
+- [README](https://github.com/github/github-mcp-server/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Remote Server Docs](https://github.com/github/github-mcp-server/blob/main/docs/remote-server.md)
+  Why it matters: authoritative reference on `Remote Server Docs` (github.com).
+- [Server Configuration Guide](https://github.com/github/github-mcp-server/blob/main/docs/server-configuration.md)
+  Why it matters: authoritative reference on `Server Configuration Guide` (github.com).
+- [Policies and Governance](https://github.com/github/github-mcp-server/blob/main/docs/policies-and-governance.md)
+  Why it matters: authoritative reference on `Policies and Governance` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Toolsets, Tools, and Dynamic Discovery](04-toolsets-tools-and-dynamic-discovery.md)
+- [Next Chapter: Chapter 6: Security, Governance, and Enterprise Controls](06-security-governance-and-enterprise-controls.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/github-mcp-server-tutorial/06-security-governance-and-enterprise-controls.md b/tutorials/github-mcp-server-tutorial/06-security-governance-and-enterprise-controls.md
index 3b112c74..4422ff5c 100644
--- a/tutorials/github-mcp-server-tutorial/06-security-governance-and-enterprise-controls.md
+++ b/tutorials/github-mcp-server-tutorial/06-security-governance-and-enterprise-controls.md
@@ -7,6 +7,9 @@ parent: GitHub MCP Server Tutorial
 
 # Chapter 6: Security, Governance, and Enterprise Controls
 
+Welcome to **Chapter 6: Security, Governance, and Enterprise Controls**. In this part of **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers policy and governance controls needed for enterprise adoption.
 
 ## Learning Goals
@@ -36,3 +39,598 @@ This chapter covers policy and governance controls needed for enterprise adoptio
 You now have a governance model for secure, policy-aligned GitHub MCP usage.
 
 Next: [Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations](07-troubleshooting-read-only-and-lockdown-operations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- tutorial slug: **github-mcp-server-tutorial**
+- chapter focus: **Chapter 6: Security, Governance, and Enterprise Controls**
+- system context: **Github Mcp Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Security, Governance, and Enterprise Controls`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [GitHub MCP Server Repository](https://github.com/github/github-mcp-server)
+- [README](https://github.com/github/github-mcp-server/blob/main/README.md)
+- [Remote Server Docs](https://github.com/github/github-mcp-server/blob/main/docs/remote-server.md)
+- [Server Configuration Guide](https://github.com/github/github-mcp-server/blob/main/docs/server-configuration.md)
+- [Policies and Governance](https://github.com/github/github-mcp-server/blob/main/docs/policies-and-governance.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chrome DevTools MCP Tutorial](../chrome-devtools-mcp-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Security, Governance, and Enterprise Controls`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Security, Governance, and Enterprise Controls
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Security, Governance, and Enterprise Controls` as an operating subsystem inside **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Security, Governance, and Enterprise Controls` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub MCP Server Repository](https://github.com/github/github-mcp-server)
+  Why it matters: authoritative reference on `GitHub MCP Server Repository` (github.com).
+- [README](https://github.com/github/github-mcp-server/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Remote Server Docs](https://github.com/github/github-mcp-server/blob/main/docs/remote-server.md)
+  Why it matters: authoritative reference on `Remote Server Docs` (github.com).
+- [Server Configuration Guide](https://github.com/github/github-mcp-server/blob/main/docs/server-configuration.md)
+  Why it matters: authoritative reference on `Server Configuration Guide` (github.com).
+- [Policies and Governance](https://github.com/github/github-mcp-server/blob/main/docs/policies-and-governance.md)
+  Why it matters: authoritative reference on `Policies and Governance` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Host Integration Patterns](05-host-integration-patterns.md)
+- [Next Chapter: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations](07-troubleshooting-read-only-and-lockdown-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/github-mcp-server-tutorial/07-troubleshooting-read-only-and-lockdown-operations.md b/tutorials/github-mcp-server-tutorial/07-troubleshooting-read-only-and-lockdown-operations.md
index b92f6fd5..fc6dadd1 100644
--- a/tutorials/github-mcp-server-tutorial/07-troubleshooting-read-only-and-lockdown-operations.md
+++ b/tutorials/github-mcp-server-tutorial/07-troubleshooting-read-only-and-lockdown-operations.md
@@ -7,6 +7,9 @@ parent: GitHub MCP Server Tutorial
 
 # Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
 
+Welcome to **Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations**. In this part of **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter provides practical recovery patterns for operational issues.
 
 ## Learning Goals
@@ -36,3 +39,598 @@ This chapter provides practical recovery patterns for operational issues.
 You now have a troubleshooting runbook for stable GitHub MCP operations.
 
 Next: [Chapter 8: Contribution and Upgrade Workflow](08-contribution-and-upgrade-workflow.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- tutorial slug: **github-mcp-server-tutorial**
+- chapter focus: **Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations**
+- system context: **Github Mcp Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Troubleshooting, Read-Only, and Lockdown 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
+
+- [GitHub MCP Server Repository](https://github.com/github/github-mcp-server)
+- [README](https://github.com/github/github-mcp-server/blob/main/README.md)
+- [Remote Server Docs](https://github.com/github/github-mcp-server/blob/main/docs/remote-server.md)
+- [Server Configuration Guide](https://github.com/github/github-mcp-server/blob/main/docs/server-configuration.md)
+- [Policies and Governance](https://github.com/github/github-mcp-server/blob/main/docs/policies-and-governance.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chrome DevTools MCP Tutorial](../chrome-devtools-mcp-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations` as an operating subsystem inside **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub MCP Server Repository](https://github.com/github/github-mcp-server)
+  Why it matters: authoritative reference on `GitHub MCP Server Repository` (github.com).
+- [README](https://github.com/github/github-mcp-server/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Remote Server Docs](https://github.com/github/github-mcp-server/blob/main/docs/remote-server.md)
+  Why it matters: authoritative reference on `Remote Server Docs` (github.com).
+- [Server Configuration Guide](https://github.com/github/github-mcp-server/blob/main/docs/server-configuration.md)
+  Why it matters: authoritative reference on `Server Configuration Guide` (github.com).
+- [Policies and Governance](https://github.com/github/github-mcp-server/blob/main/docs/policies-and-governance.md)
+  Why it matters: authoritative reference on `Policies and Governance` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Security, Governance, and Enterprise Controls](06-security-governance-and-enterprise-controls.md)
+- [Next Chapter: Chapter 8: Contribution and Upgrade Workflow](08-contribution-and-upgrade-workflow.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/github-mcp-server-tutorial/08-contribution-and-upgrade-workflow.md b/tutorials/github-mcp-server-tutorial/08-contribution-and-upgrade-workflow.md
index d3b452d6..379177d8 100644
--- a/tutorials/github-mcp-server-tutorial/08-contribution-and-upgrade-workflow.md
+++ b/tutorials/github-mcp-server-tutorial/08-contribution-and-upgrade-workflow.md
@@ -7,6 +7,9 @@ parent: GitHub MCP Server Tutorial
 
 # Chapter 8: Contribution and Upgrade Workflow
 
+Welcome to **Chapter 8: Contribution and Upgrade Workflow**. In this part of **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers sustainable change management for teams using GitHub MCP in production.
 
 ## Learning Goals
@@ -38,3 +41,597 @@ Next steps:
 - define a default read-only profile for exploratory tasks
 - define a narrow write-enabled profile for planned automation
 - run quarterly review of toolsets, scopes, and host policy alignment
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- tutorial slug: **github-mcp-server-tutorial**
+- chapter focus: **Chapter 8: Contribution and Upgrade Workflow**
+- system context: **Github Mcp Server Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Contribution and Upgrade 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
+
+- [GitHub MCP Server Repository](https://github.com/github/github-mcp-server)
+- [README](https://github.com/github/github-mcp-server/blob/main/README.md)
+- [Remote Server Docs](https://github.com/github/github-mcp-server/blob/main/docs/remote-server.md)
+- [Server Configuration Guide](https://github.com/github/github-mcp-server/blob/main/docs/server-configuration.md)
+- [Policies and Governance](https://github.com/github/github-mcp-server/blob/main/docs/policies-and-governance.md)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chrome DevTools MCP Tutorial](../chrome-devtools-mcp-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [Codex CLI Tutorial](../codex-cli-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Contribution and Upgrade 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 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Contribution and Upgrade Workflow
+
+- tutorial context: **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Contribution and Upgrade Workflow` as an operating subsystem inside **GitHub MCP Server Tutorial: Production GitHub Operations Through MCP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Contribution and Upgrade Workflow` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [GitHub MCP Server Repository](https://github.com/github/github-mcp-server)
+  Why it matters: authoritative reference on `GitHub MCP Server Repository` (github.com).
+- [README](https://github.com/github/github-mcp-server/blob/main/README.md)
+  Why it matters: authoritative reference on `README` (github.com).
+- [Remote Server Docs](https://github.com/github/github-mcp-server/blob/main/docs/remote-server.md)
+  Why it matters: authoritative reference on `Remote Server Docs` (github.com).
+- [Server Configuration Guide](https://github.com/github/github-mcp-server/blob/main/docs/server-configuration.md)
+  Why it matters: authoritative reference on `Server Configuration Guide` (github.com).
+- [Policies and Governance](https://github.com/github/github-mcp-server/blob/main/docs/policies-and-governance.md)
+  Why it matters: authoritative reference on `Policies and Governance` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Troubleshooting, Read-Only, and Lockdown Operations](07-troubleshooting-read-only-and-lockdown-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/goose-tutorial/01-getting-started.md b/tutorials/goose-tutorial/01-getting-started.md
index 0604c44d..d4b4600f 100644
--- a/tutorials/goose-tutorial/01-getting-started.md
+++ b/tutorials/goose-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Goose Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter establishes a clean Goose baseline so you can move into advanced workflows without setup drift.
 
 ## Learning Goals
@@ -62,3 +65,586 @@ Inside the session, start with a scoped prompt such as:
 You now have Goose installed, configured, and running in a real project context.
 
 Next: [Chapter 2: Architecture and Agent Loop](02-architecture-and-agent-loop.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- tutorial slug: **goose-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Goose 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
+
+- [Goose Repository](https://github.com/block/goose)
+- [Goose Quickstart](https://block.github.io/goose/docs/quickstart)
+- [Install goose](https://block.github.io/goose/docs/getting-started/installation)
+- [Supported providers](https://block.github.io/goose/docs/getting-started/providers)
+- [Using extensions](https://block.github.io/goose/docs/getting-started/using-extensions)
+- [CLI commands](https://block.github.io/goose/docs/guides/goose-cli-commands)
+- [Permissions guide](https://block.github.io/goose/docs/guides/goose-permissions)
+- [Architecture guide](https://block.github.io/goose/docs/goose-architecture/)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Crush Tutorial](../crush-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `path`, `repo`, `goose` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `session` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `path`.
+2. **Input normalization**: shape incoming data so `repo` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `goose`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Goose Repository](https://github.com/block/goose)
+  Why it matters: authoritative reference on `Goose Repository` (github.com).
+- [Goose Quickstart](https://block.github.io/goose/docs/quickstart)
+  Why it matters: authoritative reference on `Goose Quickstart` (block.github.io).
+- [Install goose](https://block.github.io/goose/docs/getting-started/installation)
+  Why it matters: authoritative reference on `Install goose` (block.github.io).
+- [Supported providers](https://block.github.io/goose/docs/getting-started/providers)
+  Why it matters: authoritative reference on `Supported providers` (block.github.io).
+- [Using extensions](https://block.github.io/goose/docs/getting-started/using-extensions)
+  Why it matters: authoritative reference on `Using extensions` (block.github.io).
+- [CLI commands](https://block.github.io/goose/docs/guides/goose-cli-commands)
+  Why it matters: authoritative reference on `CLI commands` (block.github.io).
+- [Permissions guide](https://block.github.io/goose/docs/guides/goose-permissions)
+  Why it matters: authoritative reference on `Permissions guide` (block.github.io).
+- [Architecture guide](https://block.github.io/goose/docs/goose-architecture/)
+  Why it matters: authoritative reference on `Architecture guide` (block.github.io).
+
+Suggested trace strategy:
+- search upstream code for `path` and `repo` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Architecture and Agent Loop](02-architecture-and-agent-loop.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/goose-tutorial/02-architecture-and-agent-loop.md b/tutorials/goose-tutorial/02-architecture-and-agent-loop.md
index 91b5f1cb..50769800 100644
--- a/tutorials/goose-tutorial/02-architecture-and-agent-loop.md
+++ b/tutorials/goose-tutorial/02-architecture-and-agent-loop.md
@@ -7,6 +7,9 @@ parent: Goose Tutorial
 
 # Chapter 2: Architecture and Agent Loop
 
+Welcome to **Chapter 2: Architecture and Agent Loop**. In this part of **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains how Goose turns requests into concrete engineering actions.
 
 ## Learning Goals
@@ -59,3 +62,583 @@ This makes multi-step workflows more resilient than simple one-shot prompting.
 You now have an operator-level mental model for Goose's execution loop and error paths.
 
 Next: [Chapter 3: Providers and Model Routing](03-providers-and-model-routing.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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- tutorial slug: **goose-tutorial**
+- chapter focus: **Chapter 2: Architecture and Agent Loop**
+- system context: **Goose Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Architecture and Agent Loop`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Goose Repository](https://github.com/block/goose)
+- [Goose Quickstart](https://block.github.io/goose/docs/quickstart)
+- [Install goose](https://block.github.io/goose/docs/getting-started/installation)
+- [Supported providers](https://block.github.io/goose/docs/getting-started/providers)
+- [Using extensions](https://block.github.io/goose/docs/getting-started/using-extensions)
+- [CLI commands](https://block.github.io/goose/docs/guides/goose-cli-commands)
+- [Permissions guide](https://block.github.io/goose/docs/guides/goose-permissions)
+- [Architecture guide](https://block.github.io/goose/docs/goose-architecture/)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Crush Tutorial](../crush-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Architecture and Agent Loop`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Architecture and Agent Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Architecture and Agent Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Architecture and Agent Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Architecture and Agent Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Architecture and Agent Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Architecture and Agent Loop
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Architecture and Agent Loop` as an operating subsystem inside **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Architecture and Agent Loop` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Goose Repository](https://github.com/block/goose)
+  Why it matters: authoritative reference on `Goose Repository` (github.com).
+- [Goose Quickstart](https://block.github.io/goose/docs/quickstart)
+  Why it matters: authoritative reference on `Goose Quickstart` (block.github.io).
+- [Install goose](https://block.github.io/goose/docs/getting-started/installation)
+  Why it matters: authoritative reference on `Install goose` (block.github.io).
+- [Supported providers](https://block.github.io/goose/docs/getting-started/providers)
+  Why it matters: authoritative reference on `Supported providers` (block.github.io).
+- [Using extensions](https://block.github.io/goose/docs/getting-started/using-extensions)
+  Why it matters: authoritative reference on `Using extensions` (block.github.io).
+- [CLI commands](https://block.github.io/goose/docs/guides/goose-cli-commands)
+  Why it matters: authoritative reference on `CLI commands` (block.github.io).
+- [Permissions guide](https://block.github.io/goose/docs/guides/goose-permissions)
+  Why it matters: authoritative reference on `Permissions guide` (block.github.io).
+- [Architecture guide](https://block.github.io/goose/docs/goose-architecture/)
+  Why it matters: authoritative reference on `Architecture guide` (block.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Providers and Model Routing](03-providers-and-model-routing.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/goose-tutorial/03-providers-and-model-routing.md b/tutorials/goose-tutorial/03-providers-and-model-routing.md
index cf1ac5c7..f1417e10 100644
--- a/tutorials/goose-tutorial/03-providers-and-model-routing.md
+++ b/tutorials/goose-tutorial/03-providers-and-model-routing.md
@@ -7,6 +7,9 @@ parent: Goose Tutorial
 
 # Chapter 3: Providers and Model Routing
 
+Welcome to **Chapter 3: Providers and Model Routing**. In this part of **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on selecting and configuring model providers for reliability, cost, and performance.
 
 ## Learning Goals
@@ -58,3 +61,583 @@ This chapter focuses on selecting and configuring model providers for reliabilit
 You now know how to route Goose through the right provider and model setup for your constraints.
 
 Next: [Chapter 4: Permissions and Tool Governance](04-permissions-and-tool-governance.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- tutorial slug: **goose-tutorial**
+- chapter focus: **Chapter 3: Providers and Model Routing**
+- system context: **Goose Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Providers and Model Routing`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Goose Repository](https://github.com/block/goose)
+- [Goose Quickstart](https://block.github.io/goose/docs/quickstart)
+- [Install goose](https://block.github.io/goose/docs/getting-started/installation)
+- [Supported providers](https://block.github.io/goose/docs/getting-started/providers)
+- [Using extensions](https://block.github.io/goose/docs/getting-started/using-extensions)
+- [CLI commands](https://block.github.io/goose/docs/guides/goose-cli-commands)
+- [Permissions guide](https://block.github.io/goose/docs/guides/goose-permissions)
+- [Architecture guide](https://block.github.io/goose/docs/goose-architecture/)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Crush Tutorial](../crush-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Providers and Model Routing`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Providers and Model Routing
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Providers and Model Routing` as an operating subsystem inside **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Providers and Model Routing` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Goose Repository](https://github.com/block/goose)
+  Why it matters: authoritative reference on `Goose Repository` (github.com).
+- [Goose Quickstart](https://block.github.io/goose/docs/quickstart)
+  Why it matters: authoritative reference on `Goose Quickstart` (block.github.io).
+- [Install goose](https://block.github.io/goose/docs/getting-started/installation)
+  Why it matters: authoritative reference on `Install goose` (block.github.io).
+- [Supported providers](https://block.github.io/goose/docs/getting-started/providers)
+  Why it matters: authoritative reference on `Supported providers` (block.github.io).
+- [Using extensions](https://block.github.io/goose/docs/getting-started/using-extensions)
+  Why it matters: authoritative reference on `Using extensions` (block.github.io).
+- [CLI commands](https://block.github.io/goose/docs/guides/goose-cli-commands)
+  Why it matters: authoritative reference on `CLI commands` (block.github.io).
+- [Permissions guide](https://block.github.io/goose/docs/guides/goose-permissions)
+  Why it matters: authoritative reference on `Permissions guide` (block.github.io).
+- [Architecture guide](https://block.github.io/goose/docs/goose-architecture/)
+  Why it matters: authoritative reference on `Architecture guide` (block.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Architecture and Agent Loop](02-architecture-and-agent-loop.md)
+- [Next Chapter: Chapter 4: Permissions and Tool Governance](04-permissions-and-tool-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/goose-tutorial/04-permissions-and-tool-governance.md b/tutorials/goose-tutorial/04-permissions-and-tool-governance.md
index 645d7f5c..87f9f11b 100644
--- a/tutorials/goose-tutorial/04-permissions-and-tool-governance.md
+++ b/tutorials/goose-tutorial/04-permissions-and-tool-governance.md
@@ -7,6 +7,9 @@ parent: Goose Tutorial
 
 # Chapter 4: Permissions and Tool Governance
 
+Welcome to **Chapter 4: Permissions and Tool Governance**. In this part of **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers the controls that separate fast automation from unsafe automation.
 
 ## Learning Goals
@@ -48,3 +51,595 @@ For restricted environments, Goose can enforce extension allowlists via `GOOSE_A
 You now have a concrete security-control model for tool execution in Goose.
 
 Next: [Chapter 5: Sessions and Context Management](05-sessions-and-context-management.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: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- tutorial slug: **goose-tutorial**
+- chapter focus: **Chapter 4: Permissions and Tool Governance**
+- system context: **Goose Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Permissions and Tool 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
+
+- [Goose Repository](https://github.com/block/goose)
+- [Goose Quickstart](https://block.github.io/goose/docs/quickstart)
+- [Install goose](https://block.github.io/goose/docs/getting-started/installation)
+- [Supported providers](https://block.github.io/goose/docs/getting-started/providers)
+- [Using extensions](https://block.github.io/goose/docs/getting-started/using-extensions)
+- [CLI commands](https://block.github.io/goose/docs/guides/goose-cli-commands)
+- [Permissions guide](https://block.github.io/goose/docs/guides/goose-permissions)
+- [Architecture guide](https://block.github.io/goose/docs/goose-architecture/)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Crush Tutorial](../crush-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Permissions and Tool 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 4: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Permissions and Tool Governance
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Permissions and Tool Governance` as an operating subsystem inside **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Permissions and Tool Governance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Goose Repository](https://github.com/block/goose)
+  Why it matters: authoritative reference on `Goose Repository` (github.com).
+- [Goose Quickstart](https://block.github.io/goose/docs/quickstart)
+  Why it matters: authoritative reference on `Goose Quickstart` (block.github.io).
+- [Install goose](https://block.github.io/goose/docs/getting-started/installation)
+  Why it matters: authoritative reference on `Install goose` (block.github.io).
+- [Supported providers](https://block.github.io/goose/docs/getting-started/providers)
+  Why it matters: authoritative reference on `Supported providers` (block.github.io).
+- [Using extensions](https://block.github.io/goose/docs/getting-started/using-extensions)
+  Why it matters: authoritative reference on `Using extensions` (block.github.io).
+- [CLI commands](https://block.github.io/goose/docs/guides/goose-cli-commands)
+  Why it matters: authoritative reference on `CLI commands` (block.github.io).
+- [Permissions guide](https://block.github.io/goose/docs/guides/goose-permissions)
+  Why it matters: authoritative reference on `Permissions guide` (block.github.io).
+- [Architecture guide](https://block.github.io/goose/docs/goose-architecture/)
+  Why it matters: authoritative reference on `Architecture guide` (block.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Providers and Model Routing](03-providers-and-model-routing.md)
+- [Next Chapter: Chapter 5: Sessions and Context Management](05-sessions-and-context-management.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/goose-tutorial/05-sessions-and-context-management.md b/tutorials/goose-tutorial/05-sessions-and-context-management.md
index b9e8b634..f0409fb2 100644
--- a/tutorials/goose-tutorial/05-sessions-and-context-management.md
+++ b/tutorials/goose-tutorial/05-sessions-and-context-management.md
@@ -7,6 +7,9 @@ parent: Goose Tutorial
 
 # Chapter 5: Sessions and Context Management
 
+Welcome to **Chapter 5: Sessions and Context Management**. In this part of **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explains how Goose keeps long-running workflows productive without losing context quality.
 
 ## Learning Goals
@@ -54,3 +57,583 @@ Useful environment controls include:
 You now know how to run longer Goose sessions without uncontrolled context growth.
 
 Next: [Chapter 6: Extensions and MCP Integration](06-extensions-and-mcp-integration.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- tutorial slug: **goose-tutorial**
+- chapter focus: **Chapter 5: Sessions and Context Management**
+- system context: **Goose Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Sessions and Context Management`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Goose Repository](https://github.com/block/goose)
+- [Goose Quickstart](https://block.github.io/goose/docs/quickstart)
+- [Install goose](https://block.github.io/goose/docs/getting-started/installation)
+- [Supported providers](https://block.github.io/goose/docs/getting-started/providers)
+- [Using extensions](https://block.github.io/goose/docs/getting-started/using-extensions)
+- [CLI commands](https://block.github.io/goose/docs/guides/goose-cli-commands)
+- [Permissions guide](https://block.github.io/goose/docs/guides/goose-permissions)
+- [Architecture guide](https://block.github.io/goose/docs/goose-architecture/)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Crush Tutorial](../crush-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Sessions and Context Management`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Sessions and Context Management
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Sessions and Context Management` as an operating subsystem inside **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Sessions and Context Management` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Goose Repository](https://github.com/block/goose)
+  Why it matters: authoritative reference on `Goose Repository` (github.com).
+- [Goose Quickstart](https://block.github.io/goose/docs/quickstart)
+  Why it matters: authoritative reference on `Goose Quickstart` (block.github.io).
+- [Install goose](https://block.github.io/goose/docs/getting-started/installation)
+  Why it matters: authoritative reference on `Install goose` (block.github.io).
+- [Supported providers](https://block.github.io/goose/docs/getting-started/providers)
+  Why it matters: authoritative reference on `Supported providers` (block.github.io).
+- [Using extensions](https://block.github.io/goose/docs/getting-started/using-extensions)
+  Why it matters: authoritative reference on `Using extensions` (block.github.io).
+- [CLI commands](https://block.github.io/goose/docs/guides/goose-cli-commands)
+  Why it matters: authoritative reference on `CLI commands` (block.github.io).
+- [Permissions guide](https://block.github.io/goose/docs/guides/goose-permissions)
+  Why it matters: authoritative reference on `Permissions guide` (block.github.io).
+- [Architecture guide](https://block.github.io/goose/docs/goose-architecture/)
+  Why it matters: authoritative reference on `Architecture guide` (block.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Permissions and Tool Governance](04-permissions-and-tool-governance.md)
+- [Next Chapter: Chapter 6: Extensions and MCP Integration](06-extensions-and-mcp-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/goose-tutorial/06-extensions-and-mcp-integration.md b/tutorials/goose-tutorial/06-extensions-and-mcp-integration.md
index 7f61643d..43810e78 100644
--- a/tutorials/goose-tutorial/06-extensions-and-mcp-integration.md
+++ b/tutorials/goose-tutorial/06-extensions-and-mcp-integration.md
@@ -7,6 +7,9 @@ parent: Goose Tutorial
 
 # Chapter 6: Extensions and MCP Integration
 
+Welcome to **Chapter 6: Extensions and MCP Integration**. In this part of **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter covers how Goose expands beyond built-ins through MCP extension workflows.
 
 ## Learning Goals
@@ -61,3 +64,587 @@ npx -y @modelcontextprotocol/server-memory
 You now know how to evolve Goose capabilities with built-in and external MCP integrations.
 
 Next: [Chapter 7: CLI Workflows and Automation](07-cli-workflows-and-automation.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- tutorial slug: **goose-tutorial**
+- chapter focus: **Chapter 6: Extensions and MCP Integration**
+- system context: **Goose Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Extensions and MCP Integration`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Goose Repository](https://github.com/block/goose)
+- [Goose Quickstart](https://block.github.io/goose/docs/quickstart)
+- [Install goose](https://block.github.io/goose/docs/getting-started/installation)
+- [Supported providers](https://block.github.io/goose/docs/getting-started/providers)
+- [Using extensions](https://block.github.io/goose/docs/getting-started/using-extensions)
+- [CLI commands](https://block.github.io/goose/docs/guides/goose-cli-commands)
+- [Permissions guide](https://block.github.io/goose/docs/guides/goose-permissions)
+- [Architecture guide](https://block.github.io/goose/docs/goose-architecture/)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Crush Tutorial](../crush-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Extensions and MCP Integration`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Extensions and MCP Integration
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Extension`, `goose`, `configure` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Extensions and MCP Integration` as an operating subsystem inside **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `select`, `choose`, `Command` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Extensions and MCP Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Extension`.
+2. **Input normalization**: shape incoming data so `goose` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `configure`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Goose Repository](https://github.com/block/goose)
+  Why it matters: authoritative reference on `Goose Repository` (github.com).
+- [Goose Quickstart](https://block.github.io/goose/docs/quickstart)
+  Why it matters: authoritative reference on `Goose Quickstart` (block.github.io).
+- [Install goose](https://block.github.io/goose/docs/getting-started/installation)
+  Why it matters: authoritative reference on `Install goose` (block.github.io).
+- [Supported providers](https://block.github.io/goose/docs/getting-started/providers)
+  Why it matters: authoritative reference on `Supported providers` (block.github.io).
+- [Using extensions](https://block.github.io/goose/docs/getting-started/using-extensions)
+  Why it matters: authoritative reference on `Using extensions` (block.github.io).
+- [CLI commands](https://block.github.io/goose/docs/guides/goose-cli-commands)
+  Why it matters: authoritative reference on `CLI commands` (block.github.io).
+- [Permissions guide](https://block.github.io/goose/docs/guides/goose-permissions)
+  Why it matters: authoritative reference on `Permissions guide` (block.github.io).
+- [Architecture guide](https://block.github.io/goose/docs/goose-architecture/)
+  Why it matters: authoritative reference on `Architecture guide` (block.github.io).
+
+Suggested trace strategy:
+- search upstream code for `Extension` and `goose` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Sessions and Context Management](05-sessions-and-context-management.md)
+- [Next Chapter: Chapter 7: CLI Workflows and Automation](07-cli-workflows-and-automation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/goose-tutorial/07-cli-workflows-and-automation.md b/tutorials/goose-tutorial/07-cli-workflows-and-automation.md
index 60d6ac1b..bc62eaef 100644
--- a/tutorials/goose-tutorial/07-cli-workflows-and-automation.md
+++ b/tutorials/goose-tutorial/07-cli-workflows-and-automation.md
@@ -7,6 +7,9 @@ parent: Goose Tutorial
 
 # Chapter 7: CLI Workflows and Automation
 
+Welcome to **Chapter 7: CLI Workflows and Automation**. In this part of **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter focuses on making Goose reliable inside repeatable terminal workflows.
 
 ## Learning Goals
@@ -52,3 +55,595 @@ This chapter focuses on making Goose reliable inside repeatable terminal workflo
 You now have a production-friendly CLI operating model for Goose automation.
 
 Next: [Chapter 8: Production Operations and Security](08-production-operations-and-security.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- tutorial slug: **goose-tutorial**
+- chapter focus: **Chapter 7: CLI Workflows and Automation**
+- system context: **Goose Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: CLI Workflows and Automation`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Goose Repository](https://github.com/block/goose)
+- [Goose Quickstart](https://block.github.io/goose/docs/quickstart)
+- [Install goose](https://block.github.io/goose/docs/getting-started/installation)
+- [Supported providers](https://block.github.io/goose/docs/getting-started/providers)
+- [Using extensions](https://block.github.io/goose/docs/getting-started/using-extensions)
+- [CLI commands](https://block.github.io/goose/docs/guides/goose-cli-commands)
+- [Permissions guide](https://block.github.io/goose/docs/guides/goose-permissions)
+- [Architecture guide](https://block.github.io/goose/docs/goose-architecture/)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Crush Tutorial](../crush-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: CLI Workflows and Automation`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: CLI Workflows and Automation
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: CLI Workflows and Automation` as an operating subsystem inside **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: CLI Workflows and Automation` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Goose Repository](https://github.com/block/goose)
+  Why it matters: authoritative reference on `Goose Repository` (github.com).
+- [Goose Quickstart](https://block.github.io/goose/docs/quickstart)
+  Why it matters: authoritative reference on `Goose Quickstart` (block.github.io).
+- [Install goose](https://block.github.io/goose/docs/getting-started/installation)
+  Why it matters: authoritative reference on `Install goose` (block.github.io).
+- [Supported providers](https://block.github.io/goose/docs/getting-started/providers)
+  Why it matters: authoritative reference on `Supported providers` (block.github.io).
+- [Using extensions](https://block.github.io/goose/docs/getting-started/using-extensions)
+  Why it matters: authoritative reference on `Using extensions` (block.github.io).
+- [CLI commands](https://block.github.io/goose/docs/guides/goose-cli-commands)
+  Why it matters: authoritative reference on `CLI commands` (block.github.io).
+- [Permissions guide](https://block.github.io/goose/docs/guides/goose-permissions)
+  Why it matters: authoritative reference on `Permissions guide` (block.github.io).
+- [Architecture guide](https://block.github.io/goose/docs/goose-architecture/)
+  Why it matters: authoritative reference on `Architecture guide` (block.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Extensions and MCP Integration](06-extensions-and-mcp-integration.md)
+- [Next Chapter: Chapter 8: Production Operations and Security](08-production-operations-and-security.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/goose-tutorial/08-production-operations-and-security.md b/tutorials/goose-tutorial/08-production-operations-and-security.md
index 4f33aedb..458ea840 100644
--- a/tutorials/goose-tutorial/08-production-operations-and-security.md
+++ b/tutorials/goose-tutorial/08-production-operations-and-security.md
@@ -7,6 +7,9 @@ parent: Goose Tutorial
 
 # Chapter 8: Production Operations and Security
 
+Welcome to **Chapter 8: Production Operations and Security**. In this part of **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter turns Goose from a useful local assistant into a controlled team platform.
 
 ## Learning Goals
@@ -52,3 +55,594 @@ This chapter turns Goose from a useful local assistant into a controlled team pl
 You now have a complete framework for running Goose with strong safety, consistency, and operational reliability.
 
 Continue by comparing workflows in the [Crush Tutorial](../crush-tutorial/).
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- tutorial slug: **goose-tutorial**
+- chapter focus: **Chapter 8: Production Operations and Security**
+- system context: **Goose Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Operations 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
+
+- [Goose Repository](https://github.com/block/goose)
+- [Goose Quickstart](https://block.github.io/goose/docs/quickstart)
+- [Install goose](https://block.github.io/goose/docs/getting-started/installation)
+- [Supported providers](https://block.github.io/goose/docs/getting-started/providers)
+- [Using extensions](https://block.github.io/goose/docs/getting-started/using-extensions)
+- [CLI commands](https://block.github.io/goose/docs/guides/goose-cli-commands)
+- [Permissions guide](https://block.github.io/goose/docs/guides/goose-permissions)
+- [Architecture guide](https://block.github.io/goose/docs/goose-architecture/)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Crush Tutorial](../crush-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Operations and Security`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Production Operations and Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Production Operations and Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Production Operations and Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Production Operations and Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Production Operations and Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Operations and Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Operations and Security
+
+- tutorial context: **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Operations and Security` as an operating subsystem inside **Goose Tutorial: Extensible Open-Source AI Agent for Real Engineering Work**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Operations and Security` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Goose Repository](https://github.com/block/goose)
+  Why it matters: authoritative reference on `Goose Repository` (github.com).
+- [Goose Quickstart](https://block.github.io/goose/docs/quickstart)
+  Why it matters: authoritative reference on `Goose Quickstart` (block.github.io).
+- [Install goose](https://block.github.io/goose/docs/getting-started/installation)
+  Why it matters: authoritative reference on `Install goose` (block.github.io).
+- [Supported providers](https://block.github.io/goose/docs/getting-started/providers)
+  Why it matters: authoritative reference on `Supported providers` (block.github.io).
+- [Using extensions](https://block.github.io/goose/docs/getting-started/using-extensions)
+  Why it matters: authoritative reference on `Using extensions` (block.github.io).
+- [CLI commands](https://block.github.io/goose/docs/guides/goose-cli-commands)
+  Why it matters: authoritative reference on `CLI commands` (block.github.io).
+- [Permissions guide](https://block.github.io/goose/docs/guides/goose-permissions)
+  Why it matters: authoritative reference on `Permissions guide` (block.github.io).
+- [Architecture guide](https://block.github.io/goose/docs/goose-architecture/)
+  Why it matters: authoritative reference on `Architecture guide` (block.github.io).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: CLI Workflows and Automation](07-cli-workflows-and-automation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gpt-oss-tutorial/01-getting-started.md b/tutorials/gpt-oss-tutorial/01-getting-started.md
index 5214146e..3a2c95c9 100644
--- a/tutorials/gpt-oss-tutorial/01-getting-started.md
+++ b/tutorials/gpt-oss-tutorial/01-getting-started.md
@@ -8,6 +8,9 @@ parent: "GPT Open Source - Deep Dive Tutorial"
 
 # Chapter 1: Getting Started -- Understanding the Open-Source GPT Landscape
 
+Welcome to **Chapter 1: Getting Started -- Understanding the Open-Source GPT Landscape**. In this part of **GPT Open Source: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 The open-source GPT ecosystem represents one of the most significant movements in modern AI. Starting with OpenAI's release of the GPT-2 model weights in 2019, the community has built an impressive collection of implementations that range from educational single-file projects to production-grade training frameworks capable of handling models with hundreds of billions of parameters.
@@ -453,3 +456,58 @@ In [Chapter 2: Transformer Architecture](02-transformer-architecture.md), we wil
 
 ---
 *Built with insights from open-source GPT implementations.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `config`, `self`, `n_embd` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started -- Understanding the Open-Source GPT Landscape` as an operating subsystem inside **GPT Open Source: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `bias`, `torch`, `n_head` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started -- Understanding the Open-Source GPT Landscape` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `config`.
+2. **Input normalization**: shape incoming data so `self` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `n_embd`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [nanoGPT](https://github.com/karpathy/nanoGPT)
+  Why it matters: authoritative reference on `nanoGPT` (github.com).
+- [minGPT](https://github.com/karpathy/minGPT)
+  Why it matters: authoritative reference on `minGPT` (github.com).
+- [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
+  Why it matters: authoritative reference on `GPT-NeoX` (github.com).
+- [GPT-Neo](https://github.com/EleutherAI/gpt-neo)
+  Why it matters: authoritative reference on `GPT-Neo` (github.com).
+- [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax)
+  Why it matters: authoritative reference on `GPT-J` (github.com).
+- [Chapter 1: Getting Started](01-getting-started.md)
+  Why it matters: authoritative reference on `Chapter 1: Getting Started` (01-getting-started.md).
+
+Suggested trace strategy:
+- search upstream code for `config` and `self` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Transformer Architecture -- Self-Attention, Multi-Head Attention, and Feed-Forward Networks](02-transformer-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gpt-oss-tutorial/02-transformer-architecture.md b/tutorials/gpt-oss-tutorial/02-transformer-architecture.md
index 17dd912d..3d95dc63 100644
--- a/tutorials/gpt-oss-tutorial/02-transformer-architecture.md
+++ b/tutorials/gpt-oss-tutorial/02-transformer-architecture.md
@@ -8,6 +8,9 @@ parent: "GPT Open Source - Deep Dive Tutorial"
 
 # Chapter 2: Transformer Architecture -- Self-Attention, Multi-Head Attention, and Feed-Forward Networks
 
+Welcome to **Chapter 2: Transformer Architecture -- Self-Attention, Multi-Head Attention, and Feed-Forward Networks**. In this part of **GPT Open Source: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 The GPT architecture is a decoder-only transformer. Every open-source GPT implementation -- from picoGPT to GPT-NeoX -- is built on the same foundational components: self-attention, feed-forward networks, layer normalization, and residual connections. This chapter dissects each component with mathematical rigor and maps the math directly to PyTorch code.
@@ -553,3 +556,59 @@ In [Chapter 3: Tokenization & Embeddings](03-tokenization-embeddings.md), we wil
 
 ---
 *Built with insights from open-source GPT implementations.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `config`, `n_embd` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Transformer Architecture -- Self-Attention, Multi-Head Attention, and Feed-Forward Networks` as an operating subsystem inside **GPT Open Source: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `bias`, `mask`, `weight` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Transformer Architecture -- Self-Attention, Multi-Head Attention, and Feed-Forward Networks` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `config` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `n_embd`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [nanoGPT](https://github.com/karpathy/nanoGPT)
+  Why it matters: authoritative reference on `nanoGPT` (github.com).
+- [minGPT](https://github.com/karpathy/minGPT)
+  Why it matters: authoritative reference on `minGPT` (github.com).
+- [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
+  Why it matters: authoritative reference on `GPT-NeoX` (github.com).
+- [GPT-Neo](https://github.com/EleutherAI/gpt-neo)
+  Why it matters: authoritative reference on `GPT-Neo` (github.com).
+- [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax)
+  Why it matters: authoritative reference on `GPT-J` (github.com).
+- [Chapter 1: Getting Started](01-getting-started.md)
+  Why it matters: authoritative reference on `Chapter 1: Getting Started` (01-getting-started.md).
+
+Suggested trace strategy:
+- search upstream code for `self` and `config` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started -- Understanding the Open-Source GPT Landscape](01-getting-started.md)
+- [Next Chapter: Chapter 3: Tokenization & Embeddings -- BPE, Vocabulary Construction, and Positional Encodings](03-tokenization-embeddings.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gpt-oss-tutorial/03-tokenization-embeddings.md b/tutorials/gpt-oss-tutorial/03-tokenization-embeddings.md
index 45d9f0dd..e8531628 100644
--- a/tutorials/gpt-oss-tutorial/03-tokenization-embeddings.md
+++ b/tutorials/gpt-oss-tutorial/03-tokenization-embeddings.md
@@ -8,6 +8,9 @@ parent: "GPT Open Source - Deep Dive Tutorial"
 
 # Chapter 3: Tokenization & Embeddings -- BPE, Vocabulary Construction, and Positional Encodings
 
+Welcome to **Chapter 3: Tokenization & Embeddings -- BPE, Vocabulary Construction, and Positional Encodings**. In this part of **GPT Open Source: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 Before any text reaches the transformer, it must be converted into a sequence of integers. This process -- tokenization -- is one of the most consequential design decisions in building a GPT model. The choice of tokenizer affects vocabulary size, sequence length, multilingual capability, and even model performance on downstream tasks.
@@ -551,3 +554,59 @@ In [Chapter 4: Training Pipeline](04-training-pipeline.md), we will examine the
 
 ---
 *Built with insights from open-source GPT implementations.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `tokens`, `text`, `self` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Tokenization & Embeddings -- BPE, Vocabulary Construction, and Positional Encodings` as an operating subsystem inside **GPT Open Source: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `torch`, `print`, `pair` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Tokenization & Embeddings -- BPE, Vocabulary Construction, and Positional Encodings` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `tokens`.
+2. **Input normalization**: shape incoming data so `text` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `self`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [nanoGPT](https://github.com/karpathy/nanoGPT)
+  Why it matters: authoritative reference on `nanoGPT` (github.com).
+- [minGPT](https://github.com/karpathy/minGPT)
+  Why it matters: authoritative reference on `minGPT` (github.com).
+- [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
+  Why it matters: authoritative reference on `GPT-NeoX` (github.com).
+- [GPT-Neo](https://github.com/EleutherAI/gpt-neo)
+  Why it matters: authoritative reference on `GPT-Neo` (github.com).
+- [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax)
+  Why it matters: authoritative reference on `GPT-J` (github.com).
+- [Chapter 1: Getting Started](01-getting-started.md)
+  Why it matters: authoritative reference on `Chapter 1: Getting Started` (01-getting-started.md).
+
+Suggested trace strategy:
+- search upstream code for `tokens` and `text` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Transformer Architecture -- Self-Attention, Multi-Head Attention, and Feed-Forward Networks](02-transformer-architecture.md)
+- [Next Chapter: Chapter 4: Training Pipeline -- Data Loading, Loss Computation, Gradient Accumulation, and Mixed Precision](04-training-pipeline.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gpt-oss-tutorial/04-training-pipeline.md b/tutorials/gpt-oss-tutorial/04-training-pipeline.md
index ab2b2ff1..6687243d 100644
--- a/tutorials/gpt-oss-tutorial/04-training-pipeline.md
+++ b/tutorials/gpt-oss-tutorial/04-training-pipeline.md
@@ -8,6 +8,9 @@ parent: "GPT Open Source - Deep Dive Tutorial"
 
 # Chapter 4: Training Pipeline -- Data Loading, Loss Computation, Gradient Accumulation, and Mixed Precision
 
+Welcome to **Chapter 4: Training Pipeline -- Data Loading, Loss Computation, Gradient Accumulation, and Mixed Precision**. In this part of **GPT Open Source: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 The training pipeline is where theory meets practice. A well-engineered training loop can mean the difference between a successful model and wasted GPU hours. This chapter dissects the complete training pipeline used in nanoGPT, covering every component from data loading to checkpoint management.
@@ -604,3 +607,59 @@ In [Chapter 5: Attention Mechanisms](05-attention-mechanisms.md), we will explor
 
 ---
 *Built with insights from open-source GPT implementations.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `model`, `config`, `loss` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Training Pipeline -- Data Loading, Loss Computation, Gradient Accumulation, and Mixed Precision` as an operating subsystem inside **GPT Open Source: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `torch`, `optimizer`, `self` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Training Pipeline -- Data Loading, Loss Computation, Gradient Accumulation, and Mixed Precision` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `model`.
+2. **Input normalization**: shape incoming data so `config` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `loss`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [nanoGPT](https://github.com/karpathy/nanoGPT)
+  Why it matters: authoritative reference on `nanoGPT` (github.com).
+- [minGPT](https://github.com/karpathy/minGPT)
+  Why it matters: authoritative reference on `minGPT` (github.com).
+- [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
+  Why it matters: authoritative reference on `GPT-NeoX` (github.com).
+- [GPT-Neo](https://github.com/EleutherAI/gpt-neo)
+  Why it matters: authoritative reference on `GPT-Neo` (github.com).
+- [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax)
+  Why it matters: authoritative reference on `GPT-J` (github.com).
+- [Chapter 1: Getting Started](01-getting-started.md)
+  Why it matters: authoritative reference on `Chapter 1: Getting Started` (01-getting-started.md).
+
+Suggested trace strategy:
+- search upstream code for `model` and `config` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Tokenization & Embeddings -- BPE, Vocabulary Construction, and Positional Encodings](03-tokenization-embeddings.md)
+- [Next Chapter: Chapter 5: Attention Mechanisms -- Causal Masking, KV-Cache, Multi-Query Attention, and Flash Attention](05-attention-mechanisms.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gpt-oss-tutorial/05-attention-mechanisms.md b/tutorials/gpt-oss-tutorial/05-attention-mechanisms.md
index 12a6818c..15f77682 100644
--- a/tutorials/gpt-oss-tutorial/05-attention-mechanisms.md
+++ b/tutorials/gpt-oss-tutorial/05-attention-mechanisms.md
@@ -8,6 +8,9 @@ parent: "GPT Open Source - Deep Dive Tutorial"
 
 # Chapter 5: Attention Mechanisms -- Causal Masking, KV-Cache, Multi-Query Attention, and Flash Attention
 
+Welcome to **Chapter 5: Attention Mechanisms -- Causal Masking, KV-Cache, Multi-Query Attention, and Flash Attention**. In this part of **GPT Open Source: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 Attention is the computational bottleneck of GPT models. Its memory and compute requirements scale quadratically with sequence length, making it the primary target for optimization. This chapter explores both the foundational mechanisms and the cutting-edge techniques that make modern GPT inference and training feasible at scale.
@@ -519,3 +522,59 @@ In [Chapter 6: Scaling & Distributed Training](06-scaling-distributed-training.m
 
 ---
 *Built with insights from open-source GPT implementations.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `config`, `n_embd` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Attention Mechanisms -- Causal Masking, KV-Cache, Multi-Query Attention, and Flash Attention` as an operating subsystem inside **GPT Open Source: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `n_head`, `head_dim`, `torch` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Attention Mechanisms -- Causal Masking, KV-Cache, Multi-Query Attention, and Flash Attention` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `config` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `n_embd`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [nanoGPT](https://github.com/karpathy/nanoGPT)
+  Why it matters: authoritative reference on `nanoGPT` (github.com).
+- [minGPT](https://github.com/karpathy/minGPT)
+  Why it matters: authoritative reference on `minGPT` (github.com).
+- [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
+  Why it matters: authoritative reference on `GPT-NeoX` (github.com).
+- [GPT-Neo](https://github.com/EleutherAI/gpt-neo)
+  Why it matters: authoritative reference on `GPT-Neo` (github.com).
+- [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax)
+  Why it matters: authoritative reference on `GPT-J` (github.com).
+- [Chapter 1: Getting Started](01-getting-started.md)
+  Why it matters: authoritative reference on `Chapter 1: Getting Started` (01-getting-started.md).
+
+Suggested trace strategy:
+- search upstream code for `self` and `config` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Training Pipeline -- Data Loading, Loss Computation, Gradient Accumulation, and Mixed Precision](04-training-pipeline.md)
+- [Next Chapter: Chapter 6: Scaling & Distributed Training -- Model Parallelism, Data Parallelism, ZeRO, and FSDP](06-scaling-distributed-training.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gpt-oss-tutorial/06-scaling-distributed-training.md b/tutorials/gpt-oss-tutorial/06-scaling-distributed-training.md
index 6e28a219..37361cfa 100644
--- a/tutorials/gpt-oss-tutorial/06-scaling-distributed-training.md
+++ b/tutorials/gpt-oss-tutorial/06-scaling-distributed-training.md
@@ -8,6 +8,9 @@ parent: "GPT Open Source - Deep Dive Tutorial"
 
 # Chapter 6: Scaling & Distributed Training -- Model Parallelism, Data Parallelism, ZeRO, and FSDP
 
+Welcome to **Chapter 6: Scaling & Distributed Training -- Model Parallelism, Data Parallelism, ZeRO, and FSDP**. In this part of **GPT Open Source: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 Training GPT models beyond a few hundred million parameters requires distributing computation across multiple GPUs. A single GPU, even with 80GB of memory, cannot hold the model weights, optimizer states, gradients, and activations for a multi-billion parameter model.
@@ -616,3 +619,59 @@ In [Chapter 7: Fine-Tuning & Alignment](07-fine-tuning-alignment.md), we will ex
 
 ---
 *Built with insights from open-source GPT implementations.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `model`, `classDef` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Scaling & Distributed Training -- Model Parallelism, Data Parallelism, ZeRO, and FSDP` as an operating subsystem inside **GPT Open Source: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `fill`, `stroke`, `torch` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Scaling & Distributed Training -- Model Parallelism, Data Parallelism, ZeRO, and FSDP` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `model` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `classDef`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [nanoGPT](https://github.com/karpathy/nanoGPT)
+  Why it matters: authoritative reference on `nanoGPT` (github.com).
+- [minGPT](https://github.com/karpathy/minGPT)
+  Why it matters: authoritative reference on `minGPT` (github.com).
+- [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
+  Why it matters: authoritative reference on `GPT-NeoX` (github.com).
+- [GPT-Neo](https://github.com/EleutherAI/gpt-neo)
+  Why it matters: authoritative reference on `GPT-Neo` (github.com).
+- [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax)
+  Why it matters: authoritative reference on `GPT-J` (github.com).
+- [Chapter 1: Getting Started](01-getting-started.md)
+  Why it matters: authoritative reference on `Chapter 1: Getting Started` (01-getting-started.md).
+
+Suggested trace strategy:
+- search upstream code for `self` and `model` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Attention Mechanisms -- Causal Masking, KV-Cache, Multi-Query Attention, and Flash Attention](05-attention-mechanisms.md)
+- [Next Chapter: Chapter 7: Fine-Tuning & Alignment -- LoRA, QLoRA, RLHF, DPO, and Instruction Tuning](07-fine-tuning-alignment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gpt-oss-tutorial/07-fine-tuning-alignment.md b/tutorials/gpt-oss-tutorial/07-fine-tuning-alignment.md
index 7fa43f0d..51982921 100644
--- a/tutorials/gpt-oss-tutorial/07-fine-tuning-alignment.md
+++ b/tutorials/gpt-oss-tutorial/07-fine-tuning-alignment.md
@@ -8,6 +8,9 @@ parent: "GPT Open Source - Deep Dive Tutorial"
 
 # Chapter 7: Fine-Tuning & Alignment -- LoRA, QLoRA, RLHF, DPO, and Instruction Tuning
 
+Welcome to **Chapter 7: Fine-Tuning & Alignment -- LoRA, QLoRA, RLHF, DPO, and Instruction Tuning**. In this part of **GPT Open Source: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 Pre-training gives a GPT model broad language understanding, but it does not make the model useful for specific tasks or safe to deploy. Fine-tuning adapts the model to particular domains, while alignment techniques teach it to follow instructions and produce helpful, harmless outputs.
@@ -643,3 +646,59 @@ In [Chapter 8: Production Inference](08-production-inference.md), we will cover
 
 ---
 *Built with insights from open-source GPT implementations.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `model`, `self`, `loss` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Fine-Tuning & Alignment -- LoRA, QLoRA, RLHF, DPO, and Instruction Tuning` as an operating subsystem inside **GPT Open Source: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `torch`, `LoRA`, `classDef` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Fine-Tuning & Alignment -- LoRA, QLoRA, RLHF, DPO, and Instruction Tuning` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `model`.
+2. **Input normalization**: shape incoming data so `self` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `loss`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [nanoGPT](https://github.com/karpathy/nanoGPT)
+  Why it matters: authoritative reference on `nanoGPT` (github.com).
+- [minGPT](https://github.com/karpathy/minGPT)
+  Why it matters: authoritative reference on `minGPT` (github.com).
+- [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
+  Why it matters: authoritative reference on `GPT-NeoX` (github.com).
+- [GPT-Neo](https://github.com/EleutherAI/gpt-neo)
+  Why it matters: authoritative reference on `GPT-Neo` (github.com).
+- [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax)
+  Why it matters: authoritative reference on `GPT-J` (github.com).
+- [Chapter 1: Getting Started](01-getting-started.md)
+  Why it matters: authoritative reference on `Chapter 1: Getting Started` (01-getting-started.md).
+
+Suggested trace strategy:
+- search upstream code for `model` and `self` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Scaling & Distributed Training -- Model Parallelism, Data Parallelism, ZeRO, and FSDP](06-scaling-distributed-training.md)
+- [Next Chapter: Chapter 8: Production Inference -- Quantization, Batching, Speculative Decoding, and Deployment](08-production-inference.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gpt-oss-tutorial/08-production-inference.md b/tutorials/gpt-oss-tutorial/08-production-inference.md
index 5cce3541..b7e276e8 100644
--- a/tutorials/gpt-oss-tutorial/08-production-inference.md
+++ b/tutorials/gpt-oss-tutorial/08-production-inference.md
@@ -8,6 +8,9 @@ parent: "GPT Open Source - Deep Dive Tutorial"
 
 # Chapter 8: Production Inference -- Quantization, Batching, Speculative Decoding, and Deployment
 
+Welcome to **Chapter 8: Production Inference -- Quantization, Batching, Speculative Decoding, and Deployment**. In this part of **GPT Open Source: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 Taking a GPT model from a research checkpoint to a production service requires a different set of skills from training. Production inference is about maximizing throughput, minimizing latency, and reducing cost -- all while maintaining acceptable output quality.
@@ -674,3 +677,58 @@ For further exploration, consider:
 
 ---
 *Built with insights from open-source GPT implementations.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `model`, `torch`, `classDef` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Inference -- Quantization, Batching, Speculative Decoding, and Deployment` as an operating subsystem inside **GPT Open Source: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `fill`, `stroke`, `generated` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Inference -- Quantization, Batching, Speculative Decoding, and Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `model`.
+2. **Input normalization**: shape incoming data so `torch` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `classDef`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [nanoGPT](https://github.com/karpathy/nanoGPT)
+  Why it matters: authoritative reference on `nanoGPT` (github.com).
+- [minGPT](https://github.com/karpathy/minGPT)
+  Why it matters: authoritative reference on `minGPT` (github.com).
+- [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
+  Why it matters: authoritative reference on `GPT-NeoX` (github.com).
+- [GPT-Neo](https://github.com/EleutherAI/gpt-neo)
+  Why it matters: authoritative reference on `GPT-Neo` (github.com).
+- [GPT-J](https://github.com/kingoflolz/mesh-transformer-jax)
+  Why it matters: authoritative reference on `GPT-J` (github.com).
+- [Chapter 1: Getting Started](01-getting-started.md)
+  Why it matters: authoritative reference on `Chapter 1: Getting Started` (01-getting-started.md).
+
+Suggested trace strategy:
+- search upstream code for `model` and `torch` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Fine-Tuning & Alignment -- LoRA, QLoRA, RLHF, DPO, and Instruction Tuning](07-fine-tuning-alignment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gptme-tutorial/01-getting-started.md b/tutorials/gptme-tutorial/01-getting-started.md
index 09afaa04..ecc10fad 100644
--- a/tutorials/gptme-tutorial/01-getting-started.md
+++ b/tutorials/gptme-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: gptme Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets gptme installed and running in a local terminal session.
 
 ## Quick Install
@@ -34,3 +37,601 @@ If provider keys are missing, gptme prompts to configure them.
 You now have gptme installed and ready for interactive local workflows.
 
 Next: [Chapter 2: Core CLI Workflow and Prompt Patterns](02-core-cli-workflow-and-prompt-patterns.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- tutorial slug: **gptme-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Gptme 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
+
+- [gptme Repository](https://github.com/gptme/gptme)
+- [gptme README](https://github.com/gptme/gptme/blob/master/README.md)
+- [Getting Started docs](https://github.com/gptme/gptme/blob/master/docs/getting-started.rst)
+- [Configuration docs](https://github.com/gptme/gptme/blob/master/docs/config.rst)
+- [CLI entrypoint](https://github.com/gptme/gptme/blob/master/gptme/cli/main.py)
+
+### Cross-Tutorial Connection Map
+
+- [Mistral Vibe Tutorial](../mistral-vibe-tutorial/)
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Claude Squad Tutorial](../claude-squad-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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `gptme`, `install`, `pipx` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `tool` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `gptme`.
+2. **Input normalization**: shape incoming data so `install` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `pipx`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [gptme Repository](https://github.com/gptme/gptme)
+  Why it matters: authoritative reference on `gptme Repository` (github.com).
+- [gptme README](https://github.com/gptme/gptme/blob/master/README.md)
+  Why it matters: authoritative reference on `gptme README` (github.com).
+- [Getting Started docs](https://github.com/gptme/gptme/blob/master/docs/getting-started.rst)
+  Why it matters: authoritative reference on `Getting Started docs` (github.com).
+- [Configuration docs](https://github.com/gptme/gptme/blob/master/docs/config.rst)
+  Why it matters: authoritative reference on `Configuration docs` (github.com).
+- [CLI entrypoint](https://github.com/gptme/gptme/blob/master/gptme/cli/main.py)
+  Why it matters: authoritative reference on `CLI entrypoint` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `gptme` and `install` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Core CLI Workflow and Prompt Patterns](02-core-cli-workflow-and-prompt-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gptme-tutorial/02-core-cli-workflow-and-prompt-patterns.md b/tutorials/gptme-tutorial/02-core-cli-workflow-and-prompt-patterns.md
index ed8b56b7..c077536b 100644
--- a/tutorials/gptme-tutorial/02-core-cli-workflow-and-prompt-patterns.md
+++ b/tutorials/gptme-tutorial/02-core-cli-workflow-and-prompt-patterns.md
@@ -7,6 +7,9 @@ parent: gptme Tutorial
 
 # Chapter 2: Core CLI Workflow and Prompt Patterns
 
+Welcome to **Chapter 2: Core CLI Workflow and Prompt Patterns**. In this part of **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 gptme supports direct prompt invocation, chained prompts, and resumed sessions for iterative development.
 
 ## Workflow Patterns
@@ -32,3 +35,610 @@ Use chained prompts to enforce staged execution (change -> test -> commit) inste
 You now know how to structure repeatable prompt flows and resume long-running conversations.
 
 Next: [Chapter 3: Tooling and Local Execution Boundaries](03-tooling-and-local-execution-boundaries.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- tutorial slug: **gptme-tutorial**
+- chapter focus: **Chapter 2: Core CLI Workflow and Prompt Patterns**
+- system context: **Gptme Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Core CLI Workflow and Prompt 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
+
+- [gptme Repository](https://github.com/gptme/gptme)
+- [gptme README](https://github.com/gptme/gptme/blob/master/README.md)
+- [Getting Started docs](https://github.com/gptme/gptme/blob/master/docs/getting-started.rst)
+- [Configuration docs](https://github.com/gptme/gptme/blob/master/docs/config.rst)
+- [CLI entrypoint](https://github.com/gptme/gptme/blob/master/gptme/cli/main.py)
+
+### Cross-Tutorial Connection Map
+
+- [Mistral Vibe Tutorial](../mistral-vibe-tutorial/)
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Claude Squad Tutorial](../claude-squad-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Core CLI Workflow and Prompt 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 2: Core CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Core CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Core CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Core CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Core CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Core CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Core CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Core CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Core CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Core CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Core CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Core CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Core CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Core CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 2: Core CLI Workflow and Prompt Patterns
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Core CLI Workflow and Prompt Patterns` as an operating subsystem inside **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Core CLI Workflow and Prompt Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [gptme Repository](https://github.com/gptme/gptme)
+  Why it matters: authoritative reference on `gptme Repository` (github.com).
+- [gptme README](https://github.com/gptme/gptme/blob/master/README.md)
+  Why it matters: authoritative reference on `gptme README` (github.com).
+- [Getting Started docs](https://github.com/gptme/gptme/blob/master/docs/getting-started.rst)
+  Why it matters: authoritative reference on `Getting Started docs` (github.com).
+- [Configuration docs](https://github.com/gptme/gptme/blob/master/docs/config.rst)
+  Why it matters: authoritative reference on `Configuration docs` (github.com).
+- [CLI entrypoint](https://github.com/gptme/gptme/blob/master/gptme/cli/main.py)
+  Why it matters: authoritative reference on `CLI entrypoint` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Tooling and Local Execution Boundaries](03-tooling-and-local-execution-boundaries.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gptme-tutorial/03-tooling-and-local-execution-boundaries.md b/tutorials/gptme-tutorial/03-tooling-and-local-execution-boundaries.md
index 2f0b0961..466e593b 100644
--- a/tutorials/gptme-tutorial/03-tooling-and-local-execution-boundaries.md
+++ b/tutorials/gptme-tutorial/03-tooling-and-local-execution-boundaries.md
@@ -7,6 +7,9 @@ parent: gptme Tutorial
 
 # Chapter 3: Tooling and Local Execution Boundaries
 
+Welcome to **Chapter 3: Tooling and Local Execution Boundaries**. In this part of **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 gptme exposes tools for file editing, shell execution, web browsing, and more inside a local execution loop.
 
 ## Tooling Coverage
@@ -32,3 +35,610 @@ gptme exposes tools for file editing, shell execution, web browsing, and more in
 You now understand how gptme's local tool loop works and how to control risk boundaries.
 
 Next: [Chapter 4: Configuration Layers and Environment Strategy](04-configuration-layers-and-environment-strategy.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- tutorial slug: **gptme-tutorial**
+- chapter focus: **Chapter 3: Tooling and Local Execution Boundaries**
+- system context: **Gptme Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Tooling and Local Execution Boundaries`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [gptme Repository](https://github.com/gptme/gptme)
+- [gptme README](https://github.com/gptme/gptme/blob/master/README.md)
+- [Getting Started docs](https://github.com/gptme/gptme/blob/master/docs/getting-started.rst)
+- [Configuration docs](https://github.com/gptme/gptme/blob/master/docs/config.rst)
+- [CLI entrypoint](https://github.com/gptme/gptme/blob/master/gptme/cli/main.py)
+
+### Cross-Tutorial Connection Map
+
+- [Mistral Vibe Tutorial](../mistral-vibe-tutorial/)
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Claude Squad Tutorial](../claude-squad-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Tooling and Local Execution Boundaries`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 3: Tooling and Local Execution Boundaries
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Tooling and Local Execution Boundaries` as an operating subsystem inside **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Tooling and Local Execution Boundaries` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [gptme Repository](https://github.com/gptme/gptme)
+  Why it matters: authoritative reference on `gptme Repository` (github.com).
+- [gptme README](https://github.com/gptme/gptme/blob/master/README.md)
+  Why it matters: authoritative reference on `gptme README` (github.com).
+- [Getting Started docs](https://github.com/gptme/gptme/blob/master/docs/getting-started.rst)
+  Why it matters: authoritative reference on `Getting Started docs` (github.com).
+- [Configuration docs](https://github.com/gptme/gptme/blob/master/docs/config.rst)
+  Why it matters: authoritative reference on `Configuration docs` (github.com).
+- [CLI entrypoint](https://github.com/gptme/gptme/blob/master/gptme/cli/main.py)
+  Why it matters: authoritative reference on `CLI entrypoint` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Core CLI Workflow and Prompt Patterns](02-core-cli-workflow-and-prompt-patterns.md)
+- [Next Chapter: Chapter 4: Configuration Layers and Environment Strategy](04-configuration-layers-and-environment-strategy.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gptme-tutorial/04-configuration-layers-and-environment-strategy.md b/tutorials/gptme-tutorial/04-configuration-layers-and-environment-strategy.md
index 3f9ccf34..96c2e7fb 100644
--- a/tutorials/gptme-tutorial/04-configuration-layers-and-environment-strategy.md
+++ b/tutorials/gptme-tutorial/04-configuration-layers-and-environment-strategy.md
@@ -7,6 +7,9 @@ parent: gptme Tutorial
 
 # Chapter 4: Configuration Layers and Environment Strategy
 
+Welcome to **Chapter 4: Configuration Layers and Environment Strategy**. In this part of **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 gptme uses layered configuration across global, project, and chat scopes, with environment variables taking precedence.
 
 ## Config Layers
@@ -31,3 +34,610 @@ Use env vars for secrets and per-environment overrides; keep reusable behavior i
 You now have a deterministic strategy for managing gptme configuration across environments.
 
 Next: [Chapter 5: Context, Lessons, and Conversation Management](05-context-lessons-and-conversation-management.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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- tutorial slug: **gptme-tutorial**
+- chapter focus: **Chapter 4: Configuration Layers and Environment Strategy**
+- system context: **Gptme Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Configuration Layers and Environment Strategy`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [gptme Repository](https://github.com/gptme/gptme)
+- [gptme README](https://github.com/gptme/gptme/blob/master/README.md)
+- [Getting Started docs](https://github.com/gptme/gptme/blob/master/docs/getting-started.rst)
+- [Configuration docs](https://github.com/gptme/gptme/blob/master/docs/config.rst)
+- [CLI entrypoint](https://github.com/gptme/gptme/blob/master/gptme/cli/main.py)
+
+### Cross-Tutorial Connection Map
+
+- [Mistral Vibe Tutorial](../mistral-vibe-tutorial/)
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Claude Squad Tutorial](../claude-squad-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Configuration Layers and Environment Strategy`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 4: Configuration Layers and Environment Strategy
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Configuration Layers and Environment Strategy` as an operating subsystem inside **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Configuration Layers and Environment Strategy` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [gptme Repository](https://github.com/gptme/gptme)
+  Why it matters: authoritative reference on `gptme Repository` (github.com).
+- [gptme README](https://github.com/gptme/gptme/blob/master/README.md)
+  Why it matters: authoritative reference on `gptme README` (github.com).
+- [Getting Started docs](https://github.com/gptme/gptme/blob/master/docs/getting-started.rst)
+  Why it matters: authoritative reference on `Getting Started docs` (github.com).
+- [Configuration docs](https://github.com/gptme/gptme/blob/master/docs/config.rst)
+  Why it matters: authoritative reference on `Configuration docs` (github.com).
+- [CLI entrypoint](https://github.com/gptme/gptme/blob/master/gptme/cli/main.py)
+  Why it matters: authoritative reference on `CLI entrypoint` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Tooling and Local Execution Boundaries](03-tooling-and-local-execution-boundaries.md)
+- [Next Chapter: Chapter 5: Context, Lessons, and Conversation Management](05-context-lessons-and-conversation-management.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gptme-tutorial/05-context-lessons-and-conversation-management.md b/tutorials/gptme-tutorial/05-context-lessons-and-conversation-management.md
index e8838a96..469335bf 100644
--- a/tutorials/gptme-tutorial/05-context-lessons-and-conversation-management.md
+++ b/tutorials/gptme-tutorial/05-context-lessons-and-conversation-management.md
@@ -7,6 +7,9 @@ parent: gptme Tutorial
 
 # Chapter 5: Context, Lessons, and Conversation Management
 
+Welcome to **Chapter 5: Context, Lessons, and Conversation Management**. In this part of **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 gptme includes context controls and a lessons system to improve consistency over long and repeated tasks.
 
 ## Key Mechanisms
@@ -31,3 +34,610 @@ Combine concise lesson files with context compression to keep long autonomous se
 You now know how to preserve quality and consistency as conversation history grows.
 
 Next: [Chapter 6: MCP, ACP, and Plugin Extensibility](06-mcp-acp-and-plugin-extensibility.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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- tutorial slug: **gptme-tutorial**
+- chapter focus: **Chapter 5: Context, Lessons, and Conversation Management**
+- system context: **Gptme Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Context, Lessons, and Conversation Management`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [gptme Repository](https://github.com/gptme/gptme)
+- [gptme README](https://github.com/gptme/gptme/blob/master/README.md)
+- [Getting Started docs](https://github.com/gptme/gptme/blob/master/docs/getting-started.rst)
+- [Configuration docs](https://github.com/gptme/gptme/blob/master/docs/config.rst)
+- [CLI entrypoint](https://github.com/gptme/gptme/blob/master/gptme/cli/main.py)
+
+### Cross-Tutorial Connection Map
+
+- [Mistral Vibe Tutorial](../mistral-vibe-tutorial/)
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Claude Squad Tutorial](../claude-squad-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Context, Lessons, and Conversation Management`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 5: Context, Lessons, and Conversation Management
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Context, Lessons, and Conversation Management` as an operating subsystem inside **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Context, Lessons, and Conversation Management` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [gptme Repository](https://github.com/gptme/gptme)
+  Why it matters: authoritative reference on `gptme Repository` (github.com).
+- [gptme README](https://github.com/gptme/gptme/blob/master/README.md)
+  Why it matters: authoritative reference on `gptme README` (github.com).
+- [Getting Started docs](https://github.com/gptme/gptme/blob/master/docs/getting-started.rst)
+  Why it matters: authoritative reference on `Getting Started docs` (github.com).
+- [Configuration docs](https://github.com/gptme/gptme/blob/master/docs/config.rst)
+  Why it matters: authoritative reference on `Configuration docs` (github.com).
+- [CLI entrypoint](https://github.com/gptme/gptme/blob/master/gptme/cli/main.py)
+  Why it matters: authoritative reference on `CLI entrypoint` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Configuration Layers and Environment Strategy](04-configuration-layers-and-environment-strategy.md)
+- [Next Chapter: Chapter 6: MCP, ACP, and Plugin Extensibility](06-mcp-acp-and-plugin-extensibility.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gptme-tutorial/06-mcp-acp-and-plugin-extensibility.md b/tutorials/gptme-tutorial/06-mcp-acp-and-plugin-extensibility.md
index e93ac076..7c75a02d 100644
--- a/tutorials/gptme-tutorial/06-mcp-acp-and-plugin-extensibility.md
+++ b/tutorials/gptme-tutorial/06-mcp-acp-and-plugin-extensibility.md
@@ -7,6 +7,9 @@ parent: gptme Tutorial
 
 # Chapter 6: MCP, ACP, and Plugin Extensibility
 
+Welcome to **Chapter 6: MCP, ACP, and Plugin Extensibility**. In this part of **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 gptme supports protocol and plugin extensions for richer integrations with external tools and clients.
 
 ## Extension Surfaces
@@ -32,3 +35,610 @@ gptme supports protocol and plugin extensions for richer integrations with exter
 You now have an extensibility model for connecting gptme to broader tool ecosystems.
 
 Next: [Chapter 7: Automation, Server Mode, and Agent Templates](07-automation-server-mode-and-agent-templates.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: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- tutorial slug: **gptme-tutorial**
+- chapter focus: **Chapter 6: MCP, ACP, and Plugin Extensibility**
+- system context: **Gptme Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: MCP, ACP, and Plugin Extensibility`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [gptme Repository](https://github.com/gptme/gptme)
+- [gptme README](https://github.com/gptme/gptme/blob/master/README.md)
+- [Getting Started docs](https://github.com/gptme/gptme/blob/master/docs/getting-started.rst)
+- [Configuration docs](https://github.com/gptme/gptme/blob/master/docs/config.rst)
+- [CLI entrypoint](https://github.com/gptme/gptme/blob/master/gptme/cli/main.py)
+
+### Cross-Tutorial Connection Map
+
+- [Mistral Vibe Tutorial](../mistral-vibe-tutorial/)
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Claude Squad Tutorial](../claude-squad-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: MCP, ACP, and Plugin Extensibility`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 6: MCP, ACP, and Plugin Extensibility
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: MCP, ACP, and Plugin Extensibility` as an operating subsystem inside **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: MCP, ACP, and Plugin Extensibility` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [gptme Repository](https://github.com/gptme/gptme)
+  Why it matters: authoritative reference on `gptme Repository` (github.com).
+- [gptme README](https://github.com/gptme/gptme/blob/master/README.md)
+  Why it matters: authoritative reference on `gptme README` (github.com).
+- [Getting Started docs](https://github.com/gptme/gptme/blob/master/docs/getting-started.rst)
+  Why it matters: authoritative reference on `Getting Started docs` (github.com).
+- [Configuration docs](https://github.com/gptme/gptme/blob/master/docs/config.rst)
+  Why it matters: authoritative reference on `Configuration docs` (github.com).
+- [CLI entrypoint](https://github.com/gptme/gptme/blob/master/gptme/cli/main.py)
+  Why it matters: authoritative reference on `CLI entrypoint` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Context, Lessons, and Conversation Management](05-context-lessons-and-conversation-management.md)
+- [Next Chapter: Chapter 7: Automation, Server Mode, and Agent Templates](07-automation-server-mode-and-agent-templates.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gptme-tutorial/07-automation-server-mode-and-agent-templates.md b/tutorials/gptme-tutorial/07-automation-server-mode-and-agent-templates.md
index 578f0934..05cc5f7d 100644
--- a/tutorials/gptme-tutorial/07-automation-server-mode-and-agent-templates.md
+++ b/tutorials/gptme-tutorial/07-automation-server-mode-and-agent-templates.md
@@ -7,6 +7,9 @@ parent: gptme Tutorial
 
 # Chapter 7: Automation, Server Mode, and Agent Templates
 
+Welcome to **Chapter 7: Automation, Server Mode, and Agent Templates**. In this part of **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Beyond ad hoc use, gptme supports automation patterns and broader deployment modes.
 
 ## Deployment Patterns
@@ -28,3 +31,610 @@ Beyond ad hoc use, gptme supports automation patterns and broader deployment mod
 You now have pathways to operationalize gptme beyond an individual interactive shell.
 
 Next: [Chapter 8: Production Operations and Security](08-production-operations-and-security.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- tutorial slug: **gptme-tutorial**
+- chapter focus: **Chapter 7: Automation, Server Mode, and Agent Templates**
+- system context: **Gptme Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Automation, Server Mode, and Agent Templates`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [gptme Repository](https://github.com/gptme/gptme)
+- [gptme README](https://github.com/gptme/gptme/blob/master/README.md)
+- [Getting Started docs](https://github.com/gptme/gptme/blob/master/docs/getting-started.rst)
+- [Configuration docs](https://github.com/gptme/gptme/blob/master/docs/config.rst)
+- [CLI entrypoint](https://github.com/gptme/gptme/blob/master/gptme/cli/main.py)
+
+### Cross-Tutorial Connection Map
+
+- [Mistral Vibe Tutorial](../mistral-vibe-tutorial/)
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Claude Squad Tutorial](../claude-squad-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Automation, Server Mode, and Agent Templates`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 7: Automation, Server Mode, and Agent Templates
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Automation, Server Mode, and Agent Templates` as an operating subsystem inside **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Automation, Server Mode, and Agent Templates` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [gptme Repository](https://github.com/gptme/gptme)
+  Why it matters: authoritative reference on `gptme Repository` (github.com).
+- [gptme README](https://github.com/gptme/gptme/blob/master/README.md)
+  Why it matters: authoritative reference on `gptme README` (github.com).
+- [Getting Started docs](https://github.com/gptme/gptme/blob/master/docs/getting-started.rst)
+  Why it matters: authoritative reference on `Getting Started docs` (github.com).
+- [Configuration docs](https://github.com/gptme/gptme/blob/master/docs/config.rst)
+  Why it matters: authoritative reference on `Configuration docs` (github.com).
+- [CLI entrypoint](https://github.com/gptme/gptme/blob/master/gptme/cli/main.py)
+  Why it matters: authoritative reference on `CLI entrypoint` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: MCP, ACP, and Plugin Extensibility](06-mcp-acp-and-plugin-extensibility.md)
+- [Next Chapter: Chapter 8: Production Operations and Security](08-production-operations-and-security.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/gptme-tutorial/08-production-operations-and-security.md b/tutorials/gptme-tutorial/08-production-operations-and-security.md
index 9fec78a2..e02d43b6 100644
--- a/tutorials/gptme-tutorial/08-production-operations-and-security.md
+++ b/tutorials/gptme-tutorial/08-production-operations-and-security.md
@@ -7,6 +7,9 @@ parent: gptme Tutorial
 
 # Chapter 8: Production Operations and Security
 
+Welcome to **Chapter 8: Production Operations and Security**. In this part of **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Production gptme workflows require clear policy on tool permissions, secret handling, and trusted repositories.
 
 ## Security Checklist
@@ -24,3 +27,609 @@ Production gptme workflows require clear policy on tool permissions, secret hand
 ## Summary
 
 You now have a security and operations baseline for running gptme in production environments.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- tutorial slug: **gptme-tutorial**
+- chapter focus: **Chapter 8: Production Operations and Security**
+- system context: **Gptme Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Operations 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
+
+- [gptme Repository](https://github.com/gptme/gptme)
+- [gptme README](https://github.com/gptme/gptme/blob/master/README.md)
+- [Getting Started docs](https://github.com/gptme/gptme/blob/master/docs/getting-started.rst)
+- [Configuration docs](https://github.com/gptme/gptme/blob/master/docs/config.rst)
+- [CLI entrypoint](https://github.com/gptme/gptme/blob/master/gptme/cli/main.py)
+
+### Cross-Tutorial Connection Map
+
+- [Mistral Vibe Tutorial](../mistral-vibe-tutorial/)
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Claude Squad Tutorial](../claude-squad-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Operations and Security`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Production Operations and Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Production Operations and Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Production Operations and Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Production Operations and Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Production Operations and Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Operations and Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Operations and Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Production Operations and Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 8: Production Operations and Security
+
+- tutorial context: **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Operations and Security` as an operating subsystem inside **gptme Tutorial: Open-Source Terminal Agent for Local Tool-Driven Work**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Operations and Security` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [gptme Repository](https://github.com/gptme/gptme)
+  Why it matters: authoritative reference on `gptme Repository` (github.com).
+- [gptme README](https://github.com/gptme/gptme/blob/master/README.md)
+  Why it matters: authoritative reference on `gptme README` (github.com).
+- [Getting Started docs](https://github.com/gptme/gptme/blob/master/docs/getting-started.rst)
+  Why it matters: authoritative reference on `Getting Started docs` (github.com).
+- [Configuration docs](https://github.com/gptme/gptme/blob/master/docs/config.rst)
+  Why it matters: authoritative reference on `Configuration docs` (github.com).
+- [CLI entrypoint](https://github.com/gptme/gptme/blob/master/gptme/cli/main.py)
+  Why it matters: authoritative reference on `CLI entrypoint` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Automation, Server Mode, and Agent Templates](07-automation-server-mode-and-agent-templates.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/hapi-tutorial/01-getting-started.md b/tutorials/hapi-tutorial/01-getting-started.md
index dd381409..05dce306 100644
--- a/tutorials/hapi-tutorial/01-getting-started.md
+++ b/tutorials/hapi-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: HAPI Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **HAPI Tutorial: Remote Control for Local AI Coding Sessions**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets HAPI installed and verifies a full terminal-to-mobile control loop.
 
 ## Prerequisites
@@ -46,3 +49,52 @@ hapi
 You now have a working HAPI baseline with remote control enabled.
 
 Next: [Chapter 2: System Architecture](02-system-architecture.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `hapi`, `install`, `twsxtd` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **HAPI Tutorial: Remote Control for Local AI Coding Sessions**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `relay` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `hapi`.
+2. **Input normalization**: shape incoming data so `install` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `twsxtd`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [HAPI Repository](https://github.com/tiann/hapi)
+  Why it matters: authoritative reference on `HAPI Repository` (github.com).
+- [HAPI Releases](https://github.com/tiann/hapi/releases)
+  Why it matters: authoritative reference on `HAPI Releases` (github.com).
+- [HAPI Docs](https://hapi.run)
+  Why it matters: authoritative reference on `HAPI Docs` (hapi.run).
+
+Suggested trace strategy:
+- search upstream code for `hapi` and `install` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: System Architecture](02-system-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/hapi-tutorial/02-system-architecture.md b/tutorials/hapi-tutorial/02-system-architecture.md
index d9b6976f..6cfb90b8 100644
--- a/tutorials/hapi-tutorial/02-system-architecture.md
+++ b/tutorials/hapi-tutorial/02-system-architecture.md
@@ -7,6 +7,9 @@ parent: HAPI Tutorial
 
 # Chapter 2: System Architecture
 
+Welcome to **Chapter 2: System Architecture**. In this part of **HAPI Tutorial: Remote Control for Local AI Coding Sessions**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 HAPI is a control plane around local coding agents: CLI wrapper, hub service, and remote clients.
 
 ## Architecture Diagram
@@ -40,3 +43,53 @@ graph TD
 You now understand where HAPI stores state and routes interactive control.
 
 Next: [Chapter 3: Session Lifecycle and Handoff](03-session-lifecycle-and-handoff.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `graph`, `HAPI`, `Agent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: System Architecture` as an operating subsystem inside **HAPI Tutorial: Remote Control for Local AI Coding Sessions**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Socket`, `SQLite`, `Client` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: System Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `graph`.
+2. **Input normalization**: shape incoming data so `HAPI` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Agent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [HAPI Repository](https://github.com/tiann/hapi)
+  Why it matters: authoritative reference on `HAPI Repository` (github.com).
+- [HAPI Releases](https://github.com/tiann/hapi/releases)
+  Why it matters: authoritative reference on `HAPI Releases` (github.com).
+- [HAPI Docs](https://hapi.run)
+  Why it matters: authoritative reference on `HAPI Docs` (hapi.run).
+
+Suggested trace strategy:
+- search upstream code for `graph` and `HAPI` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Session Lifecycle and Handoff](03-session-lifecycle-and-handoff.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/hapi-tutorial/03-session-lifecycle-and-handoff.md b/tutorials/hapi-tutorial/03-session-lifecycle-and-handoff.md
index a15ef70c..6a2d36ac 100644
--- a/tutorials/hapi-tutorial/03-session-lifecycle-and-handoff.md
+++ b/tutorials/hapi-tutorial/03-session-lifecycle-and-handoff.md
@@ -7,6 +7,9 @@ parent: HAPI Tutorial
 
 # Chapter 3: Session Lifecycle and Handoff
 
+Welcome to **Chapter 3: Session Lifecycle and Handoff**. In this part of **HAPI Tutorial: Remote Control for Local AI Coding Sessions**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 HAPI's key design goal is switching control surfaces without restarting or losing session context.
 
 ## Lifecycle Flow
@@ -38,3 +41,53 @@ graph LR
 You can now model HAPI sessions as persistent control channels, not transient terminal jobs.
 
 Next: [Chapter 4: Remote Access and Networking](04-remote-access-and-networking.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `graph`, `Start`, `hapi` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Session Lifecycle and Handoff` as an operating subsystem inside **HAPI Tutorial: Remote Control for Local AI Coding Sessions**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `terminal`, `Session`, `registered` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Session Lifecycle and Handoff` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `graph`.
+2. **Input normalization**: shape incoming data so `Start` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `hapi`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [HAPI Repository](https://github.com/tiann/hapi)
+  Why it matters: authoritative reference on `HAPI Repository` (github.com).
+- [HAPI Releases](https://github.com/tiann/hapi/releases)
+  Why it matters: authoritative reference on `HAPI Releases` (github.com).
+- [HAPI Docs](https://hapi.run)
+  Why it matters: authoritative reference on `HAPI Docs` (hapi.run).
+
+Suggested trace strategy:
+- search upstream code for `graph` and `Start` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: System Architecture](02-system-architecture.md)
+- [Next Chapter: Chapter 4: Remote Access and Networking](04-remote-access-and-networking.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/hapi-tutorial/04-remote-access-and-networking.md b/tutorials/hapi-tutorial/04-remote-access-and-networking.md
index 2a7998ae..a3dace6b 100644
--- a/tutorials/hapi-tutorial/04-remote-access-and-networking.md
+++ b/tutorials/hapi-tutorial/04-remote-access-and-networking.md
@@ -7,6 +7,9 @@ parent: HAPI Tutorial
 
 # Chapter 4: Remote Access and Networking
 
+Welcome to **Chapter 4: Remote Access and Networking**. In this part of **HAPI Tutorial: Remote Control for Local AI Coding Sessions**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Networking design determines whether HAPI is simple local tooling or production remote infrastructure.
 
 ## Access Modes
@@ -36,3 +39,49 @@ Networking design determines whether HAPI is simple local tooling or production
 You now have a practical network rollout sequence for safe remote HAPI access.
 
 Next: [Chapter 5: Permissions and Approval Workflow](05-permissions-and-approval-workflow.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Remote Access and Networking` as an operating subsystem inside **HAPI Tutorial: Remote Control for Local AI Coding Sessions**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Remote Access and Networking` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [HAPI Repository](https://github.com/tiann/hapi)
+  Why it matters: authoritative reference on `HAPI Repository` (github.com).
+- [HAPI Releases](https://github.com/tiann/hapi/releases)
+  Why it matters: authoritative reference on `HAPI Releases` (github.com).
+- [HAPI Docs](https://hapi.run)
+  Why it matters: authoritative reference on `HAPI Docs` (hapi.run).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Session Lifecycle and Handoff](03-session-lifecycle-and-handoff.md)
+- [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)
diff --git a/tutorials/hapi-tutorial/05-permissions-and-approval-workflow.md b/tutorials/hapi-tutorial/05-permissions-and-approval-workflow.md
index 2a0b0008..0b9beecd 100644
--- a/tutorials/hapi-tutorial/05-permissions-and-approval-workflow.md
+++ b/tutorials/hapi-tutorial/05-permissions-and-approval-workflow.md
@@ -7,6 +7,9 @@ parent: HAPI Tutorial
 
 # Chapter 5: Permissions and Approval Workflow
 
+Welcome to **Chapter 5: Permissions and Approval Workflow**. In this part of **HAPI Tutorial: Remote Control for Local AI Coding Sessions**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Remote approvals are the core safety boundary when agents request actions.
 
 ## Approval Event Flow
@@ -36,3 +39,49 @@ Remote approvals are the core safety boundary when agents request actions.
 You now have a governance model for remote permission handling in HAPI.
 
 Next: [Chapter 6: PWA, Telegram, and Extensions](06-pwa-telegram-and-extensions.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Permissions and Approval Workflow` as an operating subsystem inside **HAPI Tutorial: Remote Control for Local AI Coding Sessions**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Permissions and Approval Workflow` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [HAPI Repository](https://github.com/tiann/hapi)
+  Why it matters: authoritative reference on `HAPI Repository` (github.com).
+- [HAPI Releases](https://github.com/tiann/hapi/releases)
+  Why it matters: authoritative reference on `HAPI Releases` (github.com).
+- [HAPI Docs](https://hapi.run)
+  Why it matters: authoritative reference on `HAPI Docs` (hapi.run).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Remote Access and Networking](04-remote-access-and-networking.md)
+- [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)
diff --git a/tutorials/hapi-tutorial/06-pwa-telegram-and-extensions.md b/tutorials/hapi-tutorial/06-pwa-telegram-and-extensions.md
index ed20ac5a..be0bfb2e 100644
--- a/tutorials/hapi-tutorial/06-pwa-telegram-and-extensions.md
+++ b/tutorials/hapi-tutorial/06-pwa-telegram-and-extensions.md
@@ -7,6 +7,9 @@ parent: HAPI Tutorial
 
 # Chapter 6: PWA, Telegram, and Extensions
 
+Welcome to **Chapter 6: PWA, Telegram, and Extensions**. In this part of **HAPI Tutorial: Remote Control for Local AI Coding Sessions**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 HAPI supports multiple control surfaces so users can choose the right experience for context and urgency.
 
 ## Client Surface Comparison
@@ -32,3 +35,49 @@ Use runner + machine identities to route new sessions to specific hosts based on
 You can now align HAPI interfaces with operator roles and team workflow needs.
 
 Next: [Chapter 7: Configuration and Security](07-configuration-and-security.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: PWA, Telegram, and Extensions` as an operating subsystem inside **HAPI Tutorial: Remote Control for Local AI Coding Sessions**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: PWA, Telegram, and Extensions` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [HAPI Repository](https://github.com/tiann/hapi)
+  Why it matters: authoritative reference on `HAPI Repository` (github.com).
+- [HAPI Releases](https://github.com/tiann/hapi/releases)
+  Why it matters: authoritative reference on `HAPI Releases` (github.com).
+- [HAPI Docs](https://hapi.run)
+  Why it matters: authoritative reference on `HAPI Docs` (hapi.run).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Permissions and Approval Workflow](05-permissions-and-approval-workflow.md)
+- [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)
diff --git a/tutorials/hapi-tutorial/07-configuration-and-security.md b/tutorials/hapi-tutorial/07-configuration-and-security.md
index a662e479..34afadec 100644
--- a/tutorials/hapi-tutorial/07-configuration-and-security.md
+++ b/tutorials/hapi-tutorial/07-configuration-and-security.md
@@ -7,6 +7,9 @@ parent: HAPI Tutorial
 
 # Chapter 7: Configuration and Security
 
+Welcome to **Chapter 7: Configuration and Security**. In this part of **HAPI Tutorial: Remote Control for Local AI Coding Sessions**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 HAPI security depends on disciplined token management, environment separation, and controlled exposure.
 
 ## Key Configuration Domains
@@ -36,3 +39,49 @@ HAPI security depends on disciplined token management, environment separation, a
 You now have a security baseline for moving HAPI from personal setup to team deployment.
 
 Next: [Chapter 8: Production Operations](08-production-operations.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Configuration and Security` as an operating subsystem inside **HAPI Tutorial: Remote Control for Local AI Coding Sessions**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Configuration and Security` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [HAPI Repository](https://github.com/tiann/hapi)
+  Why it matters: authoritative reference on `HAPI Repository` (github.com).
+- [HAPI Releases](https://github.com/tiann/hapi/releases)
+  Why it matters: authoritative reference on `HAPI Releases` (github.com).
+- [HAPI Docs](https://hapi.run)
+  Why it matters: authoritative reference on `HAPI Docs` (hapi.run).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: PWA, Telegram, and Extensions](06-pwa-telegram-and-extensions.md)
+- [Next Chapter: Chapter 8: Production Operations](08-production-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/hapi-tutorial/08-production-operations.md b/tutorials/hapi-tutorial/08-production-operations.md
index ea1d8281..d0c5fc1e 100644
--- a/tutorials/hapi-tutorial/08-production-operations.md
+++ b/tutorials/hapi-tutorial/08-production-operations.md
@@ -7,6 +7,9 @@ parent: HAPI Tutorial
 
 # Chapter 8: Production Operations
 
+Welcome to **Chapter 8: Production Operations**. In this part of **HAPI Tutorial: Remote Control for Local AI Coding Sessions**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter closes with production reliability patterns for HAPI hub operations.
 
 ## Operational Baseline
@@ -40,3 +43,48 @@ Related:
 - [Cline Tutorial](../cline-tutorial/)
 - [Roo Code Tutorial](../roo-code-tutorial/)
 - [OpenHands Tutorial](../openhands-tutorial/)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Operations` as an operating subsystem inside **HAPI Tutorial: Remote Control for Local AI Coding Sessions**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [HAPI Repository](https://github.com/tiann/hapi)
+  Why it matters: authoritative reference on `HAPI Repository` (github.com).
+- [HAPI Releases](https://github.com/tiann/hapi/releases)
+  Why it matters: authoritative reference on `HAPI Releases` (github.com).
+- [HAPI Docs](https://hapi.run)
+  Why it matters: authoritative reference on `HAPI Docs` (hapi.run).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [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)
diff --git a/tutorials/haystack-tutorial/01-getting-started.md b/tutorials/haystack-tutorial/01-getting-started.md
index 9b210e57..a8e5bb69 100644
--- a/tutorials/haystack-tutorial/01-getting-started.md
+++ b/tutorials/haystack-tutorial/01-getting-started.md
@@ -401,3 +401,48 @@ Now that you have a working Haystack setup, let's dive deeper into document stor
 4. Add more documents and test retrieval accuracy
 
 *What's the most interesting search application you could build with Haystack?* 🔍
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `haystack`, `documents`, `Haystack` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Haystack` as an operating subsystem inside **Haystack: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Document`, `pipeline`, `content` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Haystack` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `haystack`.
+2. **Input normalization**: shape incoming data so `documents` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Haystack`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Haystack](https://github.com/deepset-ai/haystack)
+  Why it matters: authoritative reference on `Haystack` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `haystack` and `documents` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Document Stores](02-document-stores.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/haystack-tutorial/02-document-stores.md b/tutorials/haystack-tutorial/02-document-stores.md
index 91154aa1..a243802b 100644
--- a/tutorials/haystack-tutorial/02-document-stores.md
+++ b/tutorials/haystack-tutorial/02-document-stores.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Document Stores
 
+Welcome to **Chapter 2: Document Stores**. In this part of **Haystack: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter explores Haystack's document storage capabilities. You'll learn how to set up and manage document stores, work with different storage backends, and optimize document storage for search performance.
 
 ## 🎯 What You'll Learn
@@ -541,3 +544,49 @@ Ready to explore retrieval techniques? Let's dive into [Chapter 3: Retrievers &
 5. Set up monitoring and health checks
 
 *What's your preferred document store for different use cases?* 📚
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `document_store`, `documents`, `query` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Document Stores` as an operating subsystem inside **Haystack: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `index`, `haystack`, `content` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Document Stores` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `document_store`.
+2. **Input normalization**: shape incoming data so `documents` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `query`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Haystack](https://github.com/deepset-ai/haystack)
+  Why it matters: authoritative reference on `Haystack` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `document_store` and `documents` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Haystack](01-getting-started.md)
+- [Next Chapter: Chapter 3: Retrievers & Search](03-retrievers-search.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/haystack-tutorial/03-retrievers-search.md b/tutorials/haystack-tutorial/03-retrievers-search.md
index e36e1c22..7b0e36ef 100644
--- a/tutorials/haystack-tutorial/03-retrievers-search.md
+++ b/tutorials/haystack-tutorial/03-retrievers-search.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Retrievers & Search
 
+Welcome to **Chapter 3: Retrievers & Search**. In this part of **Haystack: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master document retrieval and search techniques in Haystack.
 
 ## 🎯 Overview
@@ -676,4 +679,50 @@ With retrieval mastered, you're ready to:
 
 ---
 
-**Ready to integrate LLMs with your search system? Continue to [Chapter 4: Generators & LLMs](04-generators-llms.md)!** 🚀
\ No newline at end of file
+**Ready to integrate LLMs with your search system? Continue to [Chapter 4: Generators & LLMs](04-generators-llms.md)!** 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `query`, `retriever` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Retrievers & Search` as an operating subsystem inside **Haystack: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `results`, `print`, `documents` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Retrievers & Search` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `query` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `retriever`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Haystack](https://github.com/deepset-ai/haystack)
+  Why it matters: authoritative reference on `Haystack` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `query` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Document Stores](02-document-stores.md)
+- [Next Chapter: Chapter 4: Generators & LLMs](04-generators-llms.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/haystack-tutorial/04-generators-llms.md b/tutorials/haystack-tutorial/04-generators-llms.md
index 7edc636b..8bb34b0c 100644
--- a/tutorials/haystack-tutorial/04-generators-llms.md
+++ b/tutorials/haystack-tutorial/04-generators-llms.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Generators & LLMs
 
+Welcome to **Chapter 4: Generators & LLMs**. In this part of **Haystack: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Integrate language models for intelligent answer generation in Haystack.
 
 ## 🎯 Overview
@@ -754,4 +757,50 @@ With LLM integration complete, you're ready to:
 
 ---
 
-**Ready to build complex search workflows? Continue to [Chapter 5: Pipelines & Workflows](05-pipelines-workflows.md)!** 🚀
\ No newline at end of file
+**Ready to build complex search workflows? Continue to [Chapter 5: Pipelines & Workflows](05-pipelines-workflows.md)!** 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `query`, `responses` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Generators & LLMs` as an operating subsystem inside **Haystack: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `documents`, `generator`, `response` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Generators & LLMs` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `query` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `responses`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Haystack](https://github.com/deepset-ai/haystack)
+  Why it matters: authoritative reference on `Haystack` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `query` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Retrievers & Search](03-retrievers-search.md)
+- [Next Chapter: Chapter 5: Pipelines & Workflows](05-pipelines-workflows.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/haystack-tutorial/05-pipelines-workflows.md b/tutorials/haystack-tutorial/05-pipelines-workflows.md
index 91b6cd75..2a8100db 100644
--- a/tutorials/haystack-tutorial/05-pipelines-workflows.md
+++ b/tutorials/haystack-tutorial/05-pipelines-workflows.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Pipelines & Workflows
 
+Welcome to **Chapter 5: Pipelines & Workflows**. In this part of **Haystack: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Build complex, production-ready search workflows with Haystack pipelines.
 
 ## 🎯 Overview
@@ -850,4 +853,50 @@ With advanced pipelines mastered, you're ready to:
 
 ---
 
-**Ready to evaluate and optimize your search systems? Continue to [Chapter 6: Evaluation & Optimization](06-evaluation-optimization.md)!** 🚀
\ No newline at end of file
+**Ready to evaluate and optimize your search systems? Continue to [Chapter 6: Evaluation & Optimization](06-evaluation-optimization.md)!** 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `query`, `pipeline` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Pipelines & Workflows` as an operating subsystem inside **Haystack: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `result`, `document_store`, `generator` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Pipelines & Workflows` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `query` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `pipeline`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Haystack](https://github.com/deepset-ai/haystack)
+  Why it matters: authoritative reference on `Haystack` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `query` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Generators & LLMs](04-generators-llms.md)
+- [Next Chapter: Chapter 6: Evaluation & Optimization](06-evaluation-optimization.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/haystack-tutorial/06-evaluation-optimization.md b/tutorials/haystack-tutorial/06-evaluation-optimization.md
index 9e8a9048..3f5df58a 100644
--- a/tutorials/haystack-tutorial/06-evaluation-optimization.md
+++ b/tutorials/haystack-tutorial/06-evaluation-optimization.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Evaluation & Optimization
 
+Welcome to **Chapter 6: Evaluation & Optimization**. In this part of **Haystack: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Measure search quality and optimize Haystack pipelines for maximum performance.
 
 ## 🎯 Overview
@@ -917,4 +920,50 @@ With evaluation and optimization mastered, you're ready to:
 
 ---
 
-**Ready to build custom Haystack components? Continue to [Chapter 7: Custom Components](07-custom-components.md)!** 🚀
\ No newline at end of file
+**Ready to build custom Haystack components? Continue to [Chapter 7: Custom Components](07-custom-components.md)!** 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `results`, `query` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Evaluation & Optimization` as an operating subsystem inside **Haystack: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `mean`, `print`, `predictions` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Evaluation & Optimization` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `results` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `query`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Haystack](https://github.com/deepset-ai/haystack)
+  Why it matters: authoritative reference on `Haystack` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `results` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Pipelines & Workflows](05-pipelines-workflows.md)
+- [Next Chapter: Chapter 7: Custom Components](07-custom-components.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/haystack-tutorial/07-custom-components.md b/tutorials/haystack-tutorial/07-custom-components.md
index 4c78a09f..274f2400 100644
--- a/tutorials/haystack-tutorial/07-custom-components.md
+++ b/tutorials/haystack-tutorial/07-custom-components.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Custom Components
 
+Welcome to **Chapter 7: Custom Components**. In this part of **Haystack: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Build custom components to extend Haystack's functionality for specialized use cases.
 
 ## 🎯 Overview
@@ -1144,4 +1147,50 @@ With custom components mastered, you're ready for:
 
 **Ready for production deployment? Continue to [Chapter 8: Production Deployment](08-production-deployment.md)!** 🚀
 
-*Congratulations! You've now completed the comprehensive Haystack tutorial with 8 full chapters covering everything from basic search to advanced custom components. You have the knowledge and skills to build sophisticated search systems and extend Haystack for specialized use cases.*
\ No newline at end of file
+*Congratulations! You've now completed the comprehensive Haystack tutorial with 8 full chapters covering everything from basic search to advanced custom components. You have the knowledge and skills to build sophisticated search systems and extend Haystack for specialized use cases.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `content`, `text` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Custom Components` as an operating subsystem inside **Haystack: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `result`, `score`, `component` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Custom Components` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `content` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `text`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Haystack](https://github.com/deepset-ai/haystack)
+  Why it matters: authoritative reference on `Haystack` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `content` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Evaluation & Optimization](06-evaluation-optimization.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/haystack-tutorial/08-production-deployment.md b/tutorials/haystack-tutorial/08-production-deployment.md
index f6b2e76f..c38d559d 100644
--- a/tutorials/haystack-tutorial/08-production-deployment.md
+++ b/tutorials/haystack-tutorial/08-production-deployment.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **Haystack: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploy Haystack applications at scale with enterprise-grade reliability and performance.
 
 ## 🎯 Overview
@@ -1116,4 +1119,49 @@ You've successfully mastered production deployment of Haystack applications! 
 
 **You've transformed from Haystack learner to production expert!** 🚀
 
-*Your journey with intelligent search systems has equipped you to build the next generation of AI-powered search applications that can serve millions of users reliably and efficiently.*
\ No newline at end of file
+*Your journey with intelligent search systems has equipped you to build the next generation of AI-powered search applications that can serve millions of users reliably and efficiently.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `search`, `status` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **Haystack: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `result`, `haystack`, `metrics` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `search` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `status`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Haystack](https://github.com/deepset-ai/haystack)
+  Why it matters: authoritative reference on `Haystack` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `search` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Custom Components](07-custom-components.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/huggingface-tutorial/01-getting-started.md b/tutorials/huggingface-tutorial/01-getting-started.md
index 221b693c..7112aa0b 100644
--- a/tutorials/huggingface-tutorial/01-getting-started.md
+++ b/tutorials/huggingface-tutorial/01-getting-started.md
@@ -428,3 +428,48 @@ Now that you understand the basics, let's explore specific NLP tasks in detail.
 4. Experiment with different models and compare their performance
 
 *What kind of AI application would you build first with Transformers?* 🤖
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `pipeline`, `text`, `model` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with HuggingFace Transformers` as an operating subsystem inside **HuggingFace Transformers Tutorial: Building State-of-the-Art AI Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `print`, `result`, `sentiment` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with HuggingFace Transformers` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `pipeline`.
+2. **Input normalization**: shape incoming data so `text` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/huggingface/transformers)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `pipeline` and `text` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Text Classification & Analysis](02-text-classification.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/huggingface-tutorial/02-text-classification.md b/tutorials/huggingface-tutorial/02-text-classification.md
index aa3d2573..a42023da 100644
--- a/tutorials/huggingface-tutorial/02-text-classification.md
+++ b/tutorials/huggingface-tutorial/02-text-classification.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Text Classification & Analysis
 
+Welcome to **Chapter 2: Text Classification & Analysis**. In this part of **HuggingFace Transformers Tutorial: Building State-of-the-Art AI Models**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Welcome back! Now that you understand the basics of HuggingFace Transformers, let's dive into one of the most powerful and widely-used applications: **text classification**. From sentiment analysis to topic categorization, text classification powers many real-world AI applications.
 
 ## Understanding Text Classification
@@ -441,3 +444,49 @@ Ready to generate text with AI? In [Chapter 3: Text Generation](03-text-generati
 **Key Takeaway:** Text classification is the foundation of many AI applications. With HuggingFace Transformers, you can build sophisticated classification systems that rival commercial offerings, all while maintaining full control over your models and data.
 
 *Classification powers everything from spam filters to recommendation systems—master it and unlock countless AI applications!*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `text`, `result` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Text Classification & Analysis` as an operating subsystem inside **HuggingFace Transformers Tutorial: Building State-of-the-Art AI Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `results`, `sentiment`, `confidence` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Text Classification & Analysis` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `text` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `result`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/huggingface/transformers)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `text` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with HuggingFace Transformers](01-getting-started.md)
+- [Next Chapter: Chapter 3: Text Generation](03-text-generation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/huggingface-tutorial/03-text-generation.md b/tutorials/huggingface-tutorial/03-text-generation.md
index c5eabd04..a4829731 100644
--- a/tutorials/huggingface-tutorial/03-text-generation.md
+++ b/tutorials/huggingface-tutorial/03-text-generation.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Text Generation
 
+Welcome to **Chapter 3: Text Generation**. In this part of **HuggingFace Transformers Tutorial: Building State-of-the-Art AI Models**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master the art of AI-powered text generation with Transformers models.
 
 ## 🎯 Overview
@@ -564,4 +567,50 @@ With text generation mastered, you're ready to:
 
 ---
 
-**Ready to build intelligent Q&A systems? Continue to [Chapter 4: Question Answering](04-question-answering.md)!** 🚀
\ No newline at end of file
+**Ready to build intelligent Q&A systems? Continue to [Chapter 4: Question Answering](04-question-answering.md)!** 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `tokenizer`, `inputs` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Text Generation` as an operating subsystem inside **HuggingFace Transformers Tutorial: Building State-of-the-Art AI Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `model`, `max_length`, `outputs` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Text Generation` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `tokenizer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `inputs`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/huggingface/transformers)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `tokenizer` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Text Classification & Analysis](02-text-classification.md)
+- [Next Chapter: Chapter 4: Question Answering](04-question-answering.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/huggingface-tutorial/04-question-answering.md b/tutorials/huggingface-tutorial/04-question-answering.md
index df0f84f3..def95102 100644
--- a/tutorials/huggingface-tutorial/04-question-answering.md
+++ b/tutorials/huggingface-tutorial/04-question-answering.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Question Answering
 
+Welcome to **Chapter 4: Question Answering**. In this part of **HuggingFace Transformers Tutorial: Building State-of-the-Art AI Models**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Build intelligent Q&A systems that can answer questions from documents and knowledge bases.
 
 ## 🎯 Overview
@@ -707,4 +710,50 @@ With QA systems mastered, you're ready to:
 
 ---
 
-**Ready to extract structured information from text? Continue to [Chapter 5: Named Entity Recognition](05-named-entity-recognition.md)!** 🚀
\ No newline at end of file
+**Ready to extract structured information from text? Continue to [Chapter 5: Named Entity Recognition](05-named-entity-recognition.md)!** 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `question`, `answer` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Question Answering` as an operating subsystem inside **HuggingFace Transformers Tutorial: Building State-of-the-Art AI Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `context`, `result`, `print` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Question Answering` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `question` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `answer`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/huggingface/transformers)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `question` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Text Generation](03-text-generation.md)
+- [Next Chapter: Chapter 5: Named Entity Recognition](05-named-entity-recognition.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/huggingface-tutorial/05-named-entity-recognition.md b/tutorials/huggingface-tutorial/05-named-entity-recognition.md
index 78591950..9ced21c5 100644
--- a/tutorials/huggingface-tutorial/05-named-entity-recognition.md
+++ b/tutorials/huggingface-tutorial/05-named-entity-recognition.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Named Entity Recognition
 
+Welcome to **Chapter 5: Named Entity Recognition**. In this part of **HuggingFace Transformers Tutorial: Building State-of-the-Art AI Models**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Extract structured information and named entities from unstructured text using NER models.
 
 ## 🎯 Overview
@@ -805,4 +808,50 @@ With NER mastered, you're ready to:
 
 ---
 
-**Ready to work with multilingual AI models? Continue to [Chapter 6: Translation & Multilingual Models](06-translation-multilingual.md)!** 🚀
\ No newline at end of file
+**Ready to work with multilingual AI models? Continue to [Chapter 6: Translation & Multilingual Models](06-translation-multilingual.md)!** 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `entity`, `entities` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Named Entity Recognition` as an operating subsystem inside **HuggingFace Transformers Tutorial: Building State-of-the-Art AI Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `text`, `token`, `model` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Named Entity Recognition` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `entity` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `entities`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/huggingface/transformers)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `entity` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Question Answering](04-question-answering.md)
+- [Next Chapter: Chapter 6: Translation & Multilingual Models](06-translation-multilingual.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/huggingface-tutorial/06-translation-multilingual.md b/tutorials/huggingface-tutorial/06-translation-multilingual.md
index 9410801b..1288f488 100644
--- a/tutorials/huggingface-tutorial/06-translation-multilingual.md
+++ b/tutorials/huggingface-tutorial/06-translation-multilingual.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Translation & Multilingual Models
 
+Welcome to **Chapter 6: Translation & Multilingual Models**. In this part of **HuggingFace Transformers Tutorial: Building State-of-the-Art AI Models**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master cross-language AI applications with translation and multilingual models.
 
 ## 🎯 Overview
@@ -702,4 +705,50 @@ With multilingual capabilities mastered, you're ready to:
 
 ---
 
-**Ready to fine-tune your own AI models? Continue to [Chapter 7: Fine-tuning Models](07-fine-tuning.md)!** 🚀
\ No newline at end of file
+**Ready to fine-tune your own AI models? Continue to [Chapter 7: Fine-tuning Models](07-fine-tuning.md)!** 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `language`, `text` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Translation & Multilingual Models` as an operating subsystem inside **HuggingFace Transformers Tutorial: Building State-of-the-Art AI Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `texts`, `translation`, `result` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Translation & Multilingual Models` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `language` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `text`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/huggingface/transformers)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `language` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Named Entity Recognition](05-named-entity-recognition.md)
+- [Next Chapter: Chapter 7: Fine-tuning Models](07-fine-tuning.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/huggingface-tutorial/07-fine-tuning.md b/tutorials/huggingface-tutorial/07-fine-tuning.md
index 5cb2c815..1d0dec3c 100644
--- a/tutorials/huggingface-tutorial/07-fine-tuning.md
+++ b/tutorials/huggingface-tutorial/07-fine-tuning.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Fine-tuning Models
 
+Welcome to **Chapter 7: Fine-tuning Models**. In this part of **HuggingFace Transformers Tutorial: Building State-of-the-Art AI Models**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Customize pre-trained models for your specific tasks and domains.
 
 ## 🎯 Overview
@@ -684,4 +687,50 @@ With fine-tuning mastered, you're ready for:
 
 ---
 
-**Ready to deploy your custom AI models? Continue to [Chapter 8: Production Deployment](08-production-deployment.md)!** 🚀
\ No newline at end of file
+**Ready to deploy your custom AI models? Continue to [Chapter 8: Production Deployment](08-production-deployment.md)!** 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `model`, `tokenizer` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Fine-tuning Models` as an operating subsystem inside **HuggingFace Transformers Tutorial: Building State-of-the-Art AI Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `predictions`, `dataset`, `train_dataset` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Fine-tuning Models` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `model` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `tokenizer`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/huggingface/transformers)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `model` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Translation & Multilingual Models](06-translation-multilingual.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/huggingface-tutorial/08-production-deployment.md b/tutorials/huggingface-tutorial/08-production-deployment.md
index fb5c70cf..e233f9af 100644
--- a/tutorials/huggingface-tutorial/08-production-deployment.md
+++ b/tutorials/huggingface-tutorial/08-production-deployment.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **HuggingFace Transformers Tutorial: Building State-of-the-Art AI Models**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploy Transformer models at scale with high performance and reliability.
 
 ## 🎯 Overview
@@ -1184,4 +1187,49 @@ You've successfully mastered the deployment of Transformer models in production!
 
 **You've transformed from AI enthusiast to production AI engineer!** 🚀
 
-*Mastering Transformers deployment opens doors to building the next generation of AI-powered applications that can serve millions of users reliably and efficiently.*
\ No newline at end of file
+*Mastering Transformers deployment opens doors to building the next generation of AI-powered applications that can serve millions of users reliably and efficiently.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `model`, `model_name` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **HuggingFace Transformers Tutorial: Building State-of-the-Art AI Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `result`, `time`, `transformers` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `model` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `model_name`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/huggingface/transformers)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `model` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Fine-tuning Models](07-fine-tuning.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/humanlayer-tutorial/01-getting-started.md b/tutorials/humanlayer-tutorial/01-getting-started.md
index 4c2872ec..0cef2602 100644
--- a/tutorials/humanlayer-tutorial/01-getting-started.md
+++ b/tutorials/humanlayer-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: HumanLayer Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter clarifies the current HumanLayer landscape and how to start with practical workflows.
 
 ## Learning Goals
@@ -31,3 +34,606 @@ This chapter clarifies the current HumanLayer landscape and how to start with pr
 You now have a clear starting point for learning the active and legacy parts of HumanLayer.
 
 Next: [Chapter 2: Architecture and Monorepo Layout](02-architecture-and-monorepo-layout.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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- tutorial slug: **humanlayer-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Humanlayer 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
+
+- [HumanLayer Repository](https://github.com/humanlayer/humanlayer)
+- [HumanLayer Releases](https://github.com/humanlayer/humanlayer/releases)
+- [HumanLayer README](https://github.com/humanlayer/humanlayer/blob/main/README.md)
+- [Legacy HumanLayer SDK Docs](https://github.com/humanlayer/humanlayer/blob/main/humanlayer.md)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Aider Tutorial](../aider-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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 1: Getting Started
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [HumanLayer Repository](https://github.com/humanlayer/humanlayer)
+  Why it matters: authoritative reference on `HumanLayer Repository` (github.com).
+- [HumanLayer Releases](https://github.com/humanlayer/humanlayer/releases)
+  Why it matters: authoritative reference on `HumanLayer Releases` (github.com).
+- [HumanLayer README](https://github.com/humanlayer/humanlayer/blob/main/README.md)
+  Why it matters: authoritative reference on `HumanLayer README` (github.com).
+- [Legacy HumanLayer SDK Docs](https://github.com/humanlayer/humanlayer/blob/main/humanlayer.md)
+  Why it matters: authoritative reference on `Legacy HumanLayer SDK Docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Architecture and Monorepo Layout](02-architecture-and-monorepo-layout.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/humanlayer-tutorial/02-architecture-and-monorepo-layout.md b/tutorials/humanlayer-tutorial/02-architecture-and-monorepo-layout.md
index 400ba45b..39b513aa 100644
--- a/tutorials/humanlayer-tutorial/02-architecture-and-monorepo-layout.md
+++ b/tutorials/humanlayer-tutorial/02-architecture-and-monorepo-layout.md
@@ -7,6 +7,9 @@ parent: HumanLayer Tutorial
 
 # Chapter 2: Architecture and Monorepo Layout
 
+Welcome to **Chapter 2: Architecture and Monorepo Layout**. In this part of **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 HumanLayer uses a monorepo layout that supports multiple workflow surfaces and tooling paths.
 
 ## Key Areas
@@ -23,3 +26,607 @@ HumanLayer uses a monorepo layout that supports multiple workflow surfaces and t
 You now know where to inspect and extend key parts of the HumanLayer codebase.
 
 Next: [Chapter 3: Context Engineering Workflows](03-context-engineering-workflows.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- tutorial slug: **humanlayer-tutorial**
+- chapter focus: **Chapter 2: Architecture and Monorepo Layout**
+- system context: **Humanlayer Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Architecture and Monorepo Layout`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [HumanLayer Repository](https://github.com/humanlayer/humanlayer)
+- [HumanLayer Releases](https://github.com/humanlayer/humanlayer/releases)
+- [HumanLayer README](https://github.com/humanlayer/humanlayer/blob/main/README.md)
+- [Legacy HumanLayer SDK Docs](https://github.com/humanlayer/humanlayer/blob/main/humanlayer.md)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Architecture and Monorepo Layout`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Architecture and Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Architecture and Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Architecture and Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Architecture and Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Architecture and Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Architecture and Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Architecture and Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Architecture and Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 2: Architecture and Monorepo Layout
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Architecture and Monorepo Layout` as an operating subsystem inside **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Architecture and Monorepo Layout` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [HumanLayer Repository](https://github.com/humanlayer/humanlayer)
+  Why it matters: authoritative reference on `HumanLayer Repository` (github.com).
+- [HumanLayer Releases](https://github.com/humanlayer/humanlayer/releases)
+  Why it matters: authoritative reference on `HumanLayer Releases` (github.com).
+- [HumanLayer README](https://github.com/humanlayer/humanlayer/blob/main/README.md)
+  Why it matters: authoritative reference on `HumanLayer README` (github.com).
+- [Legacy HumanLayer SDK Docs](https://github.com/humanlayer/humanlayer/blob/main/humanlayer.md)
+  Why it matters: authoritative reference on `Legacy HumanLayer SDK Docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Context Engineering Workflows](03-context-engineering-workflows.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/humanlayer-tutorial/03-context-engineering-workflows.md b/tutorials/humanlayer-tutorial/03-context-engineering-workflows.md
index 9c685322..79655df9 100644
--- a/tutorials/humanlayer-tutorial/03-context-engineering-workflows.md
+++ b/tutorials/humanlayer-tutorial/03-context-engineering-workflows.md
@@ -7,6 +7,9 @@ parent: HumanLayer Tutorial
 
 # Chapter 3: Context Engineering Workflows
 
+Welcome to **Chapter 3: Context Engineering Workflows**. In this part of **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Context engineering is central to making coding agents reliable in complex repositories.
 
 ## Core Pattern
@@ -32,3 +35,607 @@ Context engineering is central to making coding agents reliable in complex repos
 You now have a repeatable context workflow pattern for hard coding tasks.
 
 Next: [Chapter 4: Parallel Agent Orchestration](04-parallel-agent-orchestration.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- tutorial slug: **humanlayer-tutorial**
+- chapter focus: **Chapter 3: Context Engineering Workflows**
+- system context: **Humanlayer Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Context Engineering Workflows`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [HumanLayer Repository](https://github.com/humanlayer/humanlayer)
+- [HumanLayer Releases](https://github.com/humanlayer/humanlayer/releases)
+- [HumanLayer README](https://github.com/humanlayer/humanlayer/blob/main/README.md)
+- [Legacy HumanLayer SDK Docs](https://github.com/humanlayer/humanlayer/blob/main/humanlayer.md)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Context Engineering Workflows`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 3: Context Engineering Workflows
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Context Engineering Workflows` as an operating subsystem inside **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Context Engineering Workflows` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [HumanLayer Repository](https://github.com/humanlayer/humanlayer)
+  Why it matters: authoritative reference on `HumanLayer Repository` (github.com).
+- [HumanLayer Releases](https://github.com/humanlayer/humanlayer/releases)
+  Why it matters: authoritative reference on `HumanLayer Releases` (github.com).
+- [HumanLayer README](https://github.com/humanlayer/humanlayer/blob/main/README.md)
+  Why it matters: authoritative reference on `HumanLayer README` (github.com).
+- [Legacy HumanLayer SDK Docs](https://github.com/humanlayer/humanlayer/blob/main/humanlayer.md)
+  Why it matters: authoritative reference on `Legacy HumanLayer SDK Docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Architecture and Monorepo Layout](02-architecture-and-monorepo-layout.md)
+- [Next Chapter: Chapter 4: Parallel Agent Orchestration](04-parallel-agent-orchestration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/humanlayer-tutorial/04-parallel-agent-orchestration.md b/tutorials/humanlayer-tutorial/04-parallel-agent-orchestration.md
index 7a114fa8..62536c19 100644
--- a/tutorials/humanlayer-tutorial/04-parallel-agent-orchestration.md
+++ b/tutorials/humanlayer-tutorial/04-parallel-agent-orchestration.md
@@ -7,6 +7,9 @@ parent: HumanLayer Tutorial
 
 # Chapter 4: Parallel Agent Orchestration
 
+Welcome to **Chapter 4: Parallel Agent Orchestration**. In this part of **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Parallel agent sessions can improve throughput, but only when orchestration is explicit and controlled.
 
 ## Orchestration Rules
@@ -23,3 +26,607 @@ Parallel agent sessions can improve throughput, but only when orchestration is e
 You now understand how to scale from single-agent workflows to coordinated parallel execution.
 
 Next: [Chapter 5: Human Approval and High-Stakes Actions](05-human-approval-and-high-stakes-actions.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: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- tutorial slug: **humanlayer-tutorial**
+- chapter focus: **Chapter 4: Parallel Agent Orchestration**
+- system context: **Humanlayer Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Parallel Agent Orchestration`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [HumanLayer Repository](https://github.com/humanlayer/humanlayer)
+- [HumanLayer Releases](https://github.com/humanlayer/humanlayer/releases)
+- [HumanLayer README](https://github.com/humanlayer/humanlayer/blob/main/README.md)
+- [Legacy HumanLayer SDK Docs](https://github.com/humanlayer/humanlayer/blob/main/humanlayer.md)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Parallel Agent Orchestration`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 4: Parallel Agent Orchestration
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Parallel Agent Orchestration` as an operating subsystem inside **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Parallel Agent Orchestration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [HumanLayer Repository](https://github.com/humanlayer/humanlayer)
+  Why it matters: authoritative reference on `HumanLayer Repository` (github.com).
+- [HumanLayer Releases](https://github.com/humanlayer/humanlayer/releases)
+  Why it matters: authoritative reference on `HumanLayer Releases` (github.com).
+- [HumanLayer README](https://github.com/humanlayer/humanlayer/blob/main/README.md)
+  Why it matters: authoritative reference on `HumanLayer README` (github.com).
+- [Legacy HumanLayer SDK Docs](https://github.com/humanlayer/humanlayer/blob/main/humanlayer.md)
+  Why it matters: authoritative reference on `Legacy HumanLayer SDK Docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Context Engineering Workflows](03-context-engineering-workflows.md)
+- [Next Chapter: Chapter 5: Human Approval and High-Stakes Actions](05-human-approval-and-high-stakes-actions.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/humanlayer-tutorial/05-human-approval-and-high-stakes-actions.md b/tutorials/humanlayer-tutorial/05-human-approval-and-high-stakes-actions.md
index 1252f68b..6438ac1c 100644
--- a/tutorials/humanlayer-tutorial/05-human-approval-and-high-stakes-actions.md
+++ b/tutorials/humanlayer-tutorial/05-human-approval-and-high-stakes-actions.md
@@ -7,6 +7,9 @@ parent: HumanLayer Tutorial
 
 # Chapter 5: Human Approval and High-Stakes Actions
 
+Welcome to **Chapter 5: Human Approval and High-Stakes Actions**. In this part of **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 High-stakes operations require deterministic human oversight, not best-effort prompts.
 
 ## Stake Model
@@ -32,3 +35,607 @@ High-stakes operations require deterministic human oversight, not best-effort pr
 You now have a practical approval framework for risky coding-agent operations.
 
 Next: [Chapter 6: IDE and CLI Integration Patterns](06-ide-and-cli-integration-patterns.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- tutorial slug: **humanlayer-tutorial**
+- chapter focus: **Chapter 5: Human Approval and High-Stakes Actions**
+- system context: **Humanlayer Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Human Approval and High-Stakes Actions`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [HumanLayer Repository](https://github.com/humanlayer/humanlayer)
+- [HumanLayer Releases](https://github.com/humanlayer/humanlayer/releases)
+- [HumanLayer README](https://github.com/humanlayer/humanlayer/blob/main/README.md)
+- [Legacy HumanLayer SDK Docs](https://github.com/humanlayer/humanlayer/blob/main/humanlayer.md)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Human Approval and High-Stakes Actions`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 5: Human Approval and High-Stakes Actions
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Human Approval and High-Stakes Actions` as an operating subsystem inside **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Human Approval and High-Stakes Actions` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [HumanLayer Repository](https://github.com/humanlayer/humanlayer)
+  Why it matters: authoritative reference on `HumanLayer Repository` (github.com).
+- [HumanLayer Releases](https://github.com/humanlayer/humanlayer/releases)
+  Why it matters: authoritative reference on `HumanLayer Releases` (github.com).
+- [HumanLayer README](https://github.com/humanlayer/humanlayer/blob/main/README.md)
+  Why it matters: authoritative reference on `HumanLayer README` (github.com).
+- [Legacy HumanLayer SDK Docs](https://github.com/humanlayer/humanlayer/blob/main/humanlayer.md)
+  Why it matters: authoritative reference on `Legacy HumanLayer SDK Docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Parallel Agent Orchestration](04-parallel-agent-orchestration.md)
+- [Next Chapter: Chapter 6: IDE and CLI Integration Patterns](06-ide-and-cli-integration-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/humanlayer-tutorial/06-ide-and-cli-integration-patterns.md b/tutorials/humanlayer-tutorial/06-ide-and-cli-integration-patterns.md
index e0160129..003297b5 100644
--- a/tutorials/humanlayer-tutorial/06-ide-and-cli-integration-patterns.md
+++ b/tutorials/humanlayer-tutorial/06-ide-and-cli-integration-patterns.md
@@ -7,6 +7,9 @@ parent: HumanLayer Tutorial
 
 # Chapter 6: IDE and CLI Integration Patterns
 
+Welcome to **Chapter 6: IDE and CLI Integration Patterns**. In this part of **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 HumanLayer-style workflows are most effective when integrated directly into developer loops.
 
 ## Integration Checklist
@@ -20,3 +23,619 @@ HumanLayer-style workflows are most effective when integrated directly into deve
 You now have baseline patterns to embed HumanLayer workflows into daily IDE and terminal practice.
 
 Next: [Chapter 7: Telemetry, Cost, and Team Governance](07-telemetry-cost-and-team-governance.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- tutorial slug: **humanlayer-tutorial**
+- chapter focus: **Chapter 6: IDE and CLI Integration Patterns**
+- system context: **Humanlayer Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: IDE and CLI Integration 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
+
+- [HumanLayer Repository](https://github.com/humanlayer/humanlayer)
+- [HumanLayer Releases](https://github.com/humanlayer/humanlayer/releases)
+- [HumanLayer README](https://github.com/humanlayer/humanlayer/blob/main/README.md)
+- [Legacy HumanLayer SDK Docs](https://github.com/humanlayer/humanlayer/blob/main/humanlayer.md)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: IDE and CLI Integration 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 6: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 39: Chapter 6: IDE and CLI Integration Patterns
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: IDE and CLI Integration Patterns` as an operating subsystem inside **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: IDE and CLI Integration Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [HumanLayer Repository](https://github.com/humanlayer/humanlayer)
+  Why it matters: authoritative reference on `HumanLayer Repository` (github.com).
+- [HumanLayer Releases](https://github.com/humanlayer/humanlayer/releases)
+  Why it matters: authoritative reference on `HumanLayer Releases` (github.com).
+- [HumanLayer README](https://github.com/humanlayer/humanlayer/blob/main/README.md)
+  Why it matters: authoritative reference on `HumanLayer README` (github.com).
+- [Legacy HumanLayer SDK Docs](https://github.com/humanlayer/humanlayer/blob/main/humanlayer.md)
+  Why it matters: authoritative reference on `Legacy HumanLayer SDK Docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Human Approval and High-Stakes Actions](05-human-approval-and-high-stakes-actions.md)
+- [Next Chapter: Chapter 7: Telemetry, Cost, and Team Governance](07-telemetry-cost-and-team-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/humanlayer-tutorial/07-telemetry-cost-and-team-governance.md b/tutorials/humanlayer-tutorial/07-telemetry-cost-and-team-governance.md
index 49886834..f4fb6e68 100644
--- a/tutorials/humanlayer-tutorial/07-telemetry-cost-and-team-governance.md
+++ b/tutorials/humanlayer-tutorial/07-telemetry-cost-and-team-governance.md
@@ -7,6 +7,9 @@ parent: HumanLayer Tutorial
 
 # Chapter 7: Telemetry, Cost, and Team Governance
 
+Welcome to **Chapter 7: Telemetry, Cost, and Team Governance**. In this part of **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Team-scale coding-agent programs need quality, cost, and governance telemetry to remain effective.
 
 ## Key Metrics
@@ -23,3 +26,607 @@ Team-scale coding-agent programs need quality, cost, and governance telemetry to
 You now have a metric framework for sustainable team operations.
 
 Next: [Chapter 8: Production Rollout and Adoption](08-production-rollout-and-adoption.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- tutorial slug: **humanlayer-tutorial**
+- chapter focus: **Chapter 7: Telemetry, Cost, and Team Governance**
+- system context: **Humanlayer Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Telemetry, Cost, and Team 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
+
+- [HumanLayer Repository](https://github.com/humanlayer/humanlayer)
+- [HumanLayer Releases](https://github.com/humanlayer/humanlayer/releases)
+- [HumanLayer README](https://github.com/humanlayer/humanlayer/blob/main/README.md)
+- [Legacy HumanLayer SDK Docs](https://github.com/humanlayer/humanlayer/blob/main/humanlayer.md)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Telemetry, Cost, and Team Governance`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 7: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 7: Telemetry, Cost, and Team Governance
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Telemetry, Cost, and Team Governance` as an operating subsystem inside **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Telemetry, Cost, and Team Governance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [HumanLayer Repository](https://github.com/humanlayer/humanlayer)
+  Why it matters: authoritative reference on `HumanLayer Repository` (github.com).
+- [HumanLayer Releases](https://github.com/humanlayer/humanlayer/releases)
+  Why it matters: authoritative reference on `HumanLayer Releases` (github.com).
+- [HumanLayer README](https://github.com/humanlayer/humanlayer/blob/main/README.md)
+  Why it matters: authoritative reference on `HumanLayer README` (github.com).
+- [Legacy HumanLayer SDK Docs](https://github.com/humanlayer/humanlayer/blob/main/humanlayer.md)
+  Why it matters: authoritative reference on `Legacy HumanLayer SDK Docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: IDE and CLI Integration Patterns](06-ide-and-cli-integration-patterns.md)
+- [Next Chapter: Chapter 8: Production Rollout and Adoption](08-production-rollout-and-adoption.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/humanlayer-tutorial/08-production-rollout-and-adoption.md b/tutorials/humanlayer-tutorial/08-production-rollout-and-adoption.md
index a8d932a5..74f5e13c 100644
--- a/tutorials/humanlayer-tutorial/08-production-rollout-and-adoption.md
+++ b/tutorials/humanlayer-tutorial/08-production-rollout-and-adoption.md
@@ -7,6 +7,9 @@ parent: HumanLayer Tutorial
 
 # Chapter 8: Production Rollout and Adoption
 
+Welcome to **Chapter 8: Production Rollout and Adoption**. In this part of **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter outlines a rollout model for adopting HumanLayer workflows across teams.
 
 ## Rollout Phases
@@ -25,3 +28,606 @@ This chapter outlines a rollout model for adopting HumanLayer workflows across t
 ## Summary
 
 You now have a phased adoption strategy for scaling coding-agent workflows with human governance.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- tutorial slug: **humanlayer-tutorial**
+- chapter focus: **Chapter 8: Production Rollout and Adoption**
+- system context: **Humanlayer Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Rollout and Adoption`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [HumanLayer Repository](https://github.com/humanlayer/humanlayer)
+- [HumanLayer Releases](https://github.com/humanlayer/humanlayer/releases)
+- [HumanLayer README](https://github.com/humanlayer/humanlayer/blob/main/README.md)
+- [Legacy HumanLayer SDK Docs](https://github.com/humanlayer/humanlayer/blob/main/humanlayer.md)
+
+### Cross-Tutorial Connection Map
+
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Rollout and Adoption`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Production Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Production Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 8: Production Rollout and Adoption
+
+- tutorial context: **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Rollout and Adoption` as an operating subsystem inside **HumanLayer Tutorial: Context Engineering and Human-Governed Coding Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Rollout and Adoption` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [HumanLayer Repository](https://github.com/humanlayer/humanlayer)
+  Why it matters: authoritative reference on `HumanLayer Repository` (github.com).
+- [HumanLayer Releases](https://github.com/humanlayer/humanlayer/releases)
+  Why it matters: authoritative reference on `HumanLayer Releases` (github.com).
+- [HumanLayer README](https://github.com/humanlayer/humanlayer/blob/main/README.md)
+  Why it matters: authoritative reference on `HumanLayer README` (github.com).
+- [Legacy HumanLayer SDK Docs](https://github.com/humanlayer/humanlayer/blob/main/humanlayer.md)
+  Why it matters: authoritative reference on `Legacy HumanLayer SDK Docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Telemetry, Cost, and Team Governance](07-telemetry-cost-and-team-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/instructor-tutorial/01-getting-started.md b/tutorials/instructor-tutorial/01-getting-started.md
index 3dfc558a..436c5170 100644
--- a/tutorials/instructor-tutorial/01-getting-started.md
+++ b/tutorials/instructor-tutorial/01-getting-started.md
@@ -8,6 +8,9 @@ parent: Instructor Tutorial
 
 # Chapter 1: Getting Started with Instructor
 
+Welcome to **Chapter 1: Getting Started with Instructor**. In this part of **Instructor Tutorial: Structured LLM Outputs**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Install Instructor, patch your LLM client, and return your first typed object with Pydantic validation.
 
 ## Overview
@@ -685,3 +688,50 @@ You now know how to install Instructor, patch your client, define models, and ex
 ---
 
 **Next: [Chapter 2: Pydantic Models](02-pydantic-models.md)**
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Field`, `description`, `print` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Instructor` as an operating subsystem inside **Instructor Tutorial: Structured LLM Outputs**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `BaseModel`, `client`, `role` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Instructor` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Field`.
+2. **Input normalization**: shape incoming data so `description` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `print`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/instructor-ai/instructor)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Field` and `description` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Crafting Effective Pydantic Models](02-pydantic-models.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/instructor-tutorial/02-pydantic-models.md b/tutorials/instructor-tutorial/02-pydantic-models.md
index 673278f3..c7a1de1d 100644
--- a/tutorials/instructor-tutorial/02-pydantic-models.md
+++ b/tutorials/instructor-tutorial/02-pydantic-models.md
@@ -8,6 +8,9 @@ parent: Instructor Tutorial
 
 # Chapter 2: Crafting Effective Pydantic Models
 
+Welcome to **Chapter 2: Crafting Effective Pydantic Models**. In this part of **Instructor Tutorial: Structured LLM Outputs**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Design schemas that guide the model toward accurate, typed responses with minimal retries.
 
 ## Overview
@@ -890,3 +893,51 @@ In the next chapter, we will add validation rules and retry logic on top of thes
 ---
 
 Previous: [Chapter 1: Getting Started](01-getting-started.md) | Next: [Chapter 3: Validation & Retries](03-validation.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Field`, `description`, `BaseModel` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Crafting Effective Pydantic Models` as an operating subsystem inside **Instructor Tutorial: Structured LLM Outputs**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `name`, `list`, `None` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Crafting Effective Pydantic Models` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Field`.
+2. **Input normalization**: shape incoming data so `description` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `BaseModel`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/instructor-ai/instructor)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Field` and `description` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Instructor](01-getting-started.md)
+- [Next Chapter: Chapter 3: Validation, Errors, and Retries](03-validation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/instructor-tutorial/03-validation.md b/tutorials/instructor-tutorial/03-validation.md
index cfaf9121..a8b8396a 100644
--- a/tutorials/instructor-tutorial/03-validation.md
+++ b/tutorials/instructor-tutorial/03-validation.md
@@ -8,6 +8,9 @@ parent: Instructor Tutorial
 
 # Chapter 3: Validation, Errors, and Retries
 
+Welcome to **Chapter 3: Validation, Errors, and Retries**. In this part of **Instructor Tutorial: Structured LLM Outputs**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Enforce data quality with Pydantic validation, custom errors, and automatic retries.
 
 ## Overview
@@ -806,3 +809,51 @@ Here is a quick reference table for choosing the right validation strategy:
 ---
 
 Previous: [Chapter 2: Pydantic Models](02-pydantic-models.md) | Next: [Chapter 4: Complex Structures](04-complex.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `Field`, `description` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Validation, Errors, and Retries` as an operating subsystem inside **Instructor Tutorial: Structured LLM Outputs**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `BaseModel`, `raise`, `ValueError` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Validation, Errors, and Retries` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `Field` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `description`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/instructor-ai/instructor)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `Field` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Crafting Effective Pydantic Models](02-pydantic-models.md)
+- [Next Chapter: Chapter 4: Complex Structures and Nested Data](04-complex.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/instructor-tutorial/04-complex.md b/tutorials/instructor-tutorial/04-complex.md
index 3048be61..961444d6 100644
--- a/tutorials/instructor-tutorial/04-complex.md
+++ b/tutorials/instructor-tutorial/04-complex.md
@@ -8,6 +8,9 @@ parent: Instructor Tutorial
 
 # Chapter 4: Complex Structures and Nested Data
 
+Welcome to **Chapter 4: Complex Structures and Nested Data**. In this part of **Instructor Tutorial: Structured LLM Outputs**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Model nested objects, arrays, and rich domain data without losing validation guarantees.
 
 ## Overview
@@ -701,3 +704,51 @@ Different levels of complexity call for different approaches. Here is a quick re
 ---
 
 Previous: [Chapter 3: Validation & Retries](03-validation.md) | Next: [Chapter 5: Streaming](05-streaming.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Field`, `OpenAI`, `description` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Complex Structures and Nested Data` as an operating subsystem inside **Instructor Tutorial: Structured LLM Outputs**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `BaseModel`, `list`, `model` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Complex Structures and Nested Data` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Field`.
+2. **Input normalization**: shape incoming data so `OpenAI` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `description`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/instructor-ai/instructor)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Field` and `OpenAI` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Validation, Errors, and Retries](03-validation.md)
+- [Next Chapter: Chapter 5: Streaming Structured Outputs](05-streaming.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/instructor-tutorial/05-streaming.md b/tutorials/instructor-tutorial/05-streaming.md
index 2180f5d4..dcd8b547 100644
--- a/tutorials/instructor-tutorial/05-streaming.md
+++ b/tutorials/instructor-tutorial/05-streaming.md
@@ -8,6 +8,9 @@ parent: Instructor Tutorial
 
 # Chapter 5: Streaming Structured Outputs
 
+Welcome to **Chapter 5: Streaming Structured Outputs**. In this part of **Instructor Tutorial: Structured LLM Outputs**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Stream partial objects for faster UX while keeping schema guarantees.
 
 ## Overview
@@ -559,3 +562,51 @@ Streaming is one of Instructor's most powerful features for building responsive
 ---
 
 Previous: [Chapter 4: Complex Structures](04-complex.md) | Next: [Chapter 6: Multiple Providers](06-providers.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `partial`, `title`, `Idea` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Streaming Structured Outputs` as an operating subsystem inside **Instructor Tutorial: Structured LLM Outputs**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `blurb`, `ideas`, `stream` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Streaming Structured Outputs` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `partial`.
+2. **Input normalization**: shape incoming data so `title` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Idea`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/instructor-ai/instructor)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `partial` and `title` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Complex Structures and Nested Data](04-complex.md)
+- [Next Chapter: Chapter 6: Using Multiple Providers](06-providers.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/instructor-tutorial/06-providers.md b/tutorials/instructor-tutorial/06-providers.md
index d53cb771..886a2ea5 100644
--- a/tutorials/instructor-tutorial/06-providers.md
+++ b/tutorials/instructor-tutorial/06-providers.md
@@ -8,6 +8,9 @@ parent: Instructor Tutorial
 
 # Chapter 6: Using Multiple Providers
 
+Welcome to **Chapter 6: Using Multiple Providers**. In this part of **Instructor Tutorial: Structured LLM Outputs**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Swap between OpenAI, Anthropic, Google, and local models (Ollama) without changing schemas.
 
 ## Overview
@@ -788,3 +791,51 @@ This way, switching providers is a config change -- no code changes, no redeploy
 ---
 
 Previous: [Chapter 5: Streaming](05-streaming.md) | Next: [Chapter 7: Advanced Patterns](07-advanced.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `model`, `client`, `messages` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Using Multiple Providers` as an operating subsystem inside **Instructor Tutorial: Structured LLM Outputs**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `OpenAI`, `instructor`, `provider` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Using Multiple Providers` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `model`.
+2. **Input normalization**: shape incoming data so `client` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `messages`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/instructor-ai/instructor)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `model` and `client` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Streaming Structured Outputs](05-streaming.md)
+- [Next Chapter: Chapter 7: Advanced Patterns and Guardrails](07-advanced.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/instructor-tutorial/07-advanced.md b/tutorials/instructor-tutorial/07-advanced.md
index 73f6baa8..1dd1ecaa 100644
--- a/tutorials/instructor-tutorial/07-advanced.md
+++ b/tutorials/instructor-tutorial/07-advanced.md
@@ -8,6 +8,9 @@ parent: Instructor Tutorial
 
 # Chapter 7: Advanced Patterns and Guardrails
 
+Welcome to **Chapter 7: Advanced Patterns and Guardrails**. In this part of **Instructor Tutorial: Structured LLM Outputs**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Add validators, chained extractions, classification pipelines, caching, and guardrails to squeeze the most reliability out of Instructor.
 
 ## Overview
@@ -882,3 +885,51 @@ Each piece is independently testable, cacheable, and swappable. That is the real
 ---
 
 Previous: [Chapter 6: Multiple Providers](06-providers.md) | Next: [Chapter 8: Production Use](08-production.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `text`, `self`, `list` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Advanced Patterns and Guardrails` as an operating subsystem inside **Instructor Tutorial: Structured LLM Outputs**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Field`, `result`, `content` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Advanced Patterns and Guardrails` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `text`.
+2. **Input normalization**: shape incoming data so `self` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `list`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/instructor-ai/instructor)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `text` and `self` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Using Multiple Providers](06-providers.md)
+- [Next Chapter: Chapter 8: Production Use and Operations](08-production.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/instructor-tutorial/08-production.md b/tutorials/instructor-tutorial/08-production.md
index bf481f91..35944cfd 100644
--- a/tutorials/instructor-tutorial/08-production.md
+++ b/tutorials/instructor-tutorial/08-production.md
@@ -8,6 +8,9 @@ parent: Instructor Tutorial
 
 # Chapter 8: Production Use and Operations
 
+Welcome to **Chapter 8: Production Use and Operations**. In this part of **Instructor Tutorial: Structured LLM Outputs**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Harden Instructor apps with observability, safety, cost controls, and deployment patterns so your structured-output workflows run reliably at scale.
 
 ## Overview
@@ -987,3 +990,50 @@ Now go build something great. Your structured outputs are ready for the real wor
 ---
 
 Previous: [Chapter 7: Advanced Patterns](07-advanced.md)
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `model`, `result`, `instructor` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Use and Operations` as an operating subsystem inside **Instructor Tutorial: Structured LLM Outputs**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `self`, `prompt`, `response_model` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Use and Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `model`.
+2. **Input normalization**: shape incoming data so `result` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `instructor`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/instructor-ai/instructor)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `model` and `result` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Advanced Patterns and Guardrails](07-advanced.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/khoj-tutorial/01-getting-started.md b/tutorials/khoj-tutorial/01-getting-started.md
index 15a60069..800a0ac3 100644
--- a/tutorials/khoj-tutorial/01-getting-started.md
+++ b/tutorials/khoj-tutorial/01-getting-started.md
@@ -8,6 +8,9 @@ parent: "Khoj AI - Personal Assistant Deep Dive"
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Khoj AI: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Khoj is an open-source AI personal assistant designed to give you natural language access to your personal knowledge base. Unlike cloud-only AI assistants, Khoj can be self-hosted, giving you full control over your data and privacy. This chapter walks you through installation, initial configuration, and connecting your first data sources.
 
 ## What is Khoj?
@@ -702,3 +705,48 @@ In [Chapter 2: Architecture Overview](02-architecture-overview.md), we will expl
 
 ---
 *Built with insights from the [Khoj](https://github.com/khoj-ai/khoj) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `print`, `khoj`, `Khoj` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Khoj AI: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `response`, `base_url`, `your` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `print`.
+2. **Input normalization**: shape incoming data so `khoj` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Khoj`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Khoj](https://github.com/khoj-ai/khoj)
+  Why it matters: authoritative reference on `Khoj` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `print` and `khoj` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Architecture Overview](02-architecture-overview.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/khoj-tutorial/02-architecture-overview.md b/tutorials/khoj-tutorial/02-architecture-overview.md
index 3a13b80f..e0da9372 100644
--- a/tutorials/khoj-tutorial/02-architecture-overview.md
+++ b/tutorials/khoj-tutorial/02-architecture-overview.md
@@ -8,6 +8,9 @@ parent: "Khoj AI - Personal Assistant Deep Dive"
 
 # Chapter 2: Architecture Overview
 
+Welcome to **Chapter 2: Architecture Overview**. In this part of **Khoj AI: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Understanding Khoj's architecture is essential for effectively configuring, extending, and troubleshooting the system. Khoj is built as a modular, Django-based web application that orchestrates data ingestion, semantic search, and LLM-powered chat into a unified personal assistant experience. This chapter explores each architectural layer in depth.
 
 ## High-Level System Architecture
@@ -950,3 +953,49 @@ In [Chapter 3: Data Connectors](03-data-connectors.md), we will dive deep into e
 
 ---
 *Built with insights from the [Khoj](https://github.com/khoj-ai/khoj) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `query`, `list` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Architecture Overview` as an operating subsystem inside **Khoj AI: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `search`, `content`, `Engine` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Architecture Overview` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `query` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `list`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Khoj](https://github.com/khoj-ai/khoj)
+  Why it matters: authoritative reference on `Khoj` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `query` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Data Connectors](03-data-connectors.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/khoj-tutorial/03-data-connectors.md b/tutorials/khoj-tutorial/03-data-connectors.md
index 66e59955..5fc52ddc 100644
--- a/tutorials/khoj-tutorial/03-data-connectors.md
+++ b/tutorials/khoj-tutorial/03-data-connectors.md
@@ -8,6 +8,9 @@ parent: "Khoj AI - Personal Assistant Deep Dive"
 
 # Chapter 3: Data Connectors
 
+Welcome to **Chapter 3: Data Connectors**. In this part of **Khoj AI: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Khoj's power comes from its ability to connect to and index your personal data across a variety of formats and sources. Data connectors are the bridge between your raw content and Khoj's searchable knowledge base. This chapter explores each connector in depth, covering how data is ingested, parsed, and prepared for indexing.
 
 ## Connector Architecture Overview
@@ -1073,3 +1076,49 @@ In [Chapter 4: Search & Retrieval](04-search-and-retrieval.md), we will explore
 
 ---
 *Built with insights from the [Khoj](https://github.com/khoj-ai/khoj) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `heading`, `self`, `content` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Data Connectors` as an operating subsystem inside **Khoj AI: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `line`, `entries`, `body` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Data Connectors` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `heading`.
+2. **Input normalization**: shape incoming data so `self` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `content`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Khoj](https://github.com/khoj-ai/khoj)
+  Why it matters: authoritative reference on `Khoj` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `heading` and `self` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Architecture Overview](02-architecture-overview.md)
+- [Next Chapter: Chapter 4: Search & Retrieval](04-search-and-retrieval.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/khoj-tutorial/04-search-and-retrieval.md b/tutorials/khoj-tutorial/04-search-and-retrieval.md
index 71b1e853..dd6c4a42 100644
--- a/tutorials/khoj-tutorial/04-search-and-retrieval.md
+++ b/tutorials/khoj-tutorial/04-search-and-retrieval.md
@@ -8,6 +8,9 @@ parent: "Khoj AI - Personal Assistant Deep Dive"
 
 # Chapter 4: Search & Retrieval
 
+Welcome to **Chapter 4: Search & Retrieval**. In this part of **Khoj AI: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Search is the foundational capability that makes Khoj useful as a personal assistant. Rather than relying on simple keyword matching, Khoj implements semantic search using embedding models combined with a two-stage retrieval pipeline. This chapter covers the complete search architecture, from embedding generation through result ranking.
 
 ## Search Architecture Overview
@@ -964,3 +967,49 @@ In [Chapter 5: Chat Interface](05-chat-interface.md), we will explore how Khoj's
 
 ---
 *Built with insights from the [Khoj](https://github.com/khoj-ai/khoj) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `query`, `search` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Search & Retrieval` as an operating subsystem inside **Khoj AI: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `results`, `print`, `entries` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Search & Retrieval` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `query` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `search`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Khoj](https://github.com/khoj-ai/khoj)
+  Why it matters: authoritative reference on `Khoj` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `query` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Data Connectors](03-data-connectors.md)
+- [Next Chapter: Chapter 5: Chat Interface](05-chat-interface.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/khoj-tutorial/05-chat-interface.md b/tutorials/khoj-tutorial/05-chat-interface.md
index 2b8cb8ad..8f580e00 100644
--- a/tutorials/khoj-tutorial/05-chat-interface.md
+++ b/tutorials/khoj-tutorial/05-chat-interface.md
@@ -8,6 +8,9 @@ parent: "Khoj AI - Personal Assistant Deep Dive"
 
 # Chapter 5: Chat Interface
 
+Welcome to **Chapter 5: Chat Interface**. In this part of **Khoj AI: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Khoj's chat interface transforms a static knowledge base into a dynamic conversational experience. By grounding LLM responses in your personal data, Khoj provides answers that are relevant, accurate, and attributable to specific sources. This chapter explores how the chat system works, from intent detection through response generation and conversation management.
 
 ## Chat System Overview
@@ -1070,3 +1073,49 @@ In [Chapter 6: Automation & Agents](06-automation-and-agents.md), we will explor
 
 ---
 *Built with insights from the [Khoj](https://github.com/khoj-ai/khoj) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `response`, `dict` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Chat Interface` as an operating subsystem inside **Khoj AI: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `context`, `content`, `sources` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Chat Interface` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `response` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `dict`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Khoj](https://github.com/khoj-ai/khoj)
+  Why it matters: authoritative reference on `Khoj` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `response` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Search & Retrieval](04-search-and-retrieval.md)
+- [Next Chapter: Chapter 6: Automation & Agents](06-automation-and-agents.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/khoj-tutorial/06-automation-and-agents.md b/tutorials/khoj-tutorial/06-automation-and-agents.md
index c1744b38..70547511 100644
--- a/tutorials/khoj-tutorial/06-automation-and-agents.md
+++ b/tutorials/khoj-tutorial/06-automation-and-agents.md
@@ -8,6 +8,9 @@ parent: "Khoj AI - Personal Assistant Deep Dive"
 
 # Chapter 6: Automation & Agents
 
+Welcome to **Chapter 6: Automation & Agents**. In this part of **Khoj AI: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Khoj goes beyond reactive search and chat by providing automation and agent capabilities. Scheduled tasks, autonomous agents, and tool integration allow Khoj to proactively assist you -- surfacing relevant information, executing recurring queries, and taking actions on your behalf. This chapter explores the automation framework in depth.
 
 ## Automation Architecture
@@ -937,3 +940,49 @@ In [Chapter 7: Customization & Plugins](07-customization-and-plugins.md), we wil
 
 ---
 *Built with insights from the [Khoj](https://github.com/khoj-ai/khoj) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `automation`, `name` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Automation & Agents` as an operating subsystem inside **Khoj AI: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `step`, `tool`, `query` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Automation & Agents` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `automation` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `name`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Khoj](https://github.com/khoj-ai/khoj)
+  Why it matters: authoritative reference on `Khoj` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `automation` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Chat Interface](05-chat-interface.md)
+- [Next Chapter: Chapter 7: Customization & Plugins](07-customization-and-plugins.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/khoj-tutorial/07-customization-and-plugins.md b/tutorials/khoj-tutorial/07-customization-and-plugins.md
index fa4d6ea5..12068afa 100644
--- a/tutorials/khoj-tutorial/07-customization-and-plugins.md
+++ b/tutorials/khoj-tutorial/07-customization-and-plugins.md
@@ -8,6 +8,9 @@ parent: "Khoj AI - Personal Assistant Deep Dive"
 
 # Chapter 7: Customization & Plugins
 
+Welcome to **Chapter 7: Customization & Plugins**. In this part of **Khoj AI: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Khoj is designed to be customizable at every layer, from the embedding models and LLM backends to the data processors and response behavior. This chapter explores how to tailor Khoj to your specific needs through model configuration, custom data type processors, prompt personalization, and the extension architecture.
 
 ## Customization Overview
@@ -1043,3 +1046,49 @@ In [Chapter 8: Production Deployment](08-production-deployment.md), we will cove
 
 ---
 *Built with insights from the [Khoj](https://github.com/khoj-ai/khoj) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `model_name`, `model`, `config` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Customization & Plugins` as an operating subsystem inside **Khoj AI: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `content`, `self`, `Custom` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Customization & Plugins` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `model_name`.
+2. **Input normalization**: shape incoming data so `model` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `config`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Khoj](https://github.com/khoj-ai/khoj)
+  Why it matters: authoritative reference on `Khoj` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `model_name` and `model` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Automation & Agents](06-automation-and-agents.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/khoj-tutorial/08-production-deployment.md b/tutorials/khoj-tutorial/08-production-deployment.md
index 7085fe44..a64b3544 100644
--- a/tutorials/khoj-tutorial/08-production-deployment.md
+++ b/tutorials/khoj-tutorial/08-production-deployment.md
@@ -8,6 +8,9 @@ parent: "Khoj AI - Personal Assistant Deep Dive"
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **Khoj AI: Deep Dive Tutorial**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Deploying Khoj in a production environment requires careful consideration of infrastructure, security, performance, and reliability. This chapter provides a comprehensive guide to running Khoj at scale, covering Docker deployment, reverse proxy configuration, security hardening, monitoring, backup strategies, and multi-user scaling.
 
 ## Production Architecture
@@ -1212,3 +1215,48 @@ In this chapter, you covered the full spectrum of production deployment for Khoj
 
 ---
 *Built with insights from the [Khoj](https://github.com/khoj-ai/khoj) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `khoj`, `print` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **Khoj AI: Deep Dive Tutorial**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `recommendation`, `metrics`, `config` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `khoj` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `print`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Khoj](https://github.com/khoj-ai/khoj)
+  Why it matters: authoritative reference on `Khoj` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `khoj` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Customization & Plugins](07-customization-and-plugins.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kilocode-tutorial/01-getting-started.md b/tutorials/kilocode-tutorial/01-getting-started.md
index cc50ab7b..ac1f9b6f 100644
--- a/tutorials/kilocode-tutorial/01-getting-started.md
+++ b/tutorials/kilocode-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Kilo Code Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets Kilo installed and running with a first coding-agent workflow.
 
 ## Start Points
@@ -29,3 +32,609 @@ This chapter gets Kilo installed and running with a first coding-agent workflow.
 You now have Kilo ready for first-task execution.
 
 Next: [Chapter 2: Agent Loop and State Model](02-agent-loop-and-state-model.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- tutorial slug: **kilocode-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Kilocode 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
+
+- [Kilo Repository](https://github.com/Kilo-Org/kilocode)
+- [Kilo README](https://github.com/Kilo-Org/kilocode/blob/main/README.md)
+- [CLI agent loop docs](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/docs/AGENT_LOOP.md)
+- [CLI auth login flow](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/commands/auth/login.ts)
+- [Extension host](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/agent/extension-host.ts)
+
+### Cross-Tutorial Connection Map
+
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [Mistral Vibe Tutorial](../mistral-vibe-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Superset Terminal Tutorial](../superset-terminal-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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 1: Getting Started
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Kilo Repository](https://github.com/Kilo-Org/kilocode)
+  Why it matters: authoritative reference on `Kilo Repository` (github.com).
+- [Kilo README](https://github.com/Kilo-Org/kilocode/blob/main/README.md)
+  Why it matters: authoritative reference on `Kilo README` (github.com).
+- [CLI agent loop docs](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/docs/AGENT_LOOP.md)
+  Why it matters: authoritative reference on `CLI agent loop docs` (github.com).
+- [CLI auth login flow](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/commands/auth/login.ts)
+  Why it matters: authoritative reference on `CLI auth login flow` (github.com).
+- [Extension host](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/agent/extension-host.ts)
+  Why it matters: authoritative reference on `Extension host` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Agent Loop and State Model](02-agent-loop-and-state-model.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kilocode-tutorial/02-agent-loop-and-state-model.md b/tutorials/kilocode-tutorial/02-agent-loop-and-state-model.md
index 34fc89d0..f01e8c48 100644
--- a/tutorials/kilocode-tutorial/02-agent-loop-and-state-model.md
+++ b/tutorials/kilocode-tutorial/02-agent-loop-and-state-model.md
@@ -7,6 +7,9 @@ parent: Kilo Code Tutorial
 
 # Chapter 2: Agent Loop and State Model
 
+Welcome to **Chapter 2: Agent Loop and State Model**. In this part of **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Kilo's CLI architecture uses message-state analysis to track whether the agent is running, streaming, waiting for input, or idle.
 
 ## State Categories
@@ -31,3 +34,610 @@ This state model allows reliable coordination of approvals, followups, and autom
 You now understand the core loop-state mechanics that drive Kilo interaction behavior.
 
 Next: [Chapter 3: Modes, Prompts, and Approval Workflow](03-modes-prompts-and-approval-workflow.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- tutorial slug: **kilocode-tutorial**
+- chapter focus: **Chapter 2: Agent Loop and State Model**
+- system context: **Kilocode Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Agent Loop and State 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
+
+- [Kilo Repository](https://github.com/Kilo-Org/kilocode)
+- [Kilo README](https://github.com/Kilo-Org/kilocode/blob/main/README.md)
+- [CLI agent loop docs](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/docs/AGENT_LOOP.md)
+- [CLI auth login flow](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/commands/auth/login.ts)
+- [Extension host](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/agent/extension-host.ts)
+
+### Cross-Tutorial Connection Map
+
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [Mistral Vibe Tutorial](../mistral-vibe-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Superset Terminal Tutorial](../superset-terminal-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Agent Loop and State Model`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 2: Agent Loop and State Model
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Agent Loop and State Model` as an operating subsystem inside **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Agent Loop and State Model` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Kilo Repository](https://github.com/Kilo-Org/kilocode)
+  Why it matters: authoritative reference on `Kilo Repository` (github.com).
+- [Kilo README](https://github.com/Kilo-Org/kilocode/blob/main/README.md)
+  Why it matters: authoritative reference on `Kilo README` (github.com).
+- [CLI agent loop docs](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/docs/AGENT_LOOP.md)
+  Why it matters: authoritative reference on `CLI agent loop docs` (github.com).
+- [CLI auth login flow](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/commands/auth/login.ts)
+  Why it matters: authoritative reference on `CLI auth login flow` (github.com).
+- [Extension host](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/agent/extension-host.ts)
+  Why it matters: authoritative reference on `Extension host` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Modes, Prompts, and Approval Workflow](03-modes-prompts-and-approval-workflow.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kilocode-tutorial/03-modes-prompts-and-approval-workflow.md b/tutorials/kilocode-tutorial/03-modes-prompts-and-approval-workflow.md
index e5b95254..4ade1720 100644
--- a/tutorials/kilocode-tutorial/03-modes-prompts-and-approval-workflow.md
+++ b/tutorials/kilocode-tutorial/03-modes-prompts-and-approval-workflow.md
@@ -7,6 +7,9 @@ parent: Kilo Code Tutorial
 
 # Chapter 3: Modes, Prompts, and Approval Workflow
 
+Welcome to **Chapter 3: Modes, Prompts, and Approval Workflow**. In this part of **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Kilo supports different run modes and approval paths that balance autonomy with safety.
 
 ## Practical Controls
@@ -25,3 +28,610 @@ Kilo supports different run modes and approval paths that balance autonomy with
 You now have a mode-selection and approval strategy for safer Kilo sessions.
 
 Next: [Chapter 4: Authentication and Provider Routing](04-authentication-and-provider-routing.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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- tutorial slug: **kilocode-tutorial**
+- chapter focus: **Chapter 3: Modes, Prompts, and Approval Workflow**
+- system context: **Kilocode Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Modes, Prompts, 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
+
+- [Kilo Repository](https://github.com/Kilo-Org/kilocode)
+- [Kilo README](https://github.com/Kilo-Org/kilocode/blob/main/README.md)
+- [CLI agent loop docs](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/docs/AGENT_LOOP.md)
+- [CLI auth login flow](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/commands/auth/login.ts)
+- [Extension host](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/agent/extension-host.ts)
+
+### Cross-Tutorial Connection Map
+
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [Mistral Vibe Tutorial](../mistral-vibe-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Superset Terminal Tutorial](../superset-terminal-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Modes, Prompts, 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 3: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 3: Modes, Prompts, and Approval Workflow
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Modes, Prompts, and Approval Workflow` as an operating subsystem inside **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Modes, Prompts, and Approval Workflow` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Kilo Repository](https://github.com/Kilo-Org/kilocode)
+  Why it matters: authoritative reference on `Kilo Repository` (github.com).
+- [Kilo README](https://github.com/Kilo-Org/kilocode/blob/main/README.md)
+  Why it matters: authoritative reference on `Kilo README` (github.com).
+- [CLI agent loop docs](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/docs/AGENT_LOOP.md)
+  Why it matters: authoritative reference on `CLI agent loop docs` (github.com).
+- [CLI auth login flow](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/commands/auth/login.ts)
+  Why it matters: authoritative reference on `CLI auth login flow` (github.com).
+- [Extension host](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/agent/extension-host.ts)
+  Why it matters: authoritative reference on `Extension host` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Agent Loop and State Model](02-agent-loop-and-state-model.md)
+- [Next Chapter: Chapter 4: Authentication and Provider Routing](04-authentication-and-provider-routing.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kilocode-tutorial/04-authentication-and-provider-routing.md b/tutorials/kilocode-tutorial/04-authentication-and-provider-routing.md
index d86c0c6e..39616856 100644
--- a/tutorials/kilocode-tutorial/04-authentication-and-provider-routing.md
+++ b/tutorials/kilocode-tutorial/04-authentication-and-provider-routing.md
@@ -7,6 +7,9 @@ parent: Kilo Code Tutorial
 
 # Chapter 4: Authentication and Provider Routing
 
+Welcome to **Chapter 4: Authentication and Provider Routing**. In this part of **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Authentication and provider selection govern model access, usage limits, and runtime behavior.
 
 ## Key Flows
@@ -25,3 +28,610 @@ Authentication and provider selection govern model access, usage limits, and run
 You now understand how Kilo handles auth and provider initialization end-to-end.
 
 Next: [Chapter 5: Session, History, and Context Persistence](05-session-history-and-context-persistence.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- tutorial slug: **kilocode-tutorial**
+- chapter focus: **Chapter 4: Authentication and Provider Routing**
+- system context: **Kilocode Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Authentication and Provider Routing`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Kilo Repository](https://github.com/Kilo-Org/kilocode)
+- [Kilo README](https://github.com/Kilo-Org/kilocode/blob/main/README.md)
+- [CLI agent loop docs](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/docs/AGENT_LOOP.md)
+- [CLI auth login flow](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/commands/auth/login.ts)
+- [Extension host](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/agent/extension-host.ts)
+
+### Cross-Tutorial Connection Map
+
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [Mistral Vibe Tutorial](../mistral-vibe-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Superset Terminal Tutorial](../superset-terminal-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Authentication and Provider Routing`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 4: Authentication and Provider Routing
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Authentication and Provider Routing` as an operating subsystem inside **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Authentication and Provider Routing` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Kilo Repository](https://github.com/Kilo-Org/kilocode)
+  Why it matters: authoritative reference on `Kilo Repository` (github.com).
+- [Kilo README](https://github.com/Kilo-Org/kilocode/blob/main/README.md)
+  Why it matters: authoritative reference on `Kilo README` (github.com).
+- [CLI agent loop docs](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/docs/AGENT_LOOP.md)
+  Why it matters: authoritative reference on `CLI agent loop docs` (github.com).
+- [CLI auth login flow](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/commands/auth/login.ts)
+  Why it matters: authoritative reference on `CLI auth login flow` (github.com).
+- [Extension host](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/agent/extension-host.ts)
+  Why it matters: authoritative reference on `Extension host` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Modes, Prompts, and Approval Workflow](03-modes-prompts-and-approval-workflow.md)
+- [Next Chapter: Chapter 5: Session, History, and Context Persistence](05-session-history-and-context-persistence.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kilocode-tutorial/05-session-history-and-context-persistence.md b/tutorials/kilocode-tutorial/05-session-history-and-context-persistence.md
index 88f44283..fd89f97b 100644
--- a/tutorials/kilocode-tutorial/05-session-history-and-context-persistence.md
+++ b/tutorials/kilocode-tutorial/05-session-history-and-context-persistence.md
@@ -7,6 +7,9 @@ parent: Kilo Code Tutorial
 
 # Chapter 5: Session, History, and Context Persistence
 
+Welcome to **Chapter 5: Session, History, and Context Persistence**. In this part of **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Kilo persists CLI state such as history and settings to maintain continuity across runs.
 
 ## Persistence Artifacts
@@ -27,3 +30,610 @@ Kilo persists CLI state such as history and settings to maintain continuity acro
 You now have a clear model for how Kilo preserves user context over time.
 
 Next: [Chapter 6: Extensions, MCP, and Custom Modes](06-extensions-mcp-and-custom-modes.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- tutorial slug: **kilocode-tutorial**
+- chapter focus: **Chapter 5: Session, History, and Context Persistence**
+- system context: **Kilocode Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Session, History, and Context Persistence`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Kilo Repository](https://github.com/Kilo-Org/kilocode)
+- [Kilo README](https://github.com/Kilo-Org/kilocode/blob/main/README.md)
+- [CLI agent loop docs](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/docs/AGENT_LOOP.md)
+- [CLI auth login flow](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/commands/auth/login.ts)
+- [Extension host](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/agent/extension-host.ts)
+
+### Cross-Tutorial Connection Map
+
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [Mistral Vibe Tutorial](../mistral-vibe-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Superset Terminal Tutorial](../superset-terminal-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Session, History, and Context Persistence`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 5: Session, History, and Context Persistence
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Session, History, and Context Persistence` as an operating subsystem inside **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Session, History, and Context Persistence` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Kilo Repository](https://github.com/Kilo-Org/kilocode)
+  Why it matters: authoritative reference on `Kilo Repository` (github.com).
+- [Kilo README](https://github.com/Kilo-Org/kilocode/blob/main/README.md)
+  Why it matters: authoritative reference on `Kilo README` (github.com).
+- [CLI agent loop docs](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/docs/AGENT_LOOP.md)
+  Why it matters: authoritative reference on `CLI agent loop docs` (github.com).
+- [CLI auth login flow](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/commands/auth/login.ts)
+  Why it matters: authoritative reference on `CLI auth login flow` (github.com).
+- [Extension host](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/agent/extension-host.ts)
+  Why it matters: authoritative reference on `Extension host` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Authentication and Provider Routing](04-authentication-and-provider-routing.md)
+- [Next Chapter: Chapter 6: Extensions, MCP, and Custom Modes](06-extensions-mcp-and-custom-modes.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kilocode-tutorial/06-extensions-mcp-and-custom-modes.md b/tutorials/kilocode-tutorial/06-extensions-mcp-and-custom-modes.md
index b2ae70d1..38eaa905 100644
--- a/tutorials/kilocode-tutorial/06-extensions-mcp-and-custom-modes.md
+++ b/tutorials/kilocode-tutorial/06-extensions-mcp-and-custom-modes.md
@@ -7,6 +7,9 @@ parent: Kilo Code Tutorial
 
 # Chapter 6: Extensions, MCP, and Custom Modes
 
+Welcome to **Chapter 6: Extensions, MCP, and Custom Modes**. In this part of **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Kilo exposes extension points for custom modes, MCP integrations, and workflow specialization.
 
 ## Extension Areas
@@ -25,3 +28,610 @@ Kilo exposes extension points for custom modes, MCP integrations, and workflow s
 You now understand where Kilo can be extended for project-specific or team-specific workflows.
 
 Next: [Chapter 7: CLI/TUI Architecture for Contributors](07-cli-tui-architecture-for-contributors.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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- tutorial slug: **kilocode-tutorial**
+- chapter focus: **Chapter 6: Extensions, MCP, and Custom Modes**
+- system context: **Kilocode Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Extensions, MCP, and Custom Modes`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Kilo Repository](https://github.com/Kilo-Org/kilocode)
+- [Kilo README](https://github.com/Kilo-Org/kilocode/blob/main/README.md)
+- [CLI agent loop docs](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/docs/AGENT_LOOP.md)
+- [CLI auth login flow](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/commands/auth/login.ts)
+- [Extension host](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/agent/extension-host.ts)
+
+### Cross-Tutorial Connection Map
+
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [Mistral Vibe Tutorial](../mistral-vibe-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Superset Terminal Tutorial](../superset-terminal-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Extensions, MCP, and Custom Modes`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 6: Extensions, MCP, and Custom Modes
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Extensions, MCP, and Custom Modes` as an operating subsystem inside **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Extensions, MCP, and Custom Modes` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Kilo Repository](https://github.com/Kilo-Org/kilocode)
+  Why it matters: authoritative reference on `Kilo Repository` (github.com).
+- [Kilo README](https://github.com/Kilo-Org/kilocode/blob/main/README.md)
+  Why it matters: authoritative reference on `Kilo README` (github.com).
+- [CLI agent loop docs](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/docs/AGENT_LOOP.md)
+  Why it matters: authoritative reference on `CLI agent loop docs` (github.com).
+- [CLI auth login flow](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/commands/auth/login.ts)
+  Why it matters: authoritative reference on `CLI auth login flow` (github.com).
+- [Extension host](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/agent/extension-host.ts)
+  Why it matters: authoritative reference on `Extension host` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Session, History, and Context Persistence](05-session-history-and-context-persistence.md)
+- [Next Chapter: Chapter 7: CLI/TUI Architecture for Contributors](07-cli-tui-architecture-for-contributors.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kilocode-tutorial/07-cli-tui-architecture-for-contributors.md b/tutorials/kilocode-tutorial/07-cli-tui-architecture-for-contributors.md
index 1b8e9779..abf25d60 100644
--- a/tutorials/kilocode-tutorial/07-cli-tui-architecture-for-contributors.md
+++ b/tutorials/kilocode-tutorial/07-cli-tui-architecture-for-contributors.md
@@ -7,6 +7,9 @@ parent: Kilo Code Tutorial
 
 # Chapter 7: CLI/TUI Architecture for Contributors
 
+Welcome to **Chapter 7: CLI/TUI Architecture for Contributors**. In this part of **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Kilo's CLI stack separates extension-host runtime, state client, ask dispatch, and UI rendering concerns.
 
 ## Architecture Blocks
@@ -28,3 +31,610 @@ Kilo's CLI stack separates extension-host runtime, state client, ask dispatch, a
 You now have a contributor-level map for Kilo CLI internals.
 
 Next: [Chapter 8: Production Operations and Governance](08-production-operations-and-governance.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- tutorial slug: **kilocode-tutorial**
+- chapter focus: **Chapter 7: CLI/TUI Architecture for Contributors**
+- system context: **Kilocode Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: CLI/TUI Architecture for Contributors`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Kilo Repository](https://github.com/Kilo-Org/kilocode)
+- [Kilo README](https://github.com/Kilo-Org/kilocode/blob/main/README.md)
+- [CLI agent loop docs](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/docs/AGENT_LOOP.md)
+- [CLI auth login flow](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/commands/auth/login.ts)
+- [Extension host](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/agent/extension-host.ts)
+
+### Cross-Tutorial Connection Map
+
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [Mistral Vibe Tutorial](../mistral-vibe-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Superset Terminal Tutorial](../superset-terminal-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: CLI/TUI Architecture for Contributors`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 7: CLI/TUI Architecture for Contributors
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: CLI/TUI Architecture for Contributors` as an operating subsystem inside **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: CLI/TUI Architecture for Contributors` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Kilo Repository](https://github.com/Kilo-Org/kilocode)
+  Why it matters: authoritative reference on `Kilo Repository` (github.com).
+- [Kilo README](https://github.com/Kilo-Org/kilocode/blob/main/README.md)
+  Why it matters: authoritative reference on `Kilo README` (github.com).
+- [CLI agent loop docs](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/docs/AGENT_LOOP.md)
+  Why it matters: authoritative reference on `CLI agent loop docs` (github.com).
+- [CLI auth login flow](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/commands/auth/login.ts)
+  Why it matters: authoritative reference on `CLI auth login flow` (github.com).
+- [Extension host](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/agent/extension-host.ts)
+  Why it matters: authoritative reference on `Extension host` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Extensions, MCP, and Custom Modes](06-extensions-mcp-and-custom-modes.md)
+- [Next Chapter: Chapter 8: Production Operations and Governance](08-production-operations-and-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kilocode-tutorial/08-production-operations-and-governance.md b/tutorials/kilocode-tutorial/08-production-operations-and-governance.md
index 93b4e970..434d9625 100644
--- a/tutorials/kilocode-tutorial/08-production-operations-and-governance.md
+++ b/tutorials/kilocode-tutorial/08-production-operations-and-governance.md
@@ -7,6 +7,9 @@ parent: Kilo Code Tutorial
 
 # Chapter 8: Production Operations and Governance
 
+Welcome to **Chapter 8: Production Operations and Governance**. In this part of **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Production Kilo adoption requires clear policy around auth, approvals, extension usage, and rollout controls.
 
 ## Governance Checklist
@@ -25,3 +28,609 @@ Production Kilo adoption requires clear policy around auth, approvals, extension
 ## Summary
 
 You now have a team-ready operational baseline for Kilo deployment and governance.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- tutorial slug: **kilocode-tutorial**
+- chapter focus: **Chapter 8: Production Operations and Governance**
+- system context: **Kilocode Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Operations and Governance`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Kilo Repository](https://github.com/Kilo-Org/kilocode)
+- [Kilo README](https://github.com/Kilo-Org/kilocode/blob/main/README.md)
+- [CLI agent loop docs](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/docs/AGENT_LOOP.md)
+- [CLI auth login flow](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/commands/auth/login.ts)
+- [Extension host](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/agent/extension-host.ts)
+
+### Cross-Tutorial Connection Map
+
+- [Kimi CLI Tutorial](../kimi-cli-tutorial/)
+- [Mistral Vibe Tutorial](../mistral-vibe-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Superset Terminal Tutorial](../superset-terminal-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Operations and Governance`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Operations and Governance` as an operating subsystem inside **Kilo Code Tutorial: Agentic Engineering from IDE and CLI Surfaces**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Operations and Governance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Kilo Repository](https://github.com/Kilo-Org/kilocode)
+  Why it matters: authoritative reference on `Kilo Repository` (github.com).
+- [Kilo README](https://github.com/Kilo-Org/kilocode/blob/main/README.md)
+  Why it matters: authoritative reference on `Kilo README` (github.com).
+- [CLI agent loop docs](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/docs/AGENT_LOOP.md)
+  Why it matters: authoritative reference on `CLI agent loop docs` (github.com).
+- [CLI auth login flow](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/commands/auth/login.ts)
+  Why it matters: authoritative reference on `CLI auth login flow` (github.com).
+- [Extension host](https://github.com/Kilo-Org/kilocode/blob/main/apps/cli/src/agent/extension-host.ts)
+  Why it matters: authoritative reference on `Extension host` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: CLI/TUI Architecture for Contributors](07-cli-tui-architecture-for-contributors.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kimi-cli-tutorial/01-getting-started.md b/tutorials/kimi-cli-tutorial/01-getting-started.md
index f39dda1a..af1fafd5 100644
--- a/tutorials/kimi-cli-tutorial/01-getting-started.md
+++ b/tutorials/kimi-cli-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Kimi CLI Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets Kimi CLI installed, configured, and running in a project directory.
 
 ## Quick Install
@@ -43,3 +46,601 @@ Then run `/login` to configure provider access.
 You now have Kimi CLI running with authenticated provider access.
 
 Next: [Chapter 2: Command Surface and Session Controls](02-command-surface-and-session-controls.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- tutorial slug: **kimi-cli-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Kimi Cli 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
+
+- [Kimi CLI Repository](https://github.com/MoonshotAI/kimi-cli)
+- [Kimi CLI README](https://github.com/MoonshotAI/kimi-cli/blob/main/README.md)
+- [Getting Started docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/guides/getting-started.md)
+- [Command reference](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/reference/kimi-command.md)
+- [MCP customization docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/customization/mcp.md)
+
+### Cross-Tutorial Connection Map
+
+- [GitHub Copilot CLI Tutorial](../copilot-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [OpenSkills Tutorial](../openskills-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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `kimi`, `install`, `Linux` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `macOS`, `curl`, `LsSf` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `kimi`.
+2. **Input normalization**: shape incoming data so `install` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Linux`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Kimi CLI Repository](https://github.com/MoonshotAI/kimi-cli)
+  Why it matters: authoritative reference on `Kimi CLI Repository` (github.com).
+- [Kimi CLI README](https://github.com/MoonshotAI/kimi-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Kimi CLI README` (github.com).
+- [Getting Started docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/guides/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started docs` (github.com).
+- [Command reference](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/reference/kimi-command.md)
+  Why it matters: authoritative reference on `Command reference` (github.com).
+- [MCP customization docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/customization/mcp.md)
+  Why it matters: authoritative reference on `MCP customization docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `kimi` and `install` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Command Surface and Session Controls](02-command-surface-and-session-controls.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kimi-cli-tutorial/02-command-surface-and-session-controls.md b/tutorials/kimi-cli-tutorial/02-command-surface-and-session-controls.md
index b2fd4d83..c663799b 100644
--- a/tutorials/kimi-cli-tutorial/02-command-surface-and-session-controls.md
+++ b/tutorials/kimi-cli-tutorial/02-command-surface-and-session-controls.md
@@ -7,6 +7,9 @@ parent: Kimi CLI Tutorial
 
 # Chapter 2: Command Surface and Session Controls
 
+Welcome to **Chapter 2: Command Surface and Session Controls**. In this part of **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Kimi CLI exposes rich command-line controls for model selection, directories, sessions, and execution boundaries.
 
 ## High-Value Flags
@@ -36,3 +39,598 @@ Kimi CLI exposes rich command-line controls for model selection, directories, se
 You now understand the core startup/session controls for predictable Kimi workflows.
 
 Next: [Chapter 3: Agents, Subagents, and Skills](03-agents-subagents-and-skills.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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- tutorial slug: **kimi-cli-tutorial**
+- chapter focus: **Chapter 2: Command Surface and Session Controls**
+- system context: **Kimi Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Command Surface and Session Controls`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Kimi CLI Repository](https://github.com/MoonshotAI/kimi-cli)
+- [Kimi CLI README](https://github.com/MoonshotAI/kimi-cli/blob/main/README.md)
+- [Getting Started docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/guides/getting-started.md)
+- [Command reference](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/reference/kimi-command.md)
+- [MCP customization docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/customization/mcp.md)
+
+### Cross-Tutorial Connection Map
+
+- [GitHub Copilot CLI Tutorial](../copilot-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [OpenSkills Tutorial](../openskills-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Command Surface and Session Controls`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 2: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Command Surface and Session Controls
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Command Surface and Session Controls` as an operating subsystem inside **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Command Surface and Session Controls` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Kimi CLI Repository](https://github.com/MoonshotAI/kimi-cli)
+  Why it matters: authoritative reference on `Kimi CLI Repository` (github.com).
+- [Kimi CLI README](https://github.com/MoonshotAI/kimi-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Kimi CLI README` (github.com).
+- [Getting Started docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/guides/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started docs` (github.com).
+- [Command reference](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/reference/kimi-command.md)
+  Why it matters: authoritative reference on `Command reference` (github.com).
+- [MCP customization docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/customization/mcp.md)
+  Why it matters: authoritative reference on `MCP customization docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Agents, Subagents, and Skills](03-agents-subagents-and-skills.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kimi-cli-tutorial/03-agents-subagents-and-skills.md b/tutorials/kimi-cli-tutorial/03-agents-subagents-and-skills.md
index 6356bdc0..75039b9b 100644
--- a/tutorials/kimi-cli-tutorial/03-agents-subagents-and-skills.md
+++ b/tutorials/kimi-cli-tutorial/03-agents-subagents-and-skills.md
@@ -7,6 +7,9 @@ parent: Kimi CLI Tutorial
 
 # Chapter 3: Agents, Subagents, and Skills
 
+Welcome to **Chapter 3: Agents, Subagents, and Skills**. In this part of **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Kimi CLI supports behavior customization through built-in/custom agents, subagents, and layered skills.
 
 ## Customization Layers
@@ -33,3 +36,598 @@ Kimi CLI supports behavior customization through built-in/custom agents, subagen
 You now have a strategy for standardized yet flexible Kimi behavior customization.
 
 Next: [Chapter 4: MCP Tooling and Security Model](04-mcp-tooling-and-security-model.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- tutorial slug: **kimi-cli-tutorial**
+- chapter focus: **Chapter 3: Agents, Subagents, and Skills**
+- system context: **Kimi Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Agents, Subagents, and 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
+
+- [Kimi CLI Repository](https://github.com/MoonshotAI/kimi-cli)
+- [Kimi CLI README](https://github.com/MoonshotAI/kimi-cli/blob/main/README.md)
+- [Getting Started docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/guides/getting-started.md)
+- [Command reference](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/reference/kimi-command.md)
+- [MCP customization docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/customization/mcp.md)
+
+### Cross-Tutorial Connection Map
+
+- [GitHub Copilot CLI Tutorial](../copilot-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [OpenSkills Tutorial](../openskills-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Agents, Subagents, and 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 3: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Agents, Subagents, and Skills
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Agents, Subagents, and Skills` as an operating subsystem inside **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Agents, Subagents, and Skills` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Kimi CLI Repository](https://github.com/MoonshotAI/kimi-cli)
+  Why it matters: authoritative reference on `Kimi CLI Repository` (github.com).
+- [Kimi CLI README](https://github.com/MoonshotAI/kimi-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Kimi CLI README` (github.com).
+- [Getting Started docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/guides/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started docs` (github.com).
+- [Command reference](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/reference/kimi-command.md)
+  Why it matters: authoritative reference on `Command reference` (github.com).
+- [MCP customization docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/customization/mcp.md)
+  Why it matters: authoritative reference on `MCP customization docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Command Surface and Session Controls](02-command-surface-and-session-controls.md)
+- [Next Chapter: Chapter 4: MCP Tooling and Security Model](04-mcp-tooling-and-security-model.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kimi-cli-tutorial/04-mcp-tooling-and-security-model.md b/tutorials/kimi-cli-tutorial/04-mcp-tooling-and-security-model.md
index ebc0200c..3b10f9ed 100644
--- a/tutorials/kimi-cli-tutorial/04-mcp-tooling-and-security-model.md
+++ b/tutorials/kimi-cli-tutorial/04-mcp-tooling-and-security-model.md
@@ -7,6 +7,9 @@ parent: Kimi CLI Tutorial
 
 # Chapter 4: MCP Tooling and Security Model
 
+Welcome to **Chapter 4: MCP Tooling and Security Model**. In this part of **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Kimi CLI can connect to external MCP servers to extend tool capabilities beyond built-ins.
 
 ## Core MCP Operations
@@ -34,3 +37,602 @@ kimi mcp remove context7
 You now know how to add MCP capabilities while preserving operator control.
 
 Next: [Chapter 5: ACP and IDE Integrations](05-acp-and-ide-integrations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- tutorial slug: **kimi-cli-tutorial**
+- chapter focus: **Chapter 4: MCP Tooling and Security Model**
+- system context: **Kimi Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: MCP Tooling and Security 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
+
+- [Kimi CLI Repository](https://github.com/MoonshotAI/kimi-cli)
+- [Kimi CLI README](https://github.com/MoonshotAI/kimi-cli/blob/main/README.md)
+- [Getting Started docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/guides/getting-started.md)
+- [Command reference](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/reference/kimi-command.md)
+- [MCP customization docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/customization/mcp.md)
+
+### Cross-Tutorial Connection Map
+
+- [GitHub Copilot CLI Tutorial](../copilot-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [OpenSkills Tutorial](../openskills-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: MCP Tooling and Security 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 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: MCP Tooling and Security Model
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `kimi`, `context7`, `transport` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: MCP Tooling and Security Model` as an operating subsystem inside **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `http`, `https`, `list` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: MCP Tooling and Security Model` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `kimi`.
+2. **Input normalization**: shape incoming data so `context7` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `transport`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Kimi CLI Repository](https://github.com/MoonshotAI/kimi-cli)
+  Why it matters: authoritative reference on `Kimi CLI Repository` (github.com).
+- [Kimi CLI README](https://github.com/MoonshotAI/kimi-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Kimi CLI README` (github.com).
+- [Getting Started docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/guides/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started docs` (github.com).
+- [Command reference](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/reference/kimi-command.md)
+  Why it matters: authoritative reference on `Command reference` (github.com).
+- [MCP customization docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/customization/mcp.md)
+  Why it matters: authoritative reference on `MCP customization docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `kimi` and `context7` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Agents, Subagents, and Skills](03-agents-subagents-and-skills.md)
+- [Next Chapter: Chapter 5: ACP and IDE Integrations](05-acp-and-ide-integrations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kimi-cli-tutorial/05-acp-and-ide-integrations.md b/tutorials/kimi-cli-tutorial/05-acp-and-ide-integrations.md
index 3aac259b..b7d37f43 100644
--- a/tutorials/kimi-cli-tutorial/05-acp-and-ide-integrations.md
+++ b/tutorials/kimi-cli-tutorial/05-acp-and-ide-integrations.md
@@ -7,6 +7,9 @@ parent: Kimi CLI Tutorial
 
 # Chapter 5: ACP and IDE Integrations
 
+Welcome to **Chapter 5: ACP and IDE Integrations**. In this part of **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Kimi CLI can run as an ACP server, enabling IDE and client integrations with multi-session agent workflows.
 
 ## ACP Entry Point
@@ -37,3 +40,602 @@ kimi acp
 You now have a pathway to use Kimi beyond standalone terminal sessions.
 
 Next: [Chapter 6: Shell Mode, Print Mode, and Wire Mode](06-shell-mode-print-mode-and-wire-mode.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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- tutorial slug: **kimi-cli-tutorial**
+- chapter focus: **Chapter 5: ACP and IDE Integrations**
+- system context: **Kimi Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: ACP and IDE Integrations`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Kimi CLI Repository](https://github.com/MoonshotAI/kimi-cli)
+- [Kimi CLI README](https://github.com/MoonshotAI/kimi-cli/blob/main/README.md)
+- [Getting Started docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/guides/getting-started.md)
+- [Command reference](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/reference/kimi-command.md)
+- [MCP customization docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/customization/mcp.md)
+
+### Cross-Tutorial Connection Map
+
+- [GitHub Copilot CLI Tutorial](../copilot-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [OpenSkills Tutorial](../openskills-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: ACP and IDE Integrations`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: ACP and IDE Integrations
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `kimi` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: ACP and IDE Integrations` as an operating subsystem inside **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: ACP and IDE Integrations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `kimi`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Kimi CLI Repository](https://github.com/MoonshotAI/kimi-cli)
+  Why it matters: authoritative reference on `Kimi CLI Repository` (github.com).
+- [Kimi CLI README](https://github.com/MoonshotAI/kimi-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Kimi CLI README` (github.com).
+- [Getting Started docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/guides/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started docs` (github.com).
+- [Command reference](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/reference/kimi-command.md)
+  Why it matters: authoritative reference on `Command reference` (github.com).
+- [MCP customization docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/customization/mcp.md)
+  Why it matters: authoritative reference on `MCP customization docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `kimi` and `kimi` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: MCP Tooling and Security Model](04-mcp-tooling-and-security-model.md)
+- [Next Chapter: Chapter 6: Shell Mode, Print Mode, and Wire Mode](06-shell-mode-print-mode-and-wire-mode.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kimi-cli-tutorial/06-shell-mode-print-mode-and-wire-mode.md b/tutorials/kimi-cli-tutorial/06-shell-mode-print-mode-and-wire-mode.md
index 4012ed71..af8fa7d6 100644
--- a/tutorials/kimi-cli-tutorial/06-shell-mode-print-mode-and-wire-mode.md
+++ b/tutorials/kimi-cli-tutorial/06-shell-mode-print-mode-and-wire-mode.md
@@ -7,6 +7,9 @@ parent: Kimi CLI Tutorial
 
 # Chapter 6: Shell Mode, Print Mode, and Wire Mode
 
+Welcome to **Chapter 6: Shell Mode, Print Mode, and Wire Mode**. In this part of **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Kimi offers multiple operating modes optimized for interactive coding, automation, or protocol-level integration.
 
 ## Mode Matrix
@@ -33,3 +36,602 @@ kimi --quiet -p "Generate a Conventional Commits message for staged diff"
 You now know when to use interactive mode versus automation/protocol modes.
 
 Next: [Chapter 7: Loop Control, Retries, and Long Tasks](07-loop-control-retries-and-long-tasks.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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- tutorial slug: **kimi-cli-tutorial**
+- chapter focus: **Chapter 6: Shell Mode, Print Mode, and Wire Mode**
+- system context: **Kimi Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Shell Mode, Print Mode, and Wire Mode`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Kimi CLI Repository](https://github.com/MoonshotAI/kimi-cli)
+- [Kimi CLI README](https://github.com/MoonshotAI/kimi-cli/blob/main/README.md)
+- [Getting Started docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/guides/getting-started.md)
+- [Command reference](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/reference/kimi-command.md)
+- [MCP customization docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/customization/mcp.md)
+
+### Cross-Tutorial Connection Map
+
+- [GitHub Copilot CLI Tutorial](../copilot-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [OpenSkills Tutorial](../openskills-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Shell Mode, Print Mode, and Wire Mode`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Shell Mode, Print Mode, and Wire Mode
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `kimi`, `quiet`, `Generate` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Shell Mode, Print Mode, and Wire Mode` as an operating subsystem inside **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Conventional`, `Commits`, `message` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Shell Mode, Print Mode, and Wire Mode` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `kimi`.
+2. **Input normalization**: shape incoming data so `quiet` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Generate`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Kimi CLI Repository](https://github.com/MoonshotAI/kimi-cli)
+  Why it matters: authoritative reference on `Kimi CLI Repository` (github.com).
+- [Kimi CLI README](https://github.com/MoonshotAI/kimi-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Kimi CLI README` (github.com).
+- [Getting Started docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/guides/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started docs` (github.com).
+- [Command reference](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/reference/kimi-command.md)
+  Why it matters: authoritative reference on `Command reference` (github.com).
+- [MCP customization docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/customization/mcp.md)
+  Why it matters: authoritative reference on `MCP customization docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `kimi` and `quiet` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: ACP and IDE Integrations](05-acp-and-ide-integrations.md)
+- [Next Chapter: Chapter 7: Loop Control, Retries, and Long Tasks](07-loop-control-retries-and-long-tasks.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kimi-cli-tutorial/07-loop-control-retries-and-long-tasks.md b/tutorials/kimi-cli-tutorial/07-loop-control-retries-and-long-tasks.md
index b702b49f..291f5821 100644
--- a/tutorials/kimi-cli-tutorial/07-loop-control-retries-and-long-tasks.md
+++ b/tutorials/kimi-cli-tutorial/07-loop-control-retries-and-long-tasks.md
@@ -7,6 +7,9 @@ parent: Kimi CLI Tutorial
 
 # Chapter 7: Loop Control, Retries, and Long Tasks
 
+Welcome to **Chapter 7: Loop Control, Retries, and Long Tasks**. In this part of **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Kimi includes control knobs for multi-step and long-running tasks where bounded execution is critical.
 
 ## Key Controls
@@ -33,3 +36,598 @@ These help teams prevent runaway loops and tune behavior for complex workflows.
 You now have an execution-bounding strategy for larger autonomous task loops.
 
 Next: [Chapter 8: Production Operations and Governance](08-production-operations-and-governance.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- tutorial slug: **kimi-cli-tutorial**
+- chapter focus: **Chapter 7: Loop Control, Retries, and Long Tasks**
+- system context: **Kimi Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Loop Control, Retries, and Long Tasks`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Kimi CLI Repository](https://github.com/MoonshotAI/kimi-cli)
+- [Kimi CLI README](https://github.com/MoonshotAI/kimi-cli/blob/main/README.md)
+- [Getting Started docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/guides/getting-started.md)
+- [Command reference](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/reference/kimi-command.md)
+- [MCP customization docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/customization/mcp.md)
+
+### Cross-Tutorial Connection Map
+
+- [GitHub Copilot CLI Tutorial](../copilot-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [OpenSkills Tutorial](../openskills-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Loop Control, Retries, and Long Tasks`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Loop Control, Retries, and Long Tasks
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Loop Control, Retries, and Long Tasks` as an operating subsystem inside **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Loop Control, Retries, and Long Tasks` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Kimi CLI Repository](https://github.com/MoonshotAI/kimi-cli)
+  Why it matters: authoritative reference on `Kimi CLI Repository` (github.com).
+- [Kimi CLI README](https://github.com/MoonshotAI/kimi-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Kimi CLI README` (github.com).
+- [Getting Started docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/guides/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started docs` (github.com).
+- [Command reference](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/reference/kimi-command.md)
+  Why it matters: authoritative reference on `Command reference` (github.com).
+- [MCP customization docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/customization/mcp.md)
+  Why it matters: authoritative reference on `MCP customization docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Shell Mode, Print Mode, and Wire Mode](06-shell-mode-print-mode-and-wire-mode.md)
+- [Next Chapter: Chapter 8: Production Operations and Governance](08-production-operations-and-governance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kimi-cli-tutorial/08-production-operations-and-governance.md b/tutorials/kimi-cli-tutorial/08-production-operations-and-governance.md
index 8d49274c..af77ac77 100644
--- a/tutorials/kimi-cli-tutorial/08-production-operations-and-governance.md
+++ b/tutorials/kimi-cli-tutorial/08-production-operations-and-governance.md
@@ -7,6 +7,9 @@ parent: Kimi CLI Tutorial
 
 # Chapter 8: Production Operations and Governance
 
+Welcome to **Chapter 8: Production Operations and Governance**. In this part of **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Team-scale Kimi usage needs clear policy around approvals, skills, integrations, and update workflows.
 
 ## Governance Checklist
@@ -32,3 +35,609 @@ Team-scale Kimi usage needs clear policy around approvals, skills, integrations,
 ## Summary
 
 You now have a production-ready operating framework for Kimi CLI across developer teams.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- tutorial slug: **kimi-cli-tutorial**
+- chapter focus: **Chapter 8: Production Operations and Governance**
+- system context: **Kimi Cli Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Operations and Governance`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Kimi CLI Repository](https://github.com/MoonshotAI/kimi-cli)
+- [Kimi CLI README](https://github.com/MoonshotAI/kimi-cli/blob/main/README.md)
+- [Getting Started docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/guides/getting-started.md)
+- [Command reference](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/reference/kimi-command.md)
+- [MCP customization docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/customization/mcp.md)
+
+### Cross-Tutorial Connection Map
+
+- [GitHub Copilot CLI Tutorial](../copilot-cli-tutorial/)
+- [OpenCode Tutorial](../opencode-tutorial/)
+- [Cline Tutorial](../cline-tutorial/)
+- [OpenSkills Tutorial](../openskills-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Operations and Governance`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Operations and Governance` as an operating subsystem inside **Kimi CLI Tutorial: Multi-Mode Terminal Agent with MCP and ACP**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Operations and Governance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Kimi CLI Repository](https://github.com/MoonshotAI/kimi-cli)
+  Why it matters: authoritative reference on `Kimi CLI Repository` (github.com).
+- [Kimi CLI README](https://github.com/MoonshotAI/kimi-cli/blob/main/README.md)
+  Why it matters: authoritative reference on `Kimi CLI README` (github.com).
+- [Getting Started docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/guides/getting-started.md)
+  Why it matters: authoritative reference on `Getting Started docs` (github.com).
+- [Command reference](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/reference/kimi-command.md)
+  Why it matters: authoritative reference on `Command reference` (github.com).
+- [MCP customization docs](https://github.com/MoonshotAI/kimi-cli/blob/main/docs/en/customization/mcp.md)
+  Why it matters: authoritative reference on `MCP customization docs` (github.com).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Loop Control, Retries, and Long Tasks](07-loop-control-retries-and-long-tasks.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kubernetes-operator-patterns/01-getting-started.md b/tutorials/kubernetes-operator-patterns/01-getting-started.md
index 6df62a0b..b675f48e 100644
--- a/tutorials/kubernetes-operator-patterns/01-getting-started.md
+++ b/tutorials/kubernetes-operator-patterns/01-getting-started.md
@@ -8,6 +8,9 @@ parent: Kubernetes Operator Patterns
 
 # Chapter 1: Getting Started with Kubernetes Operators
 
+Welcome to **Chapter 1: Getting Started with Kubernetes Operators**. In this part of **Kubernetes Operator Patterns: Building Production-Grade Controllers**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Install Operator SDK, create your first operator project, and understand the core concepts and architecture.
 
 ## Overview
@@ -616,4 +619,49 @@ Next, we'll dive deep into **Custom Resource Definitions** and how to design rob
 
 **Ready for the next chapter?** [Chapter 2: Custom Resource Definitions](02-custom-resources.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `operator`, `ctrl`, `MyApp` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Kubernetes Operators` as an operating subsystem inside **Kubernetes Operator Patterns: Building Production-Grade Controllers**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `kind`, `myApp`, `example` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Kubernetes Operators` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `operator`.
+2. **Input normalization**: shape incoming data so `ctrl` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `MyApp`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `operator` and `ctrl` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Custom Resource Definitions - Designing Robust APIs](02-custom-resources.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kubernetes-operator-patterns/02-custom-resources.md b/tutorials/kubernetes-operator-patterns/02-custom-resources.md
index 9694b0b0..87b4078f 100644
--- a/tutorials/kubernetes-operator-patterns/02-custom-resources.md
+++ b/tutorials/kubernetes-operator-patterns/02-custom-resources.md
@@ -8,6 +8,9 @@ parent: Kubernetes Operator Patterns
 
 # Chapter 2: Custom Resource Definitions - Designing Robust APIs
 
+Welcome to **Chapter 2: Custom Resource Definitions - Designing Robust APIs**. In this part of **Kubernetes Operator Patterns: Building Production-Grade Controllers**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master CRD design, OpenAPI validation, versioning strategies, and best practices for extending the Kubernetes API.
 
 ## Overview
@@ -765,4 +768,50 @@ Next, we'll explore the **reconciliation loop** - the heart of operator function
 
 **Ready for the next chapter?** [Chapter 3: The Reconciliation Loop](03-reconciliation-loop.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `json`, `Spec`, `name` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Custom Resource Definitions - Designing Robust APIs` as an operating subsystem inside **Kubernetes Operator Patterns: Building Production-Grade Controllers**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `example`, `postgresql`, `database` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Custom Resource Definitions - Designing Robust APIs` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `json`.
+2. **Input normalization**: shape incoming data so `Spec` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `name`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `json` and `Spec` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Kubernetes Operators](01-getting-started.md)
+- [Next Chapter: Chapter 3: The Reconciliation Loop - Core Operator Logic](03-reconciliation-loop.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kubernetes-operator-patterns/03-reconciliation-loop.md b/tutorials/kubernetes-operator-patterns/03-reconciliation-loop.md
index 6d14c217..dab3f4df 100644
--- a/tutorials/kubernetes-operator-patterns/03-reconciliation-loop.md
+++ b/tutorials/kubernetes-operator-patterns/03-reconciliation-loop.md
@@ -8,6 +8,9 @@ parent: Kubernetes Operator Patterns
 
 # Chapter 3: The Reconciliation Loop - Core Operator Logic
 
+Welcome to **Chapter 3: The Reconciliation Loop - Core Operator Logic**. In this part of **Kubernetes Operator Patterns: Building Production-Grade Controllers**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master the reconciliation loop, state management, idempotency, and error handling patterns that form the heart of Kubernetes operators.
 
 ## Overview
@@ -758,4 +761,50 @@ Next, we'll explore **managing owned resources** - creating and managing Pods, S
 
 **Ready for the next chapter?** [Chapter 4: Managing Owned Resources](04-owned-resources.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `myApp`, `ctrl`, `Result` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: The Reconciliation Loop - Core Operator Logic` as an operating subsystem inside **Kubernetes Operator Patterns: Building Production-Grade Controllers**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Name`, `appsv1`, `func` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: The Reconciliation Loop - Core Operator Logic` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `myApp`.
+2. **Input normalization**: shape incoming data so `ctrl` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Result`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `myApp` and `ctrl` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Custom Resource Definitions - Designing Robust APIs](02-custom-resources.md)
+- [Next Chapter: Chapter 4: Managing Owned Resources - Creating and Managing Kubernetes Objects](04-owned-resources.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kubernetes-operator-patterns/04-owned-resources.md b/tutorials/kubernetes-operator-patterns/04-owned-resources.md
index 755fa5cc..4301282b 100644
--- a/tutorials/kubernetes-operator-patterns/04-owned-resources.md
+++ b/tutorials/kubernetes-operator-patterns/04-owned-resources.md
@@ -8,6 +8,9 @@ parent: Kubernetes Operator Patterns
 
 # Chapter 4: Managing Owned Resources - Creating and Managing Kubernetes Objects
 
+Welcome to **Chapter 4: Managing Owned Resources - Creating and Managing Kubernetes Objects**. In this part of **Kubernetes Operator Patterns: Building Production-Grade Controllers**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master creating, updating, and managing Kubernetes resources that operators control, including Deployments, Services, ConfigMaps, and Secrets.
 
 ## Overview
@@ -777,4 +780,50 @@ Next, we'll explore **status and conditions** - reporting resource status and im
 
 **Ready for the next chapter?** [Chapter 5: Status and Conditions](05-status-conditions.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `myApp`, `Spec`, `corev1` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Managing Owned Resources - Creating and Managing Kubernetes Objects` as an operating subsystem inside **Kubernetes Operator Patterns: Building Production-Grade Controllers**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `appsv1`, `deployment`, `func` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Managing Owned Resources - Creating and Managing Kubernetes Objects` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `myApp`.
+2. **Input normalization**: shape incoming data so `Spec` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `corev1`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `myApp` and `Spec` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: The Reconciliation Loop - Core Operator Logic](03-reconciliation-loop.md)
+- [Next Chapter: Chapter 5: Status and Conditions - Reporting Resource Status and Implementing Condition Patterns](05-status-conditions.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kubernetes-operator-patterns/05-status-conditions.md b/tutorials/kubernetes-operator-patterns/05-status-conditions.md
index 38b4b619..a50fd8ff 100644
--- a/tutorials/kubernetes-operator-patterns/05-status-conditions.md
+++ b/tutorials/kubernetes-operator-patterns/05-status-conditions.md
@@ -8,6 +8,9 @@ parent: Kubernetes Operator Patterns
 
 # Chapter 5: Status and Conditions - Reporting Resource Status and Implementing Condition Patterns
 
+Welcome to **Chapter 5: Status and Conditions - Reporting Resource Status and Implementing Condition Patterns**. In this part of **Kubernetes Operator Patterns: Building Production-Grade Controllers**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master status reporting, condition patterns, and observability best practices for Kubernetes operators.
 
 ## Overview
@@ -763,4 +766,50 @@ Next, we'll explore **testing operators** - unit tests, integration tests, and e
 
 **Ready for the next chapter?** [Chapter 6: Testing Operators](06-testing.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `database`, `Status`, `status` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Status and Conditions - Reporting Resource Status and Implementing Condition Patterns` as an operating subsystem inside **Kubernetes Operator Patterns: Building Production-Grade Controllers**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `condition`, `json`, `metav1` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Status and Conditions - Reporting Resource Status and Implementing Condition Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `database`.
+2. **Input normalization**: shape incoming data so `Status` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `status`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `database` and `Status` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Managing Owned Resources - Creating and Managing Kubernetes Objects](04-owned-resources.md)
+- [Next Chapter: Chapter 6: Testing Operators - Unit Tests, Integration Tests, and envtest Framework](06-testing.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kubernetes-operator-patterns/06-testing.md b/tutorials/kubernetes-operator-patterns/06-testing.md
index 8d7e8375..5c249fb6 100644
--- a/tutorials/kubernetes-operator-patterns/06-testing.md
+++ b/tutorials/kubernetes-operator-patterns/06-testing.md
@@ -8,6 +8,9 @@ parent: Kubernetes Operator Patterns
 
 # Chapter 6: Testing Operators - Unit Tests, Integration Tests, and envtest Framework
 
+Welcome to **Chapter 6: Testing Operators - Unit Tests, Integration Tests, and envtest Framework**. In this part of **Kubernetes Operator Patterns: Building Production-Grade Controllers**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master comprehensive testing strategies for Kubernetes operators including unit tests, integration tests, and envtest framework.
 
 ## Overview
@@ -1115,4 +1118,50 @@ Next, we'll explore **observability & debugging** - metrics, logging, tracing, a
 
 **Ready for the next chapter?** [Chapter 7: Observability & Debugging](07-observability.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `test`, `deployment`, `database` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Testing Operators - Unit Tests, Integration Tests, and envtest Framework` as an operating subsystem inside **Kubernetes Operator Patterns: Building Production-Grade Controllers**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `func`, `Name`, `Expect` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Testing Operators - Unit Tests, Integration Tests, and envtest Framework` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `test`.
+2. **Input normalization**: shape incoming data so `deployment` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `database`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `test` and `deployment` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Status and Conditions - Reporting Resource Status and Implementing Condition Patterns](05-status-conditions.md)
+- [Next Chapter: Chapter 7: Observability & Debugging - Metrics, Logging, Tracing, and Troubleshooting](07-observability.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kubernetes-operator-patterns/07-observability.md b/tutorials/kubernetes-operator-patterns/07-observability.md
index 45706394..f3bf953b 100644
--- a/tutorials/kubernetes-operator-patterns/07-observability.md
+++ b/tutorials/kubernetes-operator-patterns/07-observability.md
@@ -8,6 +8,9 @@ parent: Kubernetes Operator Patterns
 
 # Chapter 7: Observability & Debugging - Metrics, Logging, Tracing, and Troubleshooting
 
+Welcome to **Chapter 7: Observability & Debugging - Metrics, Logging, Tracing, and Troubleshooting**. In this part of **Kubernetes Operator Patterns: Building Production-Grade Controllers**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master comprehensive observability for Kubernetes operators including metrics collection, structured logging, distributed tracing, and systematic debugging approaches.
 
 ## Overview
@@ -945,4 +948,50 @@ Next, we'll explore **production deployment** - OLM installation, Helm charts, s
 
 **Ready for the next chapter?** [Chapter 8: Production Deployment](08-production-deployment.md)
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `database`, `func`, `logger` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Observability & Debugging - Metrics, Logging, Tracing, and Troubleshooting` as an operating subsystem inside **Kubernetes Operator Patterns: Building Production-Grade Controllers**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `metrics`, `Name`, `span` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Observability & Debugging - Metrics, Logging, Tracing, and Troubleshooting` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `database`.
+2. **Input normalization**: shape incoming data so `func` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `logger`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `database` and `func` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Testing Operators - Unit Tests, Integration Tests, and envtest Framework](06-testing.md)
+- [Next Chapter: Chapter 8: Production Deployment - OLM, Helm Charts, Security, and Scaling](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/kubernetes-operator-patterns/08-production-deployment.md b/tutorials/kubernetes-operator-patterns/08-production-deployment.md
index df8e1466..145b1e8f 100644
--- a/tutorials/kubernetes-operator-patterns/08-production-deployment.md
+++ b/tutorials/kubernetes-operator-patterns/08-production-deployment.md
@@ -8,6 +8,9 @@ parent: Kubernetes Operator Patterns
 
 # Chapter 8: Production Deployment - OLM, Helm Charts, Security, and Scaling
 
+Welcome to **Chapter 8: Production Deployment - OLM, Helm Charts, Security, and Scaling**. In this part of **Kubernetes Operator Patterns: Building Production-Grade Controllers**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploy operators to production with Operator Lifecycle Manager, Helm charts, enterprise security, and automated scaling strategies.
 
 ## Overview
@@ -982,4 +985,49 @@ This concludes our comprehensive Kubernetes Operator Patterns tutorial. You now
 
 ---
 
-*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
\ No newline at end of file
+*Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `operator`, `postgresql`, `name` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment - OLM, Helm Charts, Security, and Scaling` as an operating subsystem inside **Kubernetes Operator Patterns: Building Production-Grade Controllers**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `namespace`, `example`, `yaml` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment - OLM, Helm Charts, Security, and Scaling` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `operator`.
+2. **Input normalization**: shape incoming data so `postgresql` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `name`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `operator` and `postgresql` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Observability & Debugging - Metrics, Logging, Tracing, and Troubleshooting](07-observability.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/lancedb-tutorial/01-getting-started.md b/tutorials/lancedb-tutorial/01-getting-started.md
index 2da21b1d..7ba21523 100644
--- a/tutorials/lancedb-tutorial/01-getting-started.md
+++ b/tutorials/lancedb-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ nav_order: 1
 
 # Chapter 1: Getting Started with LanceDB
 
+Welcome to **Chapter 1: Getting Started with LanceDB**. In this part of **LanceDB Tutorial: Serverless Vector Database for AI**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Install LanceDB, create your first database, and perform basic vector operations.
 
 ## Overview
@@ -637,3 +640,48 @@ Now that you have a working LanceDB setup, let's explore Data Modeling in Chapte
 **Ready for Chapter 2?** [Data Modeling](02-data-modeling.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `table`, `lancedb`, `text` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with LanceDB` as an operating subsystem inside **LanceDB Tutorial: Serverless Vector Database for AI**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `vector`, `connect`, `results` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with LanceDB` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `table`.
+2. **Input normalization**: shape incoming data so `lancedb` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `text`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `table` and `lancedb` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Data Modeling](02-data-modeling.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/lancedb-tutorial/02-data-modeling.md b/tutorials/lancedb-tutorial/02-data-modeling.md
index a768196c..a54df060 100644
--- a/tutorials/lancedb-tutorial/02-data-modeling.md
+++ b/tutorials/lancedb-tutorial/02-data-modeling.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Data Modeling
 
+Welcome to **Chapter 2: Data Modeling**. In this part of **LanceDB Tutorial: Serverless Vector Database for AI**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Design effective schemas for vector data, understand Lance data types, and model complex data structures.
 
 ## Overview
@@ -606,3 +609,49 @@ Now that you understand data modeling, let's explore Vector Operations in Chapte
 **Ready for Chapter 3?** [Vector Operations](03-vector-operations.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Vector`, `Optional`, `LanceModel` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Data Modeling` as an operating subsystem inside **LanceDB Tutorial: Serverless Vector Database for AI**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `None`, `lancedb`, `vector` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Data Modeling` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Vector`.
+2. **Input normalization**: shape incoming data so `Optional` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `LanceModel`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Vector` and `Optional` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with LanceDB](01-getting-started.md)
+- [Next Chapter: Chapter 3: Vector Operations](03-vector-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/lancedb-tutorial/03-vector-operations.md b/tutorials/lancedb-tutorial/03-vector-operations.md
index c2bfe271..5c2e46a5 100644
--- a/tutorials/lancedb-tutorial/03-vector-operations.md
+++ b/tutorials/lancedb-tutorial/03-vector-operations.md
@@ -7,6 +7,9 @@ nav_order: 3
 
 # Chapter 3: Vector Operations
 
+Welcome to **Chapter 3: Vector Operations**. In this part of **LanceDB Tutorial: Serverless Vector Database for AI**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master vector indexing, similarity search, and advanced query techniques for efficient retrieval.
 
 ## Overview
@@ -531,3 +534,49 @@ Now that you understand vector operations, let's explore Hybrid Search in Chapte
 **Ready for Chapter 4?** [Hybrid Search](04-hybrid-search.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `table`, `search`, `results` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Vector Operations` as an operating subsystem inside **LanceDB Tutorial: Serverless Vector Database for AI**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `limit`, `query`, `to_pandas` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Vector Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `table`.
+2. **Input normalization**: shape incoming data so `search` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `results`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `table` and `search` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Data Modeling](02-data-modeling.md)
+- [Next Chapter: Chapter 4: Hybrid Search](04-hybrid-search.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/lancedb-tutorial/04-hybrid-search.md b/tutorials/lancedb-tutorial/04-hybrid-search.md
index 3779acb5..411f8017 100644
--- a/tutorials/lancedb-tutorial/04-hybrid-search.md
+++ b/tutorials/lancedb-tutorial/04-hybrid-search.md
@@ -7,6 +7,9 @@ nav_order: 4
 
 # Chapter 4: Hybrid Search
 
+Welcome to **Chapter 4: Hybrid Search**. In this part of **LanceDB Tutorial: Serverless Vector Database for AI**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Combine vector similarity search with full-text search and SQL filters for powerful retrieval systems.
 
 ## Overview
@@ -555,3 +558,49 @@ Now that you understand hybrid search, let's explore Integrations in Chapter 5 f
 **Ready for Chapter 5?** [Integrations](05-integrations.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `search`, `limit`, `table` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Hybrid Search` as an operating subsystem inside **LanceDB Tutorial: Serverless Vector Database for AI**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `results`, `query`, `query_vector` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Hybrid Search` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `search`.
+2. **Input normalization**: shape incoming data so `limit` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `table`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `search` and `limit` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Vector Operations](03-vector-operations.md)
+- [Next Chapter: Chapter 5: Integrations](05-integrations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/lancedb-tutorial/05-integrations.md b/tutorials/lancedb-tutorial/05-integrations.md
index 4297d882..04fdaae2 100644
--- a/tutorials/lancedb-tutorial/05-integrations.md
+++ b/tutorials/lancedb-tutorial/05-integrations.md
@@ -7,6 +7,9 @@ nav_order: 5
 
 # Chapter 5: Integrations
 
+Welcome to **Chapter 5: Integrations**. In this part of **LanceDB Tutorial: Serverless Vector Database for AI**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Connect LanceDB with LangChain, LlamaIndex, embedding providers, and other tools in the AI ecosystem.
 
 ## Overview
@@ -562,3 +565,49 @@ Now that you know how to integrate LanceDB, let's explore Performance in Chapter
 **Ready for Chapter 6?** [Performance](06-performance.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `lancedb`, `table`, `documents` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Integrations` as an operating subsystem inside **LanceDB Tutorial: Serverless Vector Database for AI**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `vector`, `search`, `Create` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Integrations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `lancedb`.
+2. **Input normalization**: shape incoming data so `table` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `documents`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `lancedb` and `table` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Hybrid Search](04-hybrid-search.md)
+- [Next Chapter: Chapter 6: Performance](06-performance.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/lancedb-tutorial/06-performance.md b/tutorials/lancedb-tutorial/06-performance.md
index 2af7d63c..08d30cf6 100644
--- a/tutorials/lancedb-tutorial/06-performance.md
+++ b/tutorials/lancedb-tutorial/06-performance.md
@@ -7,6 +7,9 @@ nav_order: 6
 
 # Chapter 6: Performance
 
+Welcome to **Chapter 6: Performance**. In this part of **LanceDB Tutorial: Serverless Vector Database for AI**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Optimize LanceDB for speed, memory efficiency, and throughput with indexing strategies and query tuning.
 
 ## Overview
@@ -619,3 +622,49 @@ Now that you can optimize performance, let's explore Production Deployment in Ch
 **Ready for Chapter 7?** [Production Deployment](07-production.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `table`, `query`, `results` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Performance` as an operating subsystem inside **LanceDB Tutorial: Serverless Vector Database for AI**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `search`, `limit`, `self` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Performance` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `table`.
+2. **Input normalization**: shape incoming data so `query` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `results`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `table` and `query` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Integrations](05-integrations.md)
+- [Next Chapter: Chapter 7: Production Deployment](07-production.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/lancedb-tutorial/07-production.md b/tutorials/lancedb-tutorial/07-production.md
index 8a86859e..0aa2c9c0 100644
--- a/tutorials/lancedb-tutorial/07-production.md
+++ b/tutorials/lancedb-tutorial/07-production.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Production Deployment
 
+Welcome to **Chapter 7: Production Deployment**. In this part of **LanceDB Tutorial: Serverless Vector Database for AI**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploy LanceDB to production with cloud storage, monitoring, backup strategies, and operational best practices.
 
 ## Overview
@@ -613,3 +616,49 @@ Now that you can deploy to production, let's explore Advanced Patterns in Chapte
 **Ready for Chapter 8?** [Advanced Patterns](08-advanced-patterns.md)
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `lancedb`, `table`, `bucket` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Production Deployment` as an operating subsystem inside **LanceDB Tutorial: Serverless Vector Database for AI**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `self`, `connect`, `time` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `lancedb`.
+2. **Input normalization**: shape incoming data so `table` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `bucket`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `lancedb` and `table` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Performance](06-performance.md)
+- [Next Chapter: Chapter 8: Advanced Patterns](08-advanced-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/lancedb-tutorial/08-advanced-patterns.md b/tutorials/lancedb-tutorial/08-advanced-patterns.md
index 26e15221..3c76bc0a 100644
--- a/tutorials/lancedb-tutorial/08-advanced-patterns.md
+++ b/tutorials/lancedb-tutorial/08-advanced-patterns.md
@@ -7,6 +7,9 @@ nav_order: 8
 
 # Chapter 8: Advanced Patterns
 
+Welcome to **Chapter 8: Advanced Patterns**. In this part of **LanceDB Tutorial: Serverless Vector Database for AI**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Implement multi-tenancy, document chunking, RAG systems, and other advanced patterns for production applications.
 
 ## Overview
@@ -675,3 +678,48 @@ Congratulations! You've completed the LanceDB Tutorial. You now have the knowled
 ---
 
 *Generated for [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `table`, `query` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Advanced Patterns` as an operating subsystem inside **LanceDB Tutorial: Serverless Vector Database for AI**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `embedder`, `tenant_id`, `content` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Advanced Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `table` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `query`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `table` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Production Deployment](07-production.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langchain-architecture-guide/01-getting-started.md b/tutorials/langchain-architecture-guide/01-getting-started.md
index da2ab6e3..4fe5aebe 100644
--- a/tutorials/langchain-architecture-guide/01-getting-started.md
+++ b/tutorials/langchain-architecture-guide/01-getting-started.md
@@ -8,6 +8,9 @@ parent: "LangChain Architecture - Internal Design Deep Dive"
 
 # Chapter 1: Getting Started -- Ecosystem Overview
 
+Welcome to **Chapter 1: Getting Started -- Ecosystem Overview**. In this part of **LangChain Architecture: Internal Design Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 When you `pip install langchain`, you are pulling in one piece of a carefully partitioned ecosystem. Understanding how the packages fit together is the first step to understanding the architecture itself. This chapter maps the package hierarchy, explains the dependency philosophy, and shows you exactly where every abstraction lives in the source tree.
 
 ## The Package Hierarchy
@@ -396,3 +399,48 @@ Now that you understand the ecosystem layout, we are ready to dive into the most
 
 ---
 *Built with insights from the [LangChain](https://github.com/langchain-ai/langchain) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `langchain`, `model`, `invoke` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started -- Ecosystem Overview` as an operating subsystem inside **LangChain Architecture: Internal Design Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Tier`, `core`, `ChatPromptTemplate` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started -- Ecosystem Overview` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `langchain`.
+2. **Input normalization**: shape incoming data so `model` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `invoke`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langchain)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `langchain` and `model` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: The Runnable Interface (LCEL)](02-runnable-interface.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langchain-architecture-guide/02-runnable-interface.md b/tutorials/langchain-architecture-guide/02-runnable-interface.md
index 16c03470..daaa186b 100644
--- a/tutorials/langchain-architecture-guide/02-runnable-interface.md
+++ b/tutorials/langchain-architecture-guide/02-runnable-interface.md
@@ -8,6 +8,9 @@ parent: "LangChain Architecture - Internal Design Deep Dive"
 
 # Chapter 2: The Runnable Interface (LCEL)
 
+Welcome to **Chapter 2: The Runnable Interface (LCEL)**. In this part of **LangChain Architecture: Internal Design Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 The `Runnable` interface is the single most important abstraction in LangChain. Every prompt template, chat model, output parser, retriever, and tool implements it. This chapter dissects the protocol itself, the concrete `Runnable*` classes that compose computation graphs, and the mechanics of the pipe operator that makes the LangChain Expression Language (LCEL) possible.
 
 ## The Runnable Protocol
@@ -457,3 +460,49 @@ With the Runnable protocol understood, we can now examine how chat models implem
 
 ---
 *Built with insights from the [LangChain](https://github.com/langchain-ai/langchain) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `config`, `self`, `input` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: The Runnable Interface (LCEL)` as an operating subsystem inside **LangChain Architecture: Internal Design Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Input`, `Runnable`, `invoke` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: The Runnable Interface (LCEL)` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `config`.
+2. **Input normalization**: shape incoming data so `self` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `input`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langchain)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `config` and `self` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started -- Ecosystem Overview](01-getting-started.md)
+- [Next Chapter: Chapter 3: Chat Model Architecture](03-chat-model-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langchain-architecture-guide/03-chat-model-architecture.md b/tutorials/langchain-architecture-guide/03-chat-model-architecture.md
index 3483dbea..5dd402e5 100644
--- a/tutorials/langchain-architecture-guide/03-chat-model-architecture.md
+++ b/tutorials/langchain-architecture-guide/03-chat-model-architecture.md
@@ -8,6 +8,9 @@ parent: "LangChain Architecture - Internal Design Deep Dive"
 
 # Chapter 3: Chat Model Architecture
 
+Welcome to **Chapter 3: Chat Model Architecture**. In this part of **LangChain Architecture: Internal Design Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Chat models are the computational heart of most LangChain applications. This chapter explores how `BaseChatModel` is designed, how the message type system works, how streaming is implemented at the model layer, and how the callback system weaves through every invocation.
 
 ## The BaseChatModel Hierarchy
@@ -539,3 +542,49 @@ Now that you understand how individual models work, let's explore how they are c
 
 ---
 *Built with insights from the [LangChain](https://github.com/langchain-ai/langchain) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `model`, `messages`, `self` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Chat Model Architecture` as an operating subsystem inside **LangChain Architecture: Internal Design Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `content`, `input`, `BaseChatModel` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Chat Model Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `model`.
+2. **Input normalization**: shape incoming data so `messages` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `self`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langchain)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `model` and `messages` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: The Runnable Interface (LCEL)](02-runnable-interface.md)
+- [Next Chapter: Chapter 4: Chain Composition](04-chain-composition.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langchain-architecture-guide/04-chain-composition.md b/tutorials/langchain-architecture-guide/04-chain-composition.md
index 3f1e0201..30eb195c 100644
--- a/tutorials/langchain-architecture-guide/04-chain-composition.md
+++ b/tutorials/langchain-architecture-guide/04-chain-composition.md
@@ -8,6 +8,9 @@ parent: "LangChain Architecture - Internal Design Deep Dive"
 
 # Chapter 4: Chain Composition
 
+Welcome to **Chapter 4: Chain Composition**. In this part of **LangChain Architecture: Internal Design Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Chains are the bread and butter of LangChain applications. This chapter examines two generations of chain design -- the legacy `Chain` class and the modern LCEL approach -- and explores the internal mechanics of routing, fallbacks, retry logic, and chain compilation.
 
 ## Two Generations of Chains
@@ -539,3 +542,49 @@ With chain composition understood, let's explore how data enters the pipeline in
 
 ---
 *Built with insights from the [LangChain](https://github.com/langchain-ai/langchain) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `chain`, `self`, `invoke` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Chain Composition` as an operating subsystem inside **LangChain Architecture: Internal Design Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `model`, `topic`, `Output` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Chain Composition` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `chain`.
+2. **Input normalization**: shape incoming data so `self` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `invoke`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langchain)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `chain` and `self` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Chat Model Architecture](03-chat-model-architecture.md)
+- [Next Chapter: Chapter 5: Document Loading & Splitting](05-document-loading-splitting.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langchain-architecture-guide/05-document-loading-splitting.md b/tutorials/langchain-architecture-guide/05-document-loading-splitting.md
index 81a4b3fb..77fc8e7c 100644
--- a/tutorials/langchain-architecture-guide/05-document-loading-splitting.md
+++ b/tutorials/langchain-architecture-guide/05-document-loading-splitting.md
@@ -8,6 +8,9 @@ parent: "LangChain Architecture - Internal Design Deep Dive"
 
 # Chapter 5: Document Loading & Splitting
 
+Welcome to **Chapter 5: Document Loading & Splitting**. In this part of **LangChain Architecture: Internal Design Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Before any information can be retrieved or reasoned about, it must be loaded from its source and broken into manageable pieces. This chapter dissects the `BaseLoader` and `TextSplitter` hierarchies, examines how metadata flows through the pipeline, and explores the architectural decisions behind different chunking strategies.
 
 ## The Document Data Model
@@ -587,3 +590,49 @@ Now that we understand how documents are loaded and split, let's explore how the
 
 ---
 *Built with insights from the [LangChain](https://github.com/langchain-ai/langchain) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Document`, `List`, `self` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Document Loading & Splitting` as an operating subsystem inside **LangChain Architecture: Internal Design Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `text`, `chunk_size`, `metadata` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Document Loading & Splitting` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Document`.
+2. **Input normalization**: shape incoming data so `List` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `self`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langchain)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `Document` and `List` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Chain Composition](04-chain-composition.md)
+- [Next Chapter: Chapter 6: Vector Store Abstraction](06-vector-store-abstraction.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langchain-architecture-guide/06-vector-store-abstraction.md b/tutorials/langchain-architecture-guide/06-vector-store-abstraction.md
index e8cf95c0..c30dcfe2 100644
--- a/tutorials/langchain-architecture-guide/06-vector-store-abstraction.md
+++ b/tutorials/langchain-architecture-guide/06-vector-store-abstraction.md
@@ -8,6 +8,9 @@ parent: "LangChain Architecture - Internal Design Deep Dive"
 
 # Chapter 6: Vector Store Abstraction
 
+Welcome to **Chapter 6: Vector Store Abstraction**. In this part of **LangChain Architecture: Internal Design Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Vector stores are where semantic search happens. This chapter examines the `VectorStore` interface, how embedding models are abstracted, the `Retriever` pattern that connects vector stores to LCEL chains, and how LangChain achieves backend portability across dozens of vector databases.
 
 ## The Embedding Model Contract
@@ -576,3 +579,49 @@ With data loading, splitting, embedding, and retrieval covered, we are ready to
 
 ---
 *Built with insights from the [LangChain](https://github.com/langchain-ai/langchain) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `List`, `query`, `self` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Vector Store Abstraction` as an operating subsystem inside **LangChain Architecture: Internal Design Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Document`, `VectorStore`, `float` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Vector Store Abstraction` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `List`.
+2. **Input normalization**: shape incoming data so `query` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `self`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langchain)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `List` and `query` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Document Loading & Splitting](05-document-loading-splitting.md)
+- [Next Chapter: Chapter 7: Agent Architecture](07-agent-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langchain-architecture-guide/07-agent-architecture.md b/tutorials/langchain-architecture-guide/07-agent-architecture.md
index d691531f..acef0dc0 100644
--- a/tutorials/langchain-architecture-guide/07-agent-architecture.md
+++ b/tutorials/langchain-architecture-guide/07-agent-architecture.md
@@ -8,6 +8,9 @@ parent: "LangChain Architecture - Internal Design Deep Dive"
 
 # Chapter 7: Agent Architecture
 
+Welcome to **Chapter 7: Agent Architecture**. In this part of **LangChain Architecture: Internal Design Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Agents are LangChain components that use LLMs to decide which actions to take. Unlike chains, where the sequence of operations is hardcoded, agents dynamically choose tools and control flow at runtime. This chapter dissects the `AgentExecutor`, the tool binding protocol, the ReAct loop, structured outputs, and the evolution toward LangGraph-based agents.
 
 ## Agent vs Chain: The Core Distinction
@@ -593,3 +596,49 @@ With the agent architecture understood, let's explore how to run these systems i
 
 ---
 *Built with insights from the [LangChain](https://github.com/langchain-ai/langchain) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `tool`, `agent`, `tools` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Agent Architecture` as an operating subsystem inside **LangChain Architecture: Internal Design Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `messages`, `self`, `name` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Agent Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `tool`.
+2. **Input normalization**: shape incoming data so `agent` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `tools`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langchain)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `tool` and `agent` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Vector Store Abstraction](06-vector-store-abstraction.md)
+- [Next Chapter: Chapter 8: Production Patterns](08-production-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langchain-architecture-guide/08-production-patterns.md b/tutorials/langchain-architecture-guide/08-production-patterns.md
index d69bf34a..f68b4a14 100644
--- a/tutorials/langchain-architecture-guide/08-production-patterns.md
+++ b/tutorials/langchain-architecture-guide/08-production-patterns.md
@@ -8,6 +8,9 @@ parent: "LangChain Architecture - Internal Design Deep Dive"
 
 # Chapter 8: Production Patterns
 
+Welcome to **Chapter 8: Production Patterns**. In this part of **LangChain Architecture: Internal Design Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Moving LangChain applications from prototypes to production requires understanding the framework's built-in support for observability, tracing, caching, error handling, and deployment. This chapter explores the callback system in depth, LangSmith integration, caching layers, and deployment strategies.
 
 ## The Callback System: Deep Dive
@@ -628,3 +631,48 @@ The common thread through all of these is the `Runnable` interface. Every compon
 
 ---
 *Built with insights from the [LangChain](https://github.com/langchain-ai/langchain) project.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `chain`, `run_id` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Patterns` as an operating subsystem inside **LangChain Architecture: Internal Design Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `cache`, `model`, `serialized` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `chain` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `run_id`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langchain)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `chain` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Agent Architecture](07-agent-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langchain-tutorial/01-getting-started.md b/tutorials/langchain-tutorial/01-getting-started.md
index 514253ff..306d6533 100644
--- a/tutorials/langchain-tutorial/01-getting-started.md
+++ b/tutorials/langchain-tutorial/01-getting-started.md
@@ -170,3 +170,48 @@ Now that you have the basics working, you're ready to explore more advanced feat
 **Ready for more? Continue to [Chapter 2: Prompt Templates & Chains](02-prompt-templates.md)**
 
 *What would you like to build with your new LangChain setup? Try modifying the example to ask different questions or change the system message!* 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `langchain`, `messages`, `chat` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with LangChain` as an operating subsystem inside **LangChain Tutorial: Building AI Applications with Large Language Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `content`, `response`, `install` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with LangChain` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `langchain`.
+2. **Input normalization**: shape incoming data so `messages` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `chat`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langchain)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `langchain` and `messages` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Prompt Templates & Chains](02-prompt-templates.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langchain-tutorial/02-prompt-templates.md b/tutorials/langchain-tutorial/02-prompt-templates.md
index 8a586e55..340ad3eb 100644
--- a/tutorials/langchain-tutorial/02-prompt-templates.md
+++ b/tutorials/langchain-tutorial/02-prompt-templates.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: Prompt Templates & Chains
 
+Welcome to **Chapter 2: Prompt Templates & Chains**. In this part of **LangChain Tutorial: Building AI Applications with Large Language Models**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Welcome back! Now that you have LangChain set up and working, let's learn about one of its most powerful features: **Prompt Templates** and **Chains**. These tools will help you create consistent, reusable interactions with language models.
 
 ## What Problem Do Prompt Templates Solve?
@@ -331,3 +334,49 @@ Ready to add memory to your applications? In [Chapter 3: Memory Systems](03-memo
 3. Experiment with different variables and see how they affect results
 
 *What's the most interesting chain you can think of building?* 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `ChatPromptTemplate`, `from_template`, `query` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Prompt Templates & Chains` as an operating subsystem inside **LangChain Tutorial: Building AI Applications with Large Language Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `template`, `topic`, `invoke` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Prompt Templates & Chains` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `ChatPromptTemplate`.
+2. **Input normalization**: shape incoming data so `from_template` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `query`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langchain)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `ChatPromptTemplate` and `from_template` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with LangChain](01-getting-started.md)
+- [Next Chapter: Chapter 3: Memory Systems](03-memory-systems.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langchain-tutorial/03-memory-systems.md b/tutorials/langchain-tutorial/03-memory-systems.md
index 06c8c6a4..6a17096b 100644
--- a/tutorials/langchain-tutorial/03-memory-systems.md
+++ b/tutorials/langchain-tutorial/03-memory-systems.md
@@ -370,3 +370,49 @@ Now that your applications can remember conversations, let's learn about working
 Create a memory-enabled chatbot that remembers user preferences (like favorite color, programming language, etc.) and uses that information in future conversations.
 
 *What kind of memory would you use for a personal assistant that needs to remember appointments, preferences, and ongoing tasks?* 🤔
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `memory`, `input`, `langchain` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Memory Systems` as an operating subsystem inside **LangChain Tutorial: Building AI Applications with Large Language Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `ChatOpenAI`, `ConversationBufferMemory`, `chain` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Memory Systems` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `memory`.
+2. **Input normalization**: shape incoming data so `input` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `langchain`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langchain)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `memory` and `input` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Prompt Templates & Chains](02-prompt-templates.md)
+- [Next Chapter: Chapter 4: Document Loading & Processing](04-document-processing.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langchain-tutorial/04-document-processing.md b/tutorials/langchain-tutorial/04-document-processing.md
index dc03b62d..0de30798 100644
--- a/tutorials/langchain-tutorial/04-document-processing.md
+++ b/tutorials/langchain-tutorial/04-document-processing.md
@@ -446,3 +446,49 @@ Create a document processing pipeline that:
 5. Saves the processed chunks for later use
 
 *What types of documents do you want to make searchable with AI?* 📚
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `documents`, `langchain`, `load` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Document Loading & Processing` as an operating subsystem inside **LangChain Tutorial: Building AI Applications with Large Language Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `metadata`, `chunks`, `Load` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Document Loading & Processing` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `documents`.
+2. **Input normalization**: shape incoming data so `langchain` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `load`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langchain)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `documents` and `langchain` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Memory Systems](03-memory-systems.md)
+- [Next Chapter: Chapter 5: Vector Stores & Retrieval](05-vector-stores.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langchain-tutorial/05-vector-stores.md b/tutorials/langchain-tutorial/05-vector-stores.md
index 1165bf67..9cbf14f3 100644
--- a/tutorials/langchain-tutorial/05-vector-stores.md
+++ b/tutorials/langchain-tutorial/05-vector-stores.md
@@ -482,3 +482,49 @@ Now that you can retrieve relevant information, let's learn about autonomous age
 Build a RAG system that can answer questions about a specific domain (like programming, cooking, or history). Experiment with different chunk sizes and embedding models to see what works best for your use case.
 
 *What's the most interesting application you can think of for RAG systems?* 🤖
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `documents`, `print`, `page_content` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Vector Stores & Retrieval` as an operating subsystem inside **LangChain Tutorial: Building AI Applications with Large Language Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `vectorstore`, `embeddings`, `vector` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Vector Stores & Retrieval` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `documents`.
+2. **Input normalization**: shape incoming data so `print` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `page_content`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langchain)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `documents` and `print` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Document Loading & Processing](04-document-processing.md)
+- [Next Chapter: Chapter 6: Agents & Tools](06-agents-tools.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langchain-tutorial/06-agents-tools.md b/tutorials/langchain-tutorial/06-agents-tools.md
index bb7f72d6..645fa08c 100644
--- a/tutorials/langchain-tutorial/06-agents-tools.md
+++ b/tutorials/langchain-tutorial/06-agents-tools.md
@@ -709,3 +709,49 @@ Now that you understand agents and tools, let's explore advanced chains and cust
 4. Set up performance monitoring for your agents
 
 *What kind of autonomous agent will you build first?* 🤖
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `tools`, `agent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Agents & Tools` as an operating subsystem inside **LangChain Tutorial: Building AI Applications with Large Language Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `memory`, `tool`, `query` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Agents & Tools` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `tools` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `agent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langchain)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `tools` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Vector Stores & Retrieval](05-vector-stores.md)
+- [Next Chapter: Chapter 7: Advanced Chains](07-advanced-chains.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langchain-tutorial/07-advanced-chains.md b/tutorials/langchain-tutorial/07-advanced-chains.md
index 3e9b208f..31889626 100644
--- a/tutorials/langchain-tutorial/07-advanced-chains.md
+++ b/tutorials/langchain-tutorial/07-advanced-chains.md
@@ -779,3 +779,49 @@ Now that you understand advanced chains, let's explore production deployment con
 4. Add performance monitoring and optimization to your chains
 
 *What kind of advanced chain will you build first?* 🔗
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `chain`, `result` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Advanced Chains` as an operating subsystem inside **LangChain Tutorial: Building AI Applications with Large Language Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `input`, `inputs`, `Dict` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Advanced Chains` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `chain` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `result`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langchain)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `chain` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Agents & Tools](06-agents-tools.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langchain-tutorial/08-production-deployment.md b/tutorials/langchain-tutorial/08-production-deployment.md
index 480fb5cd..46b76af5 100644
--- a/tutorials/langchain-tutorial/08-production-deployment.md
+++ b/tutorials/langchain-tutorial/08-production-deployment.md
@@ -927,3 +927,49 @@ Your LangChain journey is complete! You now have the knowledge and tools to buil
 - **Experiment and Innovate** - Try new combinations of chains and tools
 
 *What production LangChain application will you build first?* 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `time`, `metrics` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **LangChain Tutorial: Building AI Applications with Large Language Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `config`, `chain`, `chain_name` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `time` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `metrics`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langchain)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `time` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Advanced Chains](07-advanced-chains.md)
+- [Next Chapter: Chapter 9: Evaluation, Monitoring, and Observability](09-evaluation-monitoring.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langchain-tutorial/09-evaluation-monitoring.md b/tutorials/langchain-tutorial/09-evaluation-monitoring.md
index 11319a55..b964e888 100644
--- a/tutorials/langchain-tutorial/09-evaluation-monitoring.md
+++ b/tutorials/langchain-tutorial/09-evaluation-monitoring.md
@@ -8,6 +8,9 @@ parent: LangChain Tutorial
 
 # Chapter 9: Evaluation, Monitoring, and Observability
 
+Welcome to **Chapter 9: Evaluation, Monitoring, and Observability**. In this part of **LangChain Tutorial: Building AI Applications with Large Language Models**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Evaluate LangChain application performance, monitor production deployments, and implement comprehensive observability.
 
 ## Evaluation Frameworks
@@ -744,4 +747,49 @@ monitor = PerformanceMonitor(threshold_score=0.8)
 monitor.track_performance("qa_chain", evaluation_score, latency_seconds)
 ```
 
-This evaluation and monitoring chapter provides comprehensive tools for maintaining and improving LangChain application performance in production environments. The combination of evaluation frameworks, monitoring systems, and continuous improvement processes ensures your AI applications remain reliable and effective over time. 
\ No newline at end of file
+This evaluation and monitoring chapter provides comprehensive tools for maintaining and improving LangChain application performance in production environments. The combination of evaluation frameworks, monitoring systems, and continuous improvement processes ensures your AI applications remain reliable and effective over time.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `chain_name`, `error` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 9: Evaluation, Monitoring, and Observability` as an operating subsystem inside **LangChain Tutorial: Building AI Applications with Large Language Models**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `model`, `print`, `runs` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 9: Evaluation, Monitoring, and Observability` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `chain_name` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `error`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langchain)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `chain_name` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langflow-tutorial/01-getting-started.md b/tutorials/langflow-tutorial/01-getting-started.md
index fd3656ca..6c376cbd 100644
--- a/tutorials/langflow-tutorial/01-getting-started.md
+++ b/tutorials/langflow-tutorial/01-getting-started.md
@@ -7,6 +7,9 @@ parent: Langflow Tutorial
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Langflow Tutorial: Visual AI Agent and Workflow Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter gets Langflow running locally so you can build and test flows immediately.
 
 ## Learning Goals
@@ -42,3 +45,598 @@ Langflow starts at `http://127.0.0.1:7860` by default.
 You now have a working Langflow environment ready for architecture and workflow design.
 
 Next: [Chapter 2: Platform Architecture](02-platform-architecture.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- tutorial slug: **langflow-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Langflow 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
+
+- [Langflow Repository](https://github.com/langflow-ai/langflow)
+- [Langflow Releases](https://github.com/langflow-ai/langflow/releases)
+- [Langflow Docs](https://docs.langflow.org/)
+- [Langflow Deployment Docs](https://docs.langflow.org/deployment-overview)
+
+### Cross-Tutorial Connection Map
+
+- [Flowise Tutorial](../flowise-llm-orchestration/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LangGraph Tutorial](../langgraph-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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 1: Getting Started
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 1: Getting Started
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 1: Getting Started
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 1: Getting Started
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `langflow`, `install` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Langflow Tutorial: Visual AI Agent and Workflow Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `langflow`.
+2. **Input normalization**: shape incoming data so `install` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Langflow Repository](https://github.com/langflow-ai/langflow)
+  Why it matters: authoritative reference on `Langflow Repository` (github.com).
+- [Langflow Releases](https://github.com/langflow-ai/langflow/releases)
+  Why it matters: authoritative reference on `Langflow Releases` (github.com).
+- [Langflow Docs](https://docs.langflow.org/)
+  Why it matters: authoritative reference on `Langflow Docs` (docs.langflow.org).
+- [Langflow Deployment Docs](https://docs.langflow.org/deployment-overview)
+  Why it matters: authoritative reference on `Langflow Deployment Docs` (docs.langflow.org).
+
+Suggested trace strategy:
+- search upstream code for `langflow` and `install` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Platform Architecture](02-platform-architecture.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langflow-tutorial/02-platform-architecture.md b/tutorials/langflow-tutorial/02-platform-architecture.md
index b07bb653..6a0d9791 100644
--- a/tutorials/langflow-tutorial/02-platform-architecture.md
+++ b/tutorials/langflow-tutorial/02-platform-architecture.md
@@ -7,6 +7,9 @@ parent: Langflow Tutorial
 
 # Chapter 2: Platform Architecture
 
+Welcome to **Chapter 2: Platform Architecture**. In this part of **Langflow Tutorial: Visual AI Agent and Workflow Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Langflow combines a visual editor, execution runtime, and deployment surfaces in one platform.
 
 ## Architecture View
@@ -38,3 +41,599 @@ flowchart LR
 You now understand where to place design, logic, and deployment concerns in Langflow.
 
 Next: [Chapter 3: Visual Flow Builder](03-visual-flow-builder.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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- tutorial slug: **langflow-tutorial**
+- chapter focus: **Chapter 2: Platform Architecture**
+- system context: **Langflow Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Platform 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
+
+- [Langflow Repository](https://github.com/langflow-ai/langflow)
+- [Langflow Releases](https://github.com/langflow-ai/langflow/releases)
+- [Langflow Docs](https://docs.langflow.org/)
+- [Langflow Deployment Docs](https://docs.langflow.org/deployment-overview)
+
+### Cross-Tutorial Connection Map
+
+- [Flowise Tutorial](../flowise-llm-orchestration/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LangGraph Tutorial](../langgraph-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: Platform 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 2: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 2: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 2: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 2: Platform Architecture
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `Flow`, `flowchart`, `Builder` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Platform Architecture` as an operating subsystem inside **Langflow Tutorial: Visual AI Agent and Workflow Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Graph`, `Engine`, `Model` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Platform Architecture` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `Flow`.
+2. **Input normalization**: shape incoming data so `flowchart` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Builder`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Langflow Repository](https://github.com/langflow-ai/langflow)
+  Why it matters: authoritative reference on `Langflow Repository` (github.com).
+- [Langflow Releases](https://github.com/langflow-ai/langflow/releases)
+  Why it matters: authoritative reference on `Langflow Releases` (github.com).
+- [Langflow Docs](https://docs.langflow.org/)
+  Why it matters: authoritative reference on `Langflow Docs` (docs.langflow.org).
+- [Langflow Deployment Docs](https://docs.langflow.org/deployment-overview)
+  Why it matters: authoritative reference on `Langflow Deployment Docs` (docs.langflow.org).
+
+Suggested trace strategy:
+- search upstream code for `Flow` and `flowchart` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Visual Flow Builder](03-visual-flow-builder.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langflow-tutorial/03-visual-flow-builder.md b/tutorials/langflow-tutorial/03-visual-flow-builder.md
index 205ca9c1..b11c0f37 100644
--- a/tutorials/langflow-tutorial/03-visual-flow-builder.md
+++ b/tutorials/langflow-tutorial/03-visual-flow-builder.md
@@ -7,6 +7,9 @@ parent: Langflow Tutorial
 
 # Chapter 3: Visual Flow Builder
 
+Welcome to **Chapter 3: Visual Flow Builder**. In this part of **Langflow Tutorial: Visual AI Agent and Workflow Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Visual composition is Langflow's primary productivity surface. Good graph discipline prevents long-term maintenance pain.
 
 ## Builder Practices
@@ -33,3 +36,607 @@ Visual composition is Langflow's primary productivity surface. Good graph discip
 You now have practical rules for building maintainable visual flow graphs.
 
 Next: [Chapter 4: Agent Workflows and Orchestration](04-agent-workflows-and-orchestration.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- tutorial slug: **langflow-tutorial**
+- chapter focus: **Chapter 3: Visual Flow Builder**
+- system context: **Langflow Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Visual Flow 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
+
+- [Langflow Repository](https://github.com/langflow-ai/langflow)
+- [Langflow Releases](https://github.com/langflow-ai/langflow/releases)
+- [Langflow Docs](https://docs.langflow.org/)
+- [Langflow Deployment Docs](https://docs.langflow.org/deployment-overview)
+
+### Cross-Tutorial Connection Map
+
+- [Flowise Tutorial](../flowise-llm-orchestration/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LangGraph Tutorial](../langgraph-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: Visual Flow 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 3: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 3: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 3: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 3: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 3: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 3: Visual Flow Builder
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Visual Flow Builder` as an operating subsystem inside **Langflow Tutorial: Visual AI Agent and Workflow Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Visual Flow Builder` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Langflow Repository](https://github.com/langflow-ai/langflow)
+  Why it matters: authoritative reference on `Langflow Repository` (github.com).
+- [Langflow Releases](https://github.com/langflow-ai/langflow/releases)
+  Why it matters: authoritative reference on `Langflow Releases` (github.com).
+- [Langflow Docs](https://docs.langflow.org/)
+  Why it matters: authoritative reference on `Langflow Docs` (docs.langflow.org).
+- [Langflow Deployment Docs](https://docs.langflow.org/deployment-overview)
+  Why it matters: authoritative reference on `Langflow Deployment Docs` (docs.langflow.org).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Platform Architecture](02-platform-architecture.md)
+- [Next Chapter: Chapter 4: Agent Workflows and Orchestration](04-agent-workflows-and-orchestration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langflow-tutorial/04-agent-workflows-and-orchestration.md b/tutorials/langflow-tutorial/04-agent-workflows-and-orchestration.md
index b9f242a0..96931a12 100644
--- a/tutorials/langflow-tutorial/04-agent-workflows-and-orchestration.md
+++ b/tutorials/langflow-tutorial/04-agent-workflows-and-orchestration.md
@@ -7,6 +7,9 @@ parent: Langflow Tutorial
 
 # Chapter 4: Agent Workflows and Orchestration
 
+Welcome to **Chapter 4: Agent Workflows and Orchestration**. In this part of **Langflow Tutorial: Visual AI Agent and Workflow Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Langflow supports multi-step orchestration patterns for agentic systems that require tool usage, branching, and retrieval.
 
 ## Orchestration Model
@@ -31,3 +34,607 @@ Langflow supports multi-step orchestration patterns for agentic systems that req
 You now know how to structure robust Langflow orchestration beyond simple demo chains.
 
 Next: [Chapter 5: API and MCP Deployment](05-api-and-mcp-deployment.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- tutorial slug: **langflow-tutorial**
+- chapter focus: **Chapter 4: Agent Workflows and Orchestration**
+- system context: **Langflow Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Agent Workflows and Orchestration`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Langflow Repository](https://github.com/langflow-ai/langflow)
+- [Langflow Releases](https://github.com/langflow-ai/langflow/releases)
+- [Langflow Docs](https://docs.langflow.org/)
+- [Langflow Deployment Docs](https://docs.langflow.org/deployment-overview)
+
+### Cross-Tutorial Connection Map
+
+- [Flowise Tutorial](../flowise-llm-orchestration/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LangGraph Tutorial](../langgraph-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: Agent Workflows and Orchestration`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 4: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 4: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 4: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 4: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 4: Agent Workflows and Orchestration
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Agent Workflows and Orchestration` as an operating subsystem inside **Langflow Tutorial: Visual AI Agent and Workflow Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Agent Workflows and Orchestration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Langflow Repository](https://github.com/langflow-ai/langflow)
+  Why it matters: authoritative reference on `Langflow Repository` (github.com).
+- [Langflow Releases](https://github.com/langflow-ai/langflow/releases)
+  Why it matters: authoritative reference on `Langflow Releases` (github.com).
+- [Langflow Docs](https://docs.langflow.org/)
+  Why it matters: authoritative reference on `Langflow Docs` (docs.langflow.org).
+- [Langflow Deployment Docs](https://docs.langflow.org/deployment-overview)
+  Why it matters: authoritative reference on `Langflow Deployment Docs` (docs.langflow.org).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Visual Flow Builder](03-visual-flow-builder.md)
+- [Next Chapter: Chapter 5: API and MCP Deployment](05-api-and-mcp-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langflow-tutorial/05-api-and-mcp-deployment.md b/tutorials/langflow-tutorial/05-api-and-mcp-deployment.md
index b9f31975..965593b0 100644
--- a/tutorials/langflow-tutorial/05-api-and-mcp-deployment.md
+++ b/tutorials/langflow-tutorial/05-api-and-mcp-deployment.md
@@ -7,6 +7,9 @@ parent: Langflow Tutorial
 
 # Chapter 5: API and MCP Deployment
 
+Welcome to **Chapter 5: API and MCP Deployment**. In this part of **Langflow Tutorial: Visual AI Agent and Workflow Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Langflow can expose workflows as APIs and MCP tools, making flows reusable across applications and agents.
 
 ## Deployment Surfaces
@@ -33,3 +36,607 @@ Langflow can expose workflows as APIs and MCP tools, making flows reusable acros
 You now have a practical approach for publishing Langflow workflows as reusable runtime interfaces.
 
 Next: [Chapter 6: Observability and Security](06-observability-and-security.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- tutorial slug: **langflow-tutorial**
+- chapter focus: **Chapter 5: API and MCP Deployment**
+- system context: **Langflow Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: API and MCP 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
+
+- [Langflow Repository](https://github.com/langflow-ai/langflow)
+- [Langflow Releases](https://github.com/langflow-ai/langflow/releases)
+- [Langflow Docs](https://docs.langflow.org/)
+- [Langflow Deployment Docs](https://docs.langflow.org/deployment-overview)
+
+### Cross-Tutorial Connection Map
+
+- [Flowise Tutorial](../flowise-llm-orchestration/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LangGraph Tutorial](../langgraph-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: API and MCP Deployment`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 5: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 5: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 5: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 5: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 5: API and MCP Deployment
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: API and MCP Deployment` as an operating subsystem inside **Langflow Tutorial: Visual AI Agent and Workflow Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: API and MCP Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Langflow Repository](https://github.com/langflow-ai/langflow)
+  Why it matters: authoritative reference on `Langflow Repository` (github.com).
+- [Langflow Releases](https://github.com/langflow-ai/langflow/releases)
+  Why it matters: authoritative reference on `Langflow Releases` (github.com).
+- [Langflow Docs](https://docs.langflow.org/)
+  Why it matters: authoritative reference on `Langflow Docs` (docs.langflow.org).
+- [Langflow Deployment Docs](https://docs.langflow.org/deployment-overview)
+  Why it matters: authoritative reference on `Langflow Deployment Docs` (docs.langflow.org).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Agent Workflows and Orchestration](04-agent-workflows-and-orchestration.md)
+- [Next Chapter: Chapter 6: Observability and Security](06-observability-and-security.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langflow-tutorial/06-observability-and-security.md b/tutorials/langflow-tutorial/06-observability-and-security.md
index c5593f0b..88d5ef95 100644
--- a/tutorials/langflow-tutorial/06-observability-and-security.md
+++ b/tutorials/langflow-tutorial/06-observability-and-security.md
@@ -7,6 +7,9 @@ parent: Langflow Tutorial
 
 # Chapter 6: Observability and Security
 
+Welcome to **Chapter 6: Observability and Security**. In this part of **Langflow Tutorial: Visual AI Agent and Workflow Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Langflow production usage requires strong observability and strict security boundaries.
 
 ## Security Baseline
@@ -35,3 +38,595 @@ Langflow production usage requires strong observability and strict security boun
 You now have a security and telemetry baseline for operating Langflow safely.
 
 Next: [Chapter 7: Custom Components and Extensions](07-custom-components-and-extensions.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- tutorial slug: **langflow-tutorial**
+- chapter focus: **Chapter 6: Observability and Security**
+- system context: **Langflow Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Observability and Security`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Langflow Repository](https://github.com/langflow-ai/langflow)
+- [Langflow Releases](https://github.com/langflow-ai/langflow/releases)
+- [Langflow Docs](https://docs.langflow.org/)
+- [Langflow Deployment Docs](https://docs.langflow.org/deployment-overview)
+
+### Cross-Tutorial Connection Map
+
+- [Flowise Tutorial](../flowise-llm-orchestration/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LangGraph Tutorial](../langgraph-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: Observability and Security`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 15: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 16: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 17: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 18: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 19: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 20: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 21: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 22: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 23: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 24: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 25: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 26: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 27: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 28: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 29: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 30: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 31: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 32: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 33: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 6: Observability and Security
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Observability and Security` as an operating subsystem inside **Langflow Tutorial: Visual AI Agent and Workflow Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Observability and Security` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Langflow Repository](https://github.com/langflow-ai/langflow)
+  Why it matters: authoritative reference on `Langflow Repository` (github.com).
+- [Langflow Releases](https://github.com/langflow-ai/langflow/releases)
+  Why it matters: authoritative reference on `Langflow Releases` (github.com).
+- [Langflow Docs](https://docs.langflow.org/)
+  Why it matters: authoritative reference on `Langflow Docs` (docs.langflow.org).
+- [Langflow Deployment Docs](https://docs.langflow.org/deployment-overview)
+  Why it matters: authoritative reference on `Langflow Deployment Docs` (docs.langflow.org).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: API and MCP Deployment](05-api-and-mcp-deployment.md)
+- [Next Chapter: Chapter 7: Custom Components and Extensions](07-custom-components-and-extensions.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langflow-tutorial/07-custom-components-and-extensions.md b/tutorials/langflow-tutorial/07-custom-components-and-extensions.md
index 3b07a82f..13de5e5c 100644
--- a/tutorials/langflow-tutorial/07-custom-components-and-extensions.md
+++ b/tutorials/langflow-tutorial/07-custom-components-and-extensions.md
@@ -7,6 +7,9 @@ parent: Langflow Tutorial
 
 # Chapter 7: Custom Components and Extensions
 
+Welcome to **Chapter 7: Custom Components and Extensions**. In this part of **Langflow Tutorial: Visual AI Agent and Workflow Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Langflow supports code-level extensibility so teams can encapsulate domain logic as reusable components.
 
 ## Extension Pattern
@@ -34,3 +37,595 @@ Langflow supports code-level extensibility so teams can encapsulate domain logic
 You now know how to extend Langflow without compromising maintainability.
 
 Next: [Chapter 8: Production Operations](08-production-operations.md)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- tutorial slug: **langflow-tutorial**
+- chapter focus: **Chapter 7: Custom Components and Extensions**
+- system context: **Langflow Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Custom Components 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
+
+- [Langflow Repository](https://github.com/langflow-ai/langflow)
+- [Langflow Releases](https://github.com/langflow-ai/langflow/releases)
+- [Langflow Docs](https://docs.langflow.org/)
+- [Langflow Deployment Docs](https://docs.langflow.org/deployment-overview)
+
+### Cross-Tutorial Connection Map
+
+- [Flowise Tutorial](../flowise-llm-orchestration/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LangGraph Tutorial](../langgraph-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: Custom Components 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 7: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 7: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 7: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 7: Custom Components and Extensions
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Custom Components and Extensions` as an operating subsystem inside **Langflow Tutorial: Visual AI Agent and Workflow Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Custom Components and Extensions` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Langflow Repository](https://github.com/langflow-ai/langflow)
+  Why it matters: authoritative reference on `Langflow Repository` (github.com).
+- [Langflow Releases](https://github.com/langflow-ai/langflow/releases)
+  Why it matters: authoritative reference on `Langflow Releases` (github.com).
+- [Langflow Docs](https://docs.langflow.org/)
+  Why it matters: authoritative reference on `Langflow Docs` (docs.langflow.org).
+- [Langflow Deployment Docs](https://docs.langflow.org/deployment-overview)
+  Why it matters: authoritative reference on `Langflow Deployment Docs` (docs.langflow.org).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Observability and Security](06-observability-and-security.md)
+- [Next Chapter: Chapter 8: Production Operations](08-production-operations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langflow-tutorial/08-production-operations.md b/tutorials/langflow-tutorial/08-production-operations.md
index d9e619bd..90e26450 100644
--- a/tutorials/langflow-tutorial/08-production-operations.md
+++ b/tutorials/langflow-tutorial/08-production-operations.md
@@ -7,6 +7,9 @@ parent: Langflow Tutorial
 
 # Chapter 8: Production Operations
 
+Welcome to **Chapter 8: Production Operations**. In this part of **Langflow Tutorial: Visual AI Agent and Workflow Platform**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 This chapter turns Langflow from a builder experience into a production platform practice.
 
 ## Operations Checklist
@@ -33,3 +36,606 @@ This chapter turns Langflow from a builder experience into a production platform
 ## Summary
 
 You now have an operational baseline for running Langflow at production scale.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- tutorial slug: **langflow-tutorial**
+- chapter focus: **Chapter 8: Production Operations**
+- system context: **Langflow 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
+
+- [Langflow Repository](https://github.com/langflow-ai/langflow)
+- [Langflow Releases](https://github.com/langflow-ai/langflow/releases)
+- [Langflow Docs](https://docs.langflow.org/)
+- [Langflow Deployment Docs](https://docs.langflow.org/deployment-overview)
+
+### Cross-Tutorial Connection Map
+
+- [Flowise Tutorial](../flowise-llm-orchestration/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LangGraph Tutorial](../langgraph-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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 34: Chapter 8: Production Operations
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 35: Chapter 8: Production Operations
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 36: Chapter 8: Production Operations
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 37: Chapter 8: Production Operations
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 38: Chapter 8: Production Operations
+
+- tutorial context: **Langflow Tutorial: Visual AI Agent and Workflow Platform**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for core abstractions in this chapter so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Operations` as an operating subsystem inside **Langflow Tutorial: Visual AI Agent and Workflow Platform**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around execution and reliability details as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Operations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `core component`.
+2. **Input normalization**: shape incoming data so `execution layer` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `state model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Langflow Repository](https://github.com/langflow-ai/langflow)
+  Why it matters: authoritative reference on `Langflow Repository` (github.com).
+- [Langflow Releases](https://github.com/langflow-ai/langflow/releases)
+  Why it matters: authoritative reference on `Langflow Releases` (github.com).
+- [Langflow Docs](https://docs.langflow.org/)
+  Why it matters: authoritative reference on `Langflow Docs` (docs.langflow.org).
+- [Langflow Deployment Docs](https://docs.langflow.org/deployment-overview)
+  Why it matters: authoritative reference on `Langflow Deployment Docs` (docs.langflow.org).
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Custom Components and Extensions](07-custom-components-and-extensions.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langfuse-tutorial/01-getting-started.md b/tutorials/langfuse-tutorial/01-getting-started.md
index 51af9da5..b4dc4da5 100644
--- a/tutorials/langfuse-tutorial/01-getting-started.md
+++ b/tutorials/langfuse-tutorial/01-getting-started.md
@@ -8,6 +8,9 @@ parent: Langfuse Tutorial
 
 # Chapter 1: Getting Started with Langfuse
 
+Welcome to **Chapter 1: Getting Started with Langfuse**. In this part of **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Install Langfuse, connect your first app, and capture the first trace.
 
 ## Overview
@@ -315,3 +318,319 @@ Under **Settings** you manage API keys, team members, project configuration, and
 ---
 
 | [Previous: Tutorial Overview](./index.md) | [Next: Chapter 2 -- Tracing Fundamentals](./02-tracing.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: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- tutorial slug: **langfuse-tutorial**
+- chapter focus: **Chapter 1: Getting Started with Langfuse**
+- system context: **Langfuse Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started with Langfuse`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Langfuse Repository](https://github.com/langfuse/langfuse)
+- [Langfuse Releases](https://github.com/langfuse/langfuse/releases)
+- [Langfuse Docs](https://langfuse.com/docs)
+
+### Cross-Tutorial Connection Map
+
+- [LiteLLM Tutorial](../litellm-tutorial/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LlamaIndex Tutorial](../llamaindex-tutorial/)
+- [Vercel AI SDK Tutorial](../vercel-ai-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started with Langfuse`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started with Langfuse
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started with Langfuse
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started with Langfuse
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started with Langfuse
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started with Langfuse
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started with Langfuse
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started with Langfuse
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started with Langfuse
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started with Langfuse
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started with Langfuse
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started with Langfuse
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started with Langfuse
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 13: Chapter 1: Getting Started with Langfuse
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 14: Chapter 1: Getting Started with Langfuse
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `langfuse`, `Langfuse`, `trace` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Langfuse` as an operating subsystem inside **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `span`, `style`, `fill` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Langfuse` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `langfuse`.
+2. **Input normalization**: shape incoming data so `Langfuse` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `trace`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Langfuse Repository](https://github.com/langfuse/langfuse)
+  Why it matters: authoritative reference on `Langfuse Repository` (github.com).
+- [Langfuse Releases](https://github.com/langfuse/langfuse/releases)
+  Why it matters: authoritative reference on `Langfuse Releases` (github.com).
+- [Langfuse Docs](https://langfuse.com/docs)
+  Why it matters: authoritative reference on `Langfuse Docs` (langfuse.com).
+
+Suggested trace strategy:
+- search upstream code for `langfuse` and `Langfuse` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Tracing Fundamentals](02-tracing.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langfuse-tutorial/02-tracing.md b/tutorials/langfuse-tutorial/02-tracing.md
index c480ed81..81df42dc 100644
--- a/tutorials/langfuse-tutorial/02-tracing.md
+++ b/tutorials/langfuse-tutorial/02-tracing.md
@@ -8,6 +8,9 @@ parent: Langfuse Tutorial
 
 # Chapter 2: Tracing Fundamentals
 
+Welcome to **Chapter 2: Tracing Fundamentals**. In this part of **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Capture traces, spans, and events with rich metadata to debug LLM flows.
 
 ## Overview
@@ -519,3 +522,152 @@ In the Langfuse UI this trace will display five nested spans (`rag_pipeline` > `
 ---
 
 | [Previous: Chapter 1 -- Getting Started](./01-getting-started.md) | [Next: Chapter 3 -- Prompt Management](./03-prompts.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: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- tutorial slug: **langfuse-tutorial**
+- chapter focus: **Chapter 2: Tracing Fundamentals**
+- system context: **Langfuse Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Tracing Fundamentals`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Langfuse Repository](https://github.com/langfuse/langfuse)
+- [Langfuse Releases](https://github.com/langfuse/langfuse/releases)
+- [Langfuse Docs](https://langfuse.com/docs)
+
+### Cross-Tutorial Connection Map
+
+- [LiteLLM Tutorial](../litellm-tutorial/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LlamaIndex Tutorial](../llamaindex-tutorial/)
+- [Vercel AI SDK Tutorial](../vercel-ai-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Tracing Fundamentals`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `question`, `resp`, `observe` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Tracing Fundamentals` as an operating subsystem inside **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `trace`, `name`, `content` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Tracing Fundamentals` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `question`.
+2. **Input normalization**: shape incoming data so `resp` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `observe`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Langfuse Repository](https://github.com/langfuse/langfuse)
+  Why it matters: authoritative reference on `Langfuse Repository` (github.com).
+- [Langfuse Releases](https://github.com/langfuse/langfuse/releases)
+  Why it matters: authoritative reference on `Langfuse Releases` (github.com).
+- [Langfuse Docs](https://langfuse.com/docs)
+  Why it matters: authoritative reference on `Langfuse Docs` (langfuse.com).
+
+Suggested trace strategy:
+- search upstream code for `question` and `resp` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Langfuse](01-getting-started.md)
+- [Next Chapter: Chapter 3: Prompt Management](03-prompts.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langfuse-tutorial/03-prompts.md b/tutorials/langfuse-tutorial/03-prompts.md
index debf33d8..c0ced93a 100644
--- a/tutorials/langfuse-tutorial/03-prompts.md
+++ b/tutorials/langfuse-tutorial/03-prompts.md
@@ -8,6 +8,9 @@ parent: Langfuse Tutorial
 
 # Chapter 3: Prompt Management
 
+Welcome to **Chapter 3: Prompt Management**. In this part of **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Version, release, and A/B test prompts directly from Langfuse.
 
 ## Overview
@@ -463,3 +466,176 @@ These tips will help you get the most out of Langfuse prompt management:
 ---
 
 | [Previous: Chapter 2 -- Tracing Fundamentals](./02-tracing.md) | [Next: Chapter 4 -- Evaluation](./04-evaluation.md) |
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- tutorial slug: **langfuse-tutorial**
+- chapter focus: **Chapter 3: Prompt Management**
+- system context: **Langfuse Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Prompt Management`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Langfuse Repository](https://github.com/langfuse/langfuse)
+- [Langfuse Releases](https://github.com/langfuse/langfuse/releases)
+- [Langfuse Docs](https://langfuse.com/docs)
+
+### Cross-Tutorial Connection Map
+
+- [LiteLLM Tutorial](../litellm-tutorial/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LlamaIndex Tutorial](../llamaindex-tutorial/)
+- [Vercel AI SDK Tutorial](../vercel-ai-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Prompt Management`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How 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: Prompt Management
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 3: Prompt Management
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `prompt`, `langfuse`, `label` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Prompt Management` as an operating subsystem inside **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `name`, `production`, `messages` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Prompt Management` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `prompt`.
+2. **Input normalization**: shape incoming data so `langfuse` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `label`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Langfuse Repository](https://github.com/langfuse/langfuse)
+  Why it matters: authoritative reference on `Langfuse Repository` (github.com).
+- [Langfuse Releases](https://github.com/langfuse/langfuse/releases)
+  Why it matters: authoritative reference on `Langfuse Releases` (github.com).
+- [Langfuse Docs](https://langfuse.com/docs)
+  Why it matters: authoritative reference on `Langfuse Docs` (langfuse.com).
+
+Suggested trace strategy:
+- search upstream code for `prompt` and `langfuse` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Tracing Fundamentals](02-tracing.md)
+- [Next Chapter: Chapter 4: Evaluation](04-evaluation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langfuse-tutorial/04-evaluation.md b/tutorials/langfuse-tutorial/04-evaluation.md
index e41737b6..9be05cb8 100644
--- a/tutorials/langfuse-tutorial/04-evaluation.md
+++ b/tutorials/langfuse-tutorial/04-evaluation.md
@@ -8,6 +8,9 @@ parent: Langfuse Tutorial
 
 # Chapter 4: Evaluation
 
+Welcome to **Chapter 4: Evaluation**. In this part of **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Use LLM judges and human feedback to measure and improve output quality.
 
 ## Overview
@@ -668,3 +671,53 @@ Here is a reference of common metrics and when to use them:
 ---
 
 | [Previous: Chapter 3 -- Prompt Management](./03-prompts.md) | [Next: Analytics and Cost Tracking](./05-analytics.md) |
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `answer`, `content`, `question` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Evaluation` as an operating subsystem inside **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `langfuse`, `score`, `trace_id` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Evaluation` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `answer`.
+2. **Input normalization**: shape incoming data so `content` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `question`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Langfuse Repository](https://github.com/langfuse/langfuse)
+  Why it matters: authoritative reference on `Langfuse Repository` (github.com).
+- [Langfuse Releases](https://github.com/langfuse/langfuse/releases)
+  Why it matters: authoritative reference on `Langfuse Releases` (github.com).
+- [Langfuse Docs](https://langfuse.com/docs)
+  Why it matters: authoritative reference on `Langfuse Docs` (langfuse.com).
+
+Suggested trace strategy:
+- search upstream code for `answer` and `content` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Prompt Management](03-prompts.md)
+- [Next Chapter: Chapter 5: Analytics & Metrics](05-analytics.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langfuse-tutorial/05-analytics.md b/tutorials/langfuse-tutorial/05-analytics.md
index 3b737d31..12f119df 100644
--- a/tutorials/langfuse-tutorial/05-analytics.md
+++ b/tutorials/langfuse-tutorial/05-analytics.md
@@ -8,6 +8,9 @@ parent: Langfuse Tutorial
 
 # Chapter 5: Analytics & Metrics
 
+Welcome to **Chapter 5: Analytics & Metrics**. In this part of **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Track costs, latency, usage patterns, and ROI of your LLM applications.
 
 Previous: [Chapter 4: Evaluation](04-evaluation.md)
@@ -518,3 +521,152 @@ with open("langfuse_export.json", "w") as f:
 Analytics and metrics are not just nice-to-have -- they are essential for running an LLM application responsibly. In this chapter, you learned how to track costs across models, analyze latency with percentiles, build custom dashboards, implement cost optimization strategies, calculate ROI with real formulas, and set up alerts to catch problems early. With these tools in place, you can confidently scale your application while keeping costs under control.
 
 Next: [Chapter 6: Datasets & Testing](06-datasets.md) -- create test datasets from production traces and run regression tests.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- tutorial slug: **langfuse-tutorial**
+- chapter focus: **Chapter 5: Analytics & Metrics**
+- system context: **Langfuse Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Analytics & Metrics`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Langfuse Repository](https://github.com/langfuse/langfuse)
+- [Langfuse Releases](https://github.com/langfuse/langfuse/releases)
+- [Langfuse Docs](https://langfuse.com/docs)
+
+### Cross-Tutorial Connection Map
+
+- [LiteLLM Tutorial](../litellm-tutorial/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LlamaIndex Tutorial](../llamaindex-tutorial/)
+- [Vercel AI SDK Tutorial](../vercel-ai-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Analytics & Metrics`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `traces`, `trace`, `langfuse` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Analytics & Metrics` as an operating subsystem inside **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `total_cost`, `cost`, `print` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Analytics & Metrics` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `traces`.
+2. **Input normalization**: shape incoming data so `trace` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `langfuse`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Langfuse Repository](https://github.com/langfuse/langfuse)
+  Why it matters: authoritative reference on `Langfuse Repository` (github.com).
+- [Langfuse Releases](https://github.com/langfuse/langfuse/releases)
+  Why it matters: authoritative reference on `Langfuse Releases` (github.com).
+- [Langfuse Docs](https://langfuse.com/docs)
+  Why it matters: authoritative reference on `Langfuse Docs` (langfuse.com).
+
+Suggested trace strategy:
+- search upstream code for `traces` and `trace` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Evaluation](04-evaluation.md)
+- [Next Chapter: Chapter 6: Datasets & Testing](06-datasets.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langfuse-tutorial/06-datasets.md b/tutorials/langfuse-tutorial/06-datasets.md
index f0ec6807..bf0d5ae8 100644
--- a/tutorials/langfuse-tutorial/06-datasets.md
+++ b/tutorials/langfuse-tutorial/06-datasets.md
@@ -8,6 +8,9 @@ parent: Langfuse Tutorial
 
 # Chapter 6: Datasets & Testing
 
+Welcome to **Chapter 6: Datasets & Testing**. In this part of **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Create test suites from production traces and run regression tests.
 
 Previous: [Chapter 5: Analytics & Metrics](05-analytics.md)
@@ -509,3 +512,152 @@ A good rule of thumb: if a bug could appear in production and your dataset would
 Datasets and testing are the foundation of reliable LLM development. In this chapter, you learned how to create datasets from production traces, design them with the right principles, run tests and interpret results, set up CI/CD regression testing, manage dataset versions, and compare models systematically. With a solid test suite in place, you can iterate on your prompts and models with confidence.
 
 Next: [Chapter 7: Integrations](07-integrations.md) -- connect Langfuse with LangChain, LlamaIndex, OpenAI SDK, and other frameworks.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- tutorial slug: **langfuse-tutorial**
+- chapter focus: **Chapter 6: Datasets & Testing**
+- system context: **Langfuse Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Datasets & Testing`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [Langfuse Repository](https://github.com/langfuse/langfuse)
+- [Langfuse Releases](https://github.com/langfuse/langfuse/releases)
+- [Langfuse Docs](https://langfuse.com/docs)
+
+### Cross-Tutorial Connection Map
+
+- [LiteLLM Tutorial](../litellm-tutorial/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LlamaIndex Tutorial](../llamaindex-tutorial/)
+- [Vercel AI SDK Tutorial](../vercel-ai-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Datasets & Testing`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `scores`, `langfuse`, `results` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Datasets & Testing` as an operating subsystem inside **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `item`, `dataset`, `trace` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Datasets & Testing` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `scores`.
+2. **Input normalization**: shape incoming data so `langfuse` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `results`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Langfuse Repository](https://github.com/langfuse/langfuse)
+  Why it matters: authoritative reference on `Langfuse Repository` (github.com).
+- [Langfuse Releases](https://github.com/langfuse/langfuse/releases)
+  Why it matters: authoritative reference on `Langfuse Releases` (github.com).
+- [Langfuse Docs](https://langfuse.com/docs)
+  Why it matters: authoritative reference on `Langfuse Docs` (langfuse.com).
+
+Suggested trace strategy:
+- search upstream code for `scores` and `langfuse` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Analytics & Metrics](05-analytics.md)
+- [Next Chapter: Chapter 7: Integrations](07-integrations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langfuse-tutorial/07-integrations.md b/tutorials/langfuse-tutorial/07-integrations.md
index 1dfef38e..5722e47f 100644
--- a/tutorials/langfuse-tutorial/07-integrations.md
+++ b/tutorials/langfuse-tutorial/07-integrations.md
@@ -8,6 +8,9 @@ parent: Langfuse Tutorial
 
 # Chapter 7: Integrations
 
+Welcome to **Chapter 7: Integrations**. In this part of **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Connect Langfuse with LangChain, LlamaIndex, OpenAI SDK, and other frameworks.
 
 Previous: [Chapter 6: Datasets & Testing](06-datasets.md)
@@ -647,3 +650,53 @@ langfuse = Langfuse(
 Langfuse integrates with the most popular LLM frameworks through callback handlers, SDK wrappers, and a flexible manual tracing API. In this chapter, you learned how to set up integrations for LangChain, LlamaIndex, OpenAI, Anthropic, Instructor, Haystack, Vercel AI SDK, and CrewAI. You also learned how to build custom integrations, combine multiple frameworks in a single trace, and troubleshoot common issues. With these integrations in place, you get full observability across your entire LLM stack.
 
 Next: [Chapter 8: Production Deployment](08-production.md) -- self-host Langfuse, secure your setup, and scale for high-traffic applications.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `langfuse`, `Langfuse`, `trace` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Integrations` as an operating subsystem inside **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `model`, `response`, `mini` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Integrations` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `langfuse`.
+2. **Input normalization**: shape incoming data so `Langfuse` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `trace`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Langfuse Repository](https://github.com/langfuse/langfuse)
+  Why it matters: authoritative reference on `Langfuse Repository` (github.com).
+- [Langfuse Releases](https://github.com/langfuse/langfuse/releases)
+  Why it matters: authoritative reference on `Langfuse Releases` (github.com).
+- [Langfuse Docs](https://langfuse.com/docs)
+  Why it matters: authoritative reference on `Langfuse Docs` (langfuse.com).
+
+Suggested trace strategy:
+- search upstream code for `langfuse` and `Langfuse` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Datasets & Testing](06-datasets.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langfuse-tutorial/08-production.md b/tutorials/langfuse-tutorial/08-production.md
index 54f93e98..821d10cc 100644
--- a/tutorials/langfuse-tutorial/08-production.md
+++ b/tutorials/langfuse-tutorial/08-production.md
@@ -8,6 +8,9 @@ parent: Langfuse Tutorial
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Self-host Langfuse, secure your setup, and scale for high-traffic applications.
 
 Previous: [Chapter 7: Integrations](07-integrations.md)
@@ -411,3 +414,223 @@ Congratulations -- you have completed the Langfuse tutorial series! Over eight c
 - **Chapter 8**: Production deployment -- self-hosting, security, scaling, and monitoring.
 
 With these tools and practices in place, you are well-equipped to build, monitor, and continuously improve LLM applications at any scale. The key is to start simple, measure everything, and iterate based on real data. Happy building!
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- tutorial slug: **langfuse-tutorial**
+- chapter focus: **Chapter 8: Production Deployment**
+- system context: **Langfuse 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
+
+- [Langfuse Repository](https://github.com/langfuse/langfuse)
+- [Langfuse Releases](https://github.com/langfuse/langfuse/releases)
+- [Langfuse Docs](https://langfuse.com/docs)
+
+### Cross-Tutorial Connection Map
+
+- [LiteLLM Tutorial](../litellm-tutorial/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LlamaIndex Tutorial](../llamaindex-tutorial/)
+- [Vercel AI SDK Tutorial](../vercel-ai-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### 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: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 8: Production Deployment
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 8: Production Deployment
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 8: Production Deployment
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 8: Production Deployment
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 8: Production Deployment
+
+- tutorial context: **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `langfuse`, `redis`, `name` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **Langfuse Tutorial: LLM Observability, Evaluation, and Prompt Operations**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `subgraph`, `image`, `spec` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `langfuse`.
+2. **Input normalization**: shape incoming data so `redis` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `name`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Langfuse Repository](https://github.com/langfuse/langfuse)
+  Why it matters: authoritative reference on `Langfuse Repository` (github.com).
+- [Langfuse Releases](https://github.com/langfuse/langfuse/releases)
+  Why it matters: authoritative reference on `Langfuse Releases` (github.com).
+- [Langfuse Docs](https://langfuse.com/docs)
+  Why it matters: authoritative reference on `Langfuse Docs` (langfuse.com).
+
+Suggested trace strategy:
+- search upstream code for `langfuse` and `redis` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Integrations](07-integrations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langgraph-tutorial/01-getting-started.md b/tutorials/langgraph-tutorial/01-getting-started.md
index fcc6558c..28c1ddfa 100644
--- a/tutorials/langgraph-tutorial/01-getting-started.md
+++ b/tutorials/langgraph-tutorial/01-getting-started.md
@@ -385,3 +385,48 @@ Now that you understand LangGraph basics, let's explore state management in dept
 5. Experiment with different state structures
 
 *What kind of AI application will you build first with LangGraph?* 🤖
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `state`, `graph`, `messages` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with LangGraph` as an operating subsystem inside **LangGraph Tutorial: Building Stateful Multi-Actor Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `content`, `role`, `response` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with LangGraph` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `state`.
+2. **Input normalization**: shape incoming data so `graph` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `messages`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langgraph)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `state` and `graph` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: State Management](02-state-management.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langgraph-tutorial/02-state-management.md b/tutorials/langgraph-tutorial/02-state-management.md
index 8af01734..91ca5d59 100644
--- a/tutorials/langgraph-tutorial/02-state-management.md
+++ b/tutorials/langgraph-tutorial/02-state-management.md
@@ -7,6 +7,9 @@ nav_order: 2
 
 # Chapter 2: State Management
 
+Welcome to **Chapter 2: State Management**. In this part of **LangGraph Tutorial: Building Stateful Multi-Actor Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Master state management in LangGraph! This chapter covers advanced state patterns, persistence strategies, and memory management techniques for building robust AI applications.
 
 ## Advanced State Patterns
@@ -413,3 +416,49 @@ Ready to build complex graphs? In [Chapter 3: Nodes and Edges](03-nodes-edges.md
 5. Build state synchronization for distributed agents
 
 *How will you manage state in your AI applications?* 🧠
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `state`, `Dict` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: State Management` as an operating subsystem inside **LangGraph Tutorial: Building Stateful Multi-Actor Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `memory`, `checkpoint_id`, `List` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: State Management` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `state` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Dict`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langgraph)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `state` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with LangGraph](01-getting-started.md)
+- [Next Chapter: Chapter 3: Nodes and Edges](03-nodes-edges.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langgraph-tutorial/03-nodes-edges.md b/tutorials/langgraph-tutorial/03-nodes-edges.md
index f99f9eb6..f5242aff 100644
--- a/tutorials/langgraph-tutorial/03-nodes-edges.md
+++ b/tutorials/langgraph-tutorial/03-nodes-edges.md
@@ -567,3 +567,49 @@ Ready for conditional logic and decision-making? In [Chapter 4: Conditional Logi
 5. Build a visualization tool for your graphs
 
 *What's the most complex graph structure you'll build?* 🔀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `state`, `graph`, `GraphState` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Nodes and Edges` as an operating subsystem inside **LangGraph Tutorial: Building Stateful Multi-Actor Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `result`, `status`, `add_edge` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Nodes and Edges` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `state`.
+2. **Input normalization**: shape incoming data so `graph` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `GraphState`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langgraph)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `state` and `graph` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: State Management](02-state-management.md)
+- [Next Chapter: Chapter 4: Conditional Logic](04-conditional-logic.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langgraph-tutorial/04-conditional-logic.md b/tutorials/langgraph-tutorial/04-conditional-logic.md
index 8428094f..1d8dd10a 100644
--- a/tutorials/langgraph-tutorial/04-conditional-logic.md
+++ b/tutorials/langgraph-tutorial/04-conditional-logic.md
@@ -800,3 +800,49 @@ Ready to coordinate multiple agents? In [Chapter 5: Multi-Agent Systems](05-mult
 5. Build resilient conditional logic with comprehensive error handling
 
 *What's the most sophisticated decision system you'll build?* 🤔
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `state`, `graph`, `dict` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Conditional Logic` as an operating subsystem inside **LangGraph Tutorial: Building Stateful Multi-Actor Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `add_node`, `add_edge`, `decision` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Conditional Logic` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `state`.
+2. **Input normalization**: shape incoming data so `graph` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `dict`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langgraph)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `state` and `graph` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Nodes and Edges](03-nodes-edges.md)
+- [Next Chapter: Chapter 5: Multi-Agent Systems](05-multi-agent-systems.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langgraph-tutorial/05-multi-agent-systems.md b/tutorials/langgraph-tutorial/05-multi-agent-systems.md
index a8774dab..55e742c3 100644
--- a/tutorials/langgraph-tutorial/05-multi-agent-systems.md
+++ b/tutorials/langgraph-tutorial/05-multi-agent-systems.md
@@ -678,3 +678,49 @@ Ready to integrate external tools and APIs? In [Chapter 6: Tool Integration](06-
 5. Build a real-time multi-agent collaboration system
 
 *What's the most complex multi-agent system you'll create?* 🤝
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `state`, `self`, `graph` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Multi-Agent Systems` as an operating subsystem inside **LangGraph Tutorial: Building Stateful Multi-Actor Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `task`, `messages`, `agent` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Multi-Agent Systems` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `state`.
+2. **Input normalization**: shape incoming data so `self` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `graph`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langgraph)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `state` and `self` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Conditional Logic](04-conditional-logic.md)
+- [Next Chapter: Chapter 6: Tool Integration](06-tool-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langgraph-tutorial/06-tool-integration.md b/tutorials/langgraph-tutorial/06-tool-integration.md
index 169303c9..0b51ed7e 100644
--- a/tutorials/langgraph-tutorial/06-tool-integration.md
+++ b/tutorials/langgraph-tutorial/06-tool-integration.md
@@ -7,17 +7,24 @@ nav_order: 6
 
 # Chapter 6: Tool Integration
 
+Welcome to **Chapter 6: Tool Integration**. In this part of **LangGraph Tutorial: Building Stateful Multi-Actor Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Welcome back! By now you understand how to build graphs with nodes, edges, and conditional logic. But real-world AI applications need to interact with the world - calling APIs, querying databases, and using external services. This is where **tool integration** comes in!
 
 ## What Problem Does This Solve?
 
-Imagine you're building an AI assistant that needs to:
-- Look up weather information
-- Send emails
-- Query a database
-- Call external APIs
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `state`, `result`, `tool` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
 
-Without tool integration, your AI would be limited to just generating text. Tools give your AI agents the ability to take real actions and gather real information.
+After working through this chapter, you should be able to reason about `Chapter 6: Tool Integration` as an operating subsystem inside **LangGraph Tutorial: Building Stateful Multi-Actor Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `messages`, `tool_results`, `tools` as your checklist when adapting these patterns to your own repository.
 
 ## Understanding Tool Integration in LangGraph
 
@@ -584,3 +591,35 @@ Ready to make your graphs persistent? Let's continue to [Chapter 7: Persistence
 ---
 
 *Generated by [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)*
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Tool Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `state`.
+2. **Input normalization**: shape incoming data so `result` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `tool`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langgraph)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `state` and `result` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Multi-Agent Systems](05-multi-agent-systems.md)
+- [Next Chapter: Chapter 7: Persistence and Checkpoints](07-persistence-checkpoints.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langgraph-tutorial/07-persistence-checkpoints.md b/tutorials/langgraph-tutorial/07-persistence-checkpoints.md
index 3ca5174d..fc8222ab 100644
--- a/tutorials/langgraph-tutorial/07-persistence-checkpoints.md
+++ b/tutorials/langgraph-tutorial/07-persistence-checkpoints.md
@@ -7,6 +7,9 @@ nav_order: 7
 
 # Chapter 7: Persistence and Checkpoints
 
+Welcome to **Chapter 7: Persistence and Checkpoints**. In this part of **LangGraph Tutorial: Building Stateful Multi-Actor Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Welcome back! Your LangGraph applications are getting sophisticated with tool integration, but what happens if your application crashes or gets interrupted? How do you save progress and resume where you left off? That's where **persistence and checkpoints** come in!
 
 ## What Problem Does This Solve?
@@ -754,3 +757,35 @@ Ready to deploy your persistent AI applications? Let's finish strong with [Chapt
 ---
 
 *Generated by [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)*
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Persistence and Checkpoints` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `state`.
+2. **Input normalization**: shape incoming data so `checkpoint` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `graph`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langgraph)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `state` and `checkpoint` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Tool Integration](06-tool-integration.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/langgraph-tutorial/08-production-deployment.md b/tutorials/langgraph-tutorial/08-production-deployment.md
index a80ab681..3cdd64e9 100644
--- a/tutorials/langgraph-tutorial/08-production-deployment.md
+++ b/tutorials/langgraph-tutorial/08-production-deployment.md
@@ -7,18 +7,24 @@ nav_order: 8
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **LangGraph Tutorial: Building Stateful Multi-Actor Applications**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 Congratulations! You've made it to the final chapter of our LangGraph journey. By now, you understand how to build stateful multi-actor applications with graphs, nodes, edges, conditional logic, tool integration, and persistence. Now it's time to take your applications from development to **production deployment**!
 
 ## What Problem Does This Solve?
 
-Building AI applications is only half the battle. The real challenge is **deploying them reliably** at scale. Production deployment involves:
-- **Scaling** your applications to handle thousands of users
-- **Monitoring** performance and catching errors
-- **Ensuring reliability** with proper error handling and recovery
-- **Managing costs** for AI model usage and infrastructure
-- **Maintaining security** and compliance
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `state`, `time`, `request` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
 
-Without proper production practices, even the most sophisticated AI applications can fail under real-world load.
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **LangGraph Tutorial: Building Stateful Multi-Actor Applications**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `user_id`, `langgraph`, `session_id` as your checklist when adapting these patterns to your own repository.
 
 ## Understanding Production Deployment
 
@@ -920,3 +926,34 @@ Remember, the journey doesn't end here. AI development is an exciting, ever-evol
 ---
 
 *Generated by [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)*
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `state`.
+2. **Input normalization**: shape incoming data so `time` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `request`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/langchain-ai/langgraph)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `state` and `time` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Persistence and Checkpoints](07-persistence-checkpoints.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/letta-tutorial/01-getting-started.md b/tutorials/letta-tutorial/01-getting-started.md
index c25f345b..335c0d08 100644
--- a/tutorials/letta-tutorial/01-getting-started.md
+++ b/tutorials/letta-tutorial/01-getting-started.md
@@ -8,6 +8,9 @@ parent: Letta Tutorial
 
 # Chapter 1: Getting Started with Letta
 
+Welcome to **Chapter 1: Getting Started with Letta**. In this part of **Letta Tutorial: Stateful LLM Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Install Letta, create your first agent, and start a conversation with persistent memory.
 
 ## Overview
@@ -162,4 +165,51 @@ Human: What do you know about me?
 Assistant: I know that your name is Alex, you're a data scientist, and you're from New York. Is there anything else you'd like me to know or any questions you have?
 ```
 
-The agent automatically stored and retrieved your personal information! This is the foundation of Letta's persistent memory system. 
\ No newline at end of file
+The agent automatically stored and retrieved your personal information! This is the foundation of Letta's persistent memory system.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `letta`, `name`, `assistant` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with Letta` as an operating subsystem inside **Letta Tutorial: Stateful LLM Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `chat`, `agent`, `create` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with Letta` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `letta`.
+2. **Input normalization**: shape incoming data so `name` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `assistant`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/letta-ai/letta)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `letta` and `name` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Memory Architecture in Letta](02-memory.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/letta-tutorial/02-memory.md b/tutorials/letta-tutorial/02-memory.md
index 1a728b62..9c5bd929 100644
--- a/tutorials/letta-tutorial/02-memory.md
+++ b/tutorials/letta-tutorial/02-memory.md
@@ -8,6 +8,9 @@ parent: Letta Tutorial
 
 # Chapter 2: Memory Architecture in Letta
 
+Welcome to **Chapter 2: Memory Architecture in Letta**. In this part of **Letta Tutorial: Stateful LLM Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Understand core memory, archival memory, and recall memory - the three pillars of persistent agent memory.
 
 ## Overview
@@ -212,4 +215,52 @@ For advanced users, you can create custom memory blocks and retrieval strategies
 | Personalization | Basic | Deeply personalized experiences |
 | Consistency | May contradict itself | Maintains consistent knowledge |
 
-Next: Configure agent personalities and behavior with system prompts and models. 
\ No newline at end of file
+Next: Configure agent personalities and behavior with system prompts and models.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `client`, `memory`, `John` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Memory Architecture in Letta` as an operating subsystem inside **Letta Tutorial: Stateful LLM Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `name`, `add_to_archival_memory`, `letta` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Memory Architecture in Letta` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `client`.
+2. **Input normalization**: shape incoming data so `memory` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `John`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/letta-ai/letta)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `client` and `memory` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with Letta](01-getting-started.md)
+- [Next Chapter: Chapter 3: Agent Configuration](03-configuration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/letta-tutorial/03-configuration.md b/tutorials/letta-tutorial/03-configuration.md
index c59abe55..21bcb41b 100644
--- a/tutorials/letta-tutorial/03-configuration.md
+++ b/tutorials/letta-tutorial/03-configuration.md
@@ -8,6 +8,9 @@ parent: Letta Tutorial
 
 # Chapter 3: Agent Configuration
 
+Welcome to **Chapter 3: Agent Configuration**. In this part of **Letta Tutorial: Stateful LLM Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Customize agent personalities, system prompts, models, and behavior settings.
 
 ## Overview
@@ -287,4 +290,52 @@ def save_config(agent, version="v1"):
 
 This allows you to experiment with different configurations and roll back if needed.
 
-Next: Add custom tools and functions to extend agent capabilities. 
\ No newline at end of file
+Next: Add custom tools and functions to extend agent capabilities.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `name`, `persona`, `agent` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Agent Configuration` as an operating subsystem inside **Letta Tutorial: Stateful LLM Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `model`, `system`, `code` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Agent Configuration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `name`.
+2. **Input normalization**: shape incoming data so `persona` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `agent`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/letta-ai/letta)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `name` and `persona` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Memory Architecture in Letta](02-memory.md)
+- [Next Chapter: Chapter 4: Tool Integration](04-tools.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/letta-tutorial/04-tools.md b/tutorials/letta-tutorial/04-tools.md
index 36575eb8..88c546c6 100644
--- a/tutorials/letta-tutorial/04-tools.md
+++ b/tutorials/letta-tutorial/04-tools.md
@@ -8,6 +8,9 @@ parent: Letta Tutorial
 
 # Chapter 4: Tool Integration
 
+Welcome to **Chapter 4: Tool Integration**. In this part of **Letta Tutorial: Stateful LLM Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Extend agent capabilities with custom tools, functions, and external integrations.
 
 ## Overview
@@ -391,4 +394,52 @@ def monitored_tool_call(tool_name: str, **kwargs):
 6. **Modularity**: Keep tools focused on single responsibilities
 7. **Versioning**: Version tools and handle backward compatibility
 
-Next: Manage long-running conversations and context. 
\ No newline at end of file
+Next: Manage long-running conversations and context.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `client`, `result`, `weather` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Tool Integration` as an operating subsystem inside **Letta Tutorial: Stateful LLM Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `tool`, `description`, `name` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Tool Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `client`.
+2. **Input normalization**: shape incoming data so `result` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `weather`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/letta-ai/letta)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `client` and `result` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Agent Configuration](03-configuration.md)
+- [Next Chapter: Chapter 5: Conversation Management](05-conversations.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/letta-tutorial/05-conversations.md b/tutorials/letta-tutorial/05-conversations.md
index ec410663..0367771e 100644
--- a/tutorials/letta-tutorial/05-conversations.md
+++ b/tutorials/letta-tutorial/05-conversations.md
@@ -8,6 +8,9 @@ parent: Letta Tutorial
 
 # Chapter 5: Conversation Management
 
+Welcome to **Chapter 5: Conversation Management**. In this part of **Letta Tutorial: Stateful LLM Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Handle long-running dialogues, manage conversation state, and implement conversation patterns.
 
 ## Overview
@@ -403,4 +406,52 @@ resume_conversation("sam", "conv_123", context)
 6. **Participant Management**: Track conversation participants and their roles
 7. **Performance Monitoring**: Monitor conversation length, engagement, and outcomes
 
-Next: Coordinate multiple agents working together. 
\ No newline at end of file
+Next: Coordinate multiple agents working together.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `agent_name`, `self`, `conversation` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Conversation Management` as an operating subsystem inside **Letta Tutorial: Stateful LLM Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `client`, `conversation_id`, `message` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Conversation Management` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `agent_name`.
+2. **Input normalization**: shape incoming data so `self` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `conversation`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/letta-ai/letta)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `agent_name` and `self` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Tool Integration](04-tools.md)
+- [Next Chapter: Chapter 6: Multi-Agent Systems](06-multi-agent.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/letta-tutorial/06-multi-agent.md b/tutorials/letta-tutorial/06-multi-agent.md
index 7fccd487..8bd7b8fa 100644
--- a/tutorials/letta-tutorial/06-multi-agent.md
+++ b/tutorials/letta-tutorial/06-multi-agent.md
@@ -8,6 +8,9 @@ parent: Letta Tutorial
 
 # Chapter 6: Multi-Agent Systems
 
+Welcome to **Chapter 6: Multi-Agent Systems**. In this part of **Letta Tutorial: Stateful LLM Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Coordinate multiple agents, implement agent communication, and build collaborative workflows.
 
 ## Overview
@@ -457,4 +460,52 @@ network.broadcast_to_group("content", "New style guide released")
 6. **Memory Sharing**: Enable relevant knowledge transfer between agents
 7. **Fallbacks**: Handle agent failures gracefully
 
-Next: Deploy agents as REST API services. 
\ No newline at end of file
+Next: Deploy agents as REST API services.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `content`, `message` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Multi-Agent Systems` as an operating subsystem inside **Letta Tutorial: Stateful LLM Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `client`, `agent`, `coordinator` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Multi-Agent Systems` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `content` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `message`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/letta-ai/letta)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `content` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Conversation Management](05-conversations.md)
+- [Next Chapter: Chapter 7: REST API](07-api.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/letta-tutorial/07-api.md b/tutorials/letta-tutorial/07-api.md
index 4c162447..9a40f34e 100644
--- a/tutorials/letta-tutorial/07-api.md
+++ b/tutorials/letta-tutorial/07-api.md
@@ -8,6 +8,9 @@ parent: Letta Tutorial
 
 # Chapter 7: REST API
 
+Welcome to **Chapter 7: REST API**. In this part of **Letta Tutorial: Stateful LLM Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploy Letta agents as REST API services for integration with applications.
 
 ## Overview
@@ -463,4 +466,52 @@ if __name__ == '__main__':
     app.run(debug=True)
 ```
 
-Next: Production deployment and scaling strategies. 
\ No newline at end of file
+Next: Production deployment and scaling strategies.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `response`, `json`, `message` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: REST API` as an operating subsystem inside **Letta Tutorial: Stateful LLM Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `agents`, `requests`, `messages` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: REST API` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `response`.
+2. **Input normalization**: shape incoming data so `json` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `message`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/letta-ai/letta)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `response` and `json` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Multi-Agent Systems](06-multi-agent.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/letta-tutorial/08-production.md b/tutorials/letta-tutorial/08-production.md
index d55120f7..e5d77b41 100644
--- a/tutorials/letta-tutorial/08-production.md
+++ b/tutorials/letta-tutorial/08-production.md
@@ -8,6 +8,9 @@ parent: Letta Tutorial
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **Letta Tutorial: Stateful LLM Agents**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploy Letta agents to production with scaling, monitoring, security, and operational best practices.
 
 ## Overview
@@ -635,4 +638,51 @@ Before going live:
 - [ ] Team trained on operations
 - [ ] Support channels established
 
-With this production setup, Letta agents can reliably serve thousands of users while maintaining performance, security, and cost efficiency. 
\ No newline at end of file
+With this production setup, Letta agents can reliably serve thousands of users while maintaining performance, security, and cost efficiency.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `letta`, `name`, `redis` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **Letta Tutorial: Stateful LLM Agents**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `record`, `self`, `logging` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `letta`.
+2. **Input normalization**: shape incoming data so `name` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `redis`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/letta-ai/letta)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `letta` and `name` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: REST API](07-api.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/litellm-tutorial/01-getting-started.md b/tutorials/litellm-tutorial/01-getting-started.md
index 678e2edb..4fe2955b 100644
--- a/tutorials/litellm-tutorial/01-getting-started.md
+++ b/tutorials/litellm-tutorial/01-getting-started.md
@@ -8,6 +8,9 @@ parent: LiteLLM Tutorial
 
 # Chapter 1: Getting Started with LiteLLM
 
+Welcome to **Chapter 1: Getting Started with LiteLLM**. In this part of **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Install LiteLLM, configure your first provider, and make your initial LLM call with a unified interface.
 
 ## Overview
@@ -310,4 +313,320 @@ response = litellm.completion(
 )
 print('Response:', response.choices[0].message.content)
 "
-```
\ No newline at end of file
+```
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- tutorial slug: **litellm-tutorial**
+- chapter focus: **Chapter 1: Getting Started with LiteLLM**
+- system context: **Litellm Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Getting Started with LiteLLM`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [LiteLLM Repository](https://github.com/BerriAI/litellm)
+- [LiteLLM Releases](https://github.com/BerriAI/litellm/releases)
+- [LiteLLM Docs](https://docs.litellm.ai/)
+
+### Cross-Tutorial Connection Map
+
+- [Langfuse Tutorial](../langfuse-tutorial/)
+- [Vercel AI SDK Tutorial](../vercel-ai-tutorial/)
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Getting Started with LiteLLM`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+### Scenario Playbook 1: Chapter 1: Getting Started with LiteLLM
+
+- tutorial context: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 2: Chapter 1: Getting Started with LiteLLM
+
+- tutorial context: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 3: Chapter 1: Getting Started with LiteLLM
+
+- tutorial context: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 4: Chapter 1: Getting Started with LiteLLM
+
+- tutorial context: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 5: Chapter 1: Getting Started with LiteLLM
+
+- tutorial context: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 6: Chapter 1: Getting Started with LiteLLM
+
+- tutorial context: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 7: Chapter 1: Getting Started with LiteLLM
+
+- tutorial context: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 8: Chapter 1: Getting Started with LiteLLM
+
+- tutorial context: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 9: Chapter 1: Getting Started with LiteLLM
+
+- tutorial context: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 10: Chapter 1: Getting Started with LiteLLM
+
+- tutorial context: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 11: Chapter 1: Getting Started with LiteLLM
+
+- tutorial context: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+### Scenario Playbook 12: Chapter 1: Getting Started with LiteLLM
+
+- tutorial context: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with LiteLLM
+
+- tutorial context: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive 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 with LiteLLM
+
+- tutorial context: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `litellm`, `model`, `content` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with LiteLLM` as an operating subsystem inside **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `response`, `turbo`, `completion` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with LiteLLM` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `litellm`.
+2. **Input normalization**: shape incoming data so `model` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `content`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [LiteLLM Repository](https://github.com/BerriAI/litellm)
+  Why it matters: authoritative reference on `LiteLLM Repository` (github.com).
+- [LiteLLM Releases](https://github.com/BerriAI/litellm/releases)
+  Why it matters: authoritative reference on `LiteLLM Releases` (github.com).
+- [LiteLLM Docs](https://docs.litellm.ai/)
+  Why it matters: authoritative reference on `LiteLLM Docs` (docs.litellm.ai).
+
+Suggested trace strategy:
+- search upstream code for `litellm` and `model` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Provider Configuration](02-providers.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/litellm-tutorial/02-providers.md b/tutorials/litellm-tutorial/02-providers.md
index ad9d7935..4801cde6 100644
--- a/tutorials/litellm-tutorial/02-providers.md
+++ b/tutorials/litellm-tutorial/02-providers.md
@@ -8,6 +8,9 @@ parent: LiteLLM Tutorial
 
 # Chapter 2: Provider Configuration
 
+Welcome to **Chapter 2: Provider Configuration**. In this part of **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Set up and manage multiple LLM providers with proper authentication and routing.
 
 ## Overview
@@ -478,4 +481,153 @@ if __name__ == "__main__":
             print(f"❌ {model}: Error - {result['error']}")
 ```
 
-This comprehensive provider configuration gives you the flexibility to use any LLM provider while maintaining a consistent interface and handling provider-specific requirements. 
\ No newline at end of file
+This comprehensive provider configuration gives you the flexibility to use any LLM provider while maintaining a consistent interface and handling provider-specific requirements.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- tutorial slug: **litellm-tutorial**
+- chapter focus: **Chapter 2: Provider Configuration**
+- system context: **Litellm Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Provider Configuration`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [LiteLLM Repository](https://github.com/BerriAI/litellm)
+- [LiteLLM Releases](https://github.com/BerriAI/litellm/releases)
+- [LiteLLM Docs](https://docs.litellm.ai/)
+
+### Cross-Tutorial Connection Map
+
+- [Langfuse Tutorial](../langfuse-tutorial/)
+- [Vercel AI SDK Tutorial](../vercel-ai-tutorial/)
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Provider Configuration`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `model`, `litellm`, `messages` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Provider Configuration` as an operating subsystem inside **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `content`, `response`, `getenv` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Provider Configuration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `model`.
+2. **Input normalization**: shape incoming data so `litellm` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `messages`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [LiteLLM Repository](https://github.com/BerriAI/litellm)
+  Why it matters: authoritative reference on `LiteLLM Repository` (github.com).
+- [LiteLLM Releases](https://github.com/BerriAI/litellm/releases)
+  Why it matters: authoritative reference on `LiteLLM Releases` (github.com).
+- [LiteLLM Docs](https://docs.litellm.ai/)
+  Why it matters: authoritative reference on `LiteLLM Docs` (docs.litellm.ai).
+
+Suggested trace strategy:
+- search upstream code for `model` and `litellm` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with LiteLLM](01-getting-started.md)
+- [Next Chapter: Chapter 3: Completion API](03-completion.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/litellm-tutorial/03-completion.md b/tutorials/litellm-tutorial/03-completion.md
index 00b92a90..4834bd92 100644
--- a/tutorials/litellm-tutorial/03-completion.md
+++ b/tutorials/litellm-tutorial/03-completion.md
@@ -8,6 +8,9 @@ parent: LiteLLM Tutorial
 
 # Chapter 3: Completion API
 
+Welcome to **Chapter 3: Completion API**. In this part of **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master text and chat completions with advanced parameters, formatting, and multi-turn conversations.
 
 ## Overview
@@ -504,4 +507,153 @@ if cost_estimate:
 6. **Cost Monitoring**: Track usage and set budgets
 7. **Prompt Engineering**: Craft clear, specific prompts for better results
 
-The completion API is your primary interface to LLM capabilities. Mastering these patterns will enable you to build sophisticated AI applications across any provider. 
\ No newline at end of file
+The completion API is your primary interface to LLM capabilities. Mastering these patterns will enable you to build sophisticated AI applications across any provider.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- tutorial slug: **litellm-tutorial**
+- chapter focus: **Chapter 3: Completion API**
+- system context: **Litellm Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Completion API`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [LiteLLM Repository](https://github.com/BerriAI/litellm)
+- [LiteLLM Releases](https://github.com/BerriAI/litellm/releases)
+- [LiteLLM Docs](https://docs.litellm.ai/)
+
+### Cross-Tutorial Connection Map
+
+- [Langfuse Tutorial](../langfuse-tutorial/)
+- [Vercel AI SDK Tutorial](../vercel-ai-tutorial/)
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Completion API`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `messages`, `content`, `response` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Completion API` as an operating subsystem inside **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `self`, `role`, `model` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Completion API` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `messages`.
+2. **Input normalization**: shape incoming data so `content` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `response`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [LiteLLM Repository](https://github.com/BerriAI/litellm)
+  Why it matters: authoritative reference on `LiteLLM Repository` (github.com).
+- [LiteLLM Releases](https://github.com/BerriAI/litellm/releases)
+  Why it matters: authoritative reference on `LiteLLM Releases` (github.com).
+- [LiteLLM Docs](https://docs.litellm.ai/)
+  Why it matters: authoritative reference on `LiteLLM Docs` (docs.litellm.ai).
+
+Suggested trace strategy:
+- search upstream code for `messages` and `content` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Provider Configuration](02-providers.md)
+- [Next Chapter: Chapter 4: Streaming & Async](04-streaming.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/litellm-tutorial/04-streaming.md b/tutorials/litellm-tutorial/04-streaming.md
index f1edc833..e8c77e9e 100644
--- a/tutorials/litellm-tutorial/04-streaming.md
+++ b/tutorials/litellm-tutorial/04-streaming.md
@@ -8,6 +8,9 @@ parent: LiteLLM Tutorial
 
 # Chapter 4: Streaming & Async
 
+Welcome to **Chapter 4: Streaming & Async**. In this part of **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Implement real-time streaming responses and asynchronous processing for better user experience and performance.
 
 ## Overview
@@ -482,4 +485,153 @@ Tips for streaming performance:
 6. **Fallback Strategies**: Have non-streaming fallbacks for when streaming fails
 7. **Progress Indication**: Show meaningful progress indicators for long operations
 
-Streaming and async processing enable responsive, scalable AI applications. These patterns work across all LiteLLM-supported providers, giving you consistent real-time capabilities regardless of the underlying model. 
\ No newline at end of file
+Streaming and async processing enable responsive, scalable AI applications. These patterns work across all LiteLLM-supported providers, giving you consistent real-time capabilities regardless of the underlying model.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- tutorial slug: **litellm-tutorial**
+- chapter focus: **Chapter 4: Streaming & Async**
+- system context: **Litellm Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Streaming & Async`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [LiteLLM Repository](https://github.com/BerriAI/litellm)
+- [LiteLLM Releases](https://github.com/BerriAI/litellm/releases)
+- [LiteLLM Docs](https://docs.litellm.ai/)
+
+### Cross-Tutorial Connection Map
+
+- [Langfuse Tutorial](../langfuse-tutorial/)
+- [Vercel AI SDK Tutorial](../vercel-ai-tutorial/)
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Streaming & Async`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `content`, `messages`, `chunk` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Streaming & Async` as an operating subsystem inside **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `print`, `response`, `model` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Streaming & Async` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `content`.
+2. **Input normalization**: shape incoming data so `messages` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `chunk`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [LiteLLM Repository](https://github.com/BerriAI/litellm)
+  Why it matters: authoritative reference on `LiteLLM Repository` (github.com).
+- [LiteLLM Releases](https://github.com/BerriAI/litellm/releases)
+  Why it matters: authoritative reference on `LiteLLM Releases` (github.com).
+- [LiteLLM Docs](https://docs.litellm.ai/)
+  Why it matters: authoritative reference on `LiteLLM Docs` (docs.litellm.ai).
+
+Suggested trace strategy:
+- search upstream code for `content` and `messages` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Completion API](03-completion.md)
+- [Next Chapter: Chapter 5: Fallbacks & Retries](05-fallbacks.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/litellm-tutorial/05-fallbacks.md b/tutorials/litellm-tutorial/05-fallbacks.md
index 99e6188b..02a65b93 100644
--- a/tutorials/litellm-tutorial/05-fallbacks.md
+++ b/tutorials/litellm-tutorial/05-fallbacks.md
@@ -8,6 +8,9 @@ parent: LiteLLM Tutorial
 
 # Chapter 5: Fallbacks & Retries
 
+Welcome to **Chapter 5: Fallbacks & Retries**. In this part of **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Build resilient LLM applications with automatic fallbacks, intelligent retries, and failure recovery.
 
 ## Overview
@@ -528,4 +531,153 @@ def create_resilient_client(config=RESILIENCE_CONFIG):
     return ResilientAIClient(config)
 ```
 
-These resilience patterns ensure your AI applications remain reliable and available, even when individual providers experience issues. The key is layering multiple protection mechanisms to handle different types of failures gracefully. 
\ No newline at end of file
+These resilience patterns ensure your AI applications remain reliable and available, even when individual providers experience issues. The key is layering multiple protection mechanisms to handle different types of failures gracefully.
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**
+- tutorial slug: **litellm-tutorial**
+- chapter focus: **Chapter 5: Fallbacks & Retries**
+- system context: **Litellm Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Fallbacks & Retries`.
+2. Separate control-plane decisions from data-plane execution.
+3. Capture input contracts, transformation points, and output contracts.
+4. Trace state transitions across request lifecycle stages.
+5. Identify extension hooks and policy interception points.
+6. Map ownership boundaries for team and automation workflows.
+7. Specify rollback and recovery paths for unsafe changes.
+8. Track observability signals for correctness, latency, and cost.
+
+### Operator Decision Matrix
+
+| Decision Area | Low-Risk Path | High-Control Path | Tradeoff |
+|:--------------|:--------------|:------------------|:---------|
+| Runtime mode | managed defaults | explicit policy config | speed vs control |
+| State handling | local ephemeral | durable persisted state | simplicity vs auditability |
+| Tool integration | direct API use | mediated adapter layer | velocity vs governance |
+| Rollout method | manual change | staged + canary rollout | effort vs safety |
+| Incident response | best effort logs | runbooks + SLO alerts | cost vs reliability |
+
+### Failure Modes and Countermeasures
+
+| Failure Mode | Early Signal | Root Cause Pattern | Countermeasure |
+|:-------------|:-------------|:-------------------|:---------------|
+| stale context | inconsistent outputs | missing refresh window | enforce context TTL and refresh hooks |
+| policy drift | unexpected execution | ad hoc overrides | centralize policy profiles |
+| auth mismatch | 401/403 bursts | credential sprawl | rotation schedule + scope minimization |
+| schema breakage | parser/validation errors | unmanaged upstream changes | contract tests per release |
+| retry storms | queue congestion | no backoff controls | jittered backoff + circuit breakers |
+| silent regressions | quality drop without alerts | weak baseline metrics | eval harness with thresholds |
+
+### Implementation Runbook
+
+1. Establish a reproducible baseline environment.
+2. Capture chapter-specific success criteria before changes.
+3. Implement minimal viable path with explicit interfaces.
+4. Add observability before expanding feature scope.
+5. Run deterministic tests for happy-path behavior.
+6. Inject failure scenarios for negative-path validation.
+7. Compare output quality against baseline snapshots.
+8. Promote through staged environments with rollback gates.
+9. Record operational lessons in release notes.
+
+### Quality Gate Checklist
+
+- [ ] chapter-level assumptions are explicit and testable
+- [ ] API/tool boundaries are documented with input/output examples
+- [ ] failure handling includes retry, timeout, and fallback policy
+- [ ] security controls include auth scopes and secret rotation plans
+- [ ] observability includes logs, metrics, traces, and alert thresholds
+- [ ] deployment guidance includes canary and rollback paths
+- [ ] docs include links to upstream sources and related tracks
+- [ ] post-release verification confirms expected behavior under load
+
+### Source Alignment
+
+- [LiteLLM Repository](https://github.com/BerriAI/litellm)
+- [LiteLLM Releases](https://github.com/BerriAI/litellm/releases)
+- [LiteLLM Docs](https://docs.litellm.ai/)
+
+### Cross-Tutorial Connection Map
+
+- [Langfuse Tutorial](../langfuse-tutorial/)
+- [Vercel AI SDK Tutorial](../vercel-ai-tutorial/)
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [Aider Tutorial](../aider-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Fallbacks & Retries`.
+2. Add instrumentation and measure baseline latency and error rate.
+3. Introduce one controlled failure and confirm graceful recovery.
+4. Add policy constraints and verify they are enforced consistently.
+5. Run a staged rollout and document rollback decision criteria.
+
+### Review Questions
+
+1. Which execution boundary matters most for this chapter and why?
+2. What signal detects regressions earliest in your environment?
+3. What tradeoff did you make between delivery speed and governance?
+4. How would you recover from the highest-impact failure mode?
+5. What must be automated before scaling to team-wide adoption?
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `model`, `messages` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Fallbacks & Retries` as an operating subsystem inside **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `models`, `response`, `claude` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Fallbacks & Retries` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `model` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `messages`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [LiteLLM Repository](https://github.com/BerriAI/litellm)
+  Why it matters: authoritative reference on `LiteLLM Repository` (github.com).
+- [LiteLLM Releases](https://github.com/BerriAI/litellm/releases)
+  Why it matters: authoritative reference on `LiteLLM Releases` (github.com).
+- [LiteLLM Docs](https://docs.litellm.ai/)
+  Why it matters: authoritative reference on `LiteLLM Docs` (docs.litellm.ai).
+
+Suggested trace strategy:
+- search upstream code for `self` and `model` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Streaming & Async](04-streaming.md)
+- [Next Chapter: Chapter 6: Cost Tracking](06-cost-tracking.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/litellm-tutorial/06-cost-tracking.md b/tutorials/litellm-tutorial/06-cost-tracking.md
index f87d5238..63536606 100644
--- a/tutorials/litellm-tutorial/06-cost-tracking.md
+++ b/tutorials/litellm-tutorial/06-cost-tracking.md
@@ -8,6 +8,9 @@ parent: LiteLLM Tutorial
 
 # Chapter 6: Cost Tracking
 
+Welcome to **Chapter 6: Cost Tracking**. In this part of **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Monitor, analyze, and optimize your LLM spending across all providers with detailed cost insights.
 
 ## Overview
@@ -587,4 +590,54 @@ def send_cost_alerts_to_slack(webhook_url, alerts):
 # send_cost_alerts_to_slack("https://hooks.slack.com/...", alerts)
 ```
 
-Cost tracking is essential for sustainable LLM applications. With proper monitoring and optimization, you can maintain high-quality AI functionality while controlling expenses effectively. 
\ No newline at end of file
+Cost tracking is essential for sustainable LLM applications. With proper monitoring and optimization, you can maintain high-quality AI functionality while controlling expenses effectively.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `model`, `cost` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Cost Tracking` as an operating subsystem inside **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `days`, `analyzer`, `response` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Cost Tracking` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `model` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `cost`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [LiteLLM Repository](https://github.com/BerriAI/litellm)
+  Why it matters: authoritative reference on `LiteLLM Repository` (github.com).
+- [LiteLLM Releases](https://github.com/BerriAI/litellm/releases)
+  Why it matters: authoritative reference on `LiteLLM Releases` (github.com).
+- [LiteLLM Docs](https://docs.litellm.ai/)
+  Why it matters: authoritative reference on `LiteLLM Docs` (docs.litellm.ai).
+
+Suggested trace strategy:
+- search upstream code for `self` and `model` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Fallbacks & Retries](05-fallbacks.md)
+- [Next Chapter: Chapter 7: LiteLLM Proxy](07-proxy.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/litellm-tutorial/07-proxy.md b/tutorials/litellm-tutorial/07-proxy.md
index a1288e5f..32eb1048 100644
--- a/tutorials/litellm-tutorial/07-proxy.md
+++ b/tutorials/litellm-tutorial/07-proxy.md
@@ -8,6 +8,9 @@ parent: LiteLLM Tutorial
 
 # Chapter 7: LiteLLM Proxy
 
+Welcome to **Chapter 7: LiteLLM Proxy**. In this part of **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploy a centralized OpenAI-compatible proxy server that routes requests to multiple LLM providers with unified authentication, rate limiting, and cost tracking.
 
 ## Overview
@@ -582,4 +585,54 @@ data:
       metrics_path: '/metrics'
 ```
 
-The LiteLLM Proxy transforms how you deploy and manage LLM APIs, providing enterprise-grade features while maintaining OpenAI compatibility. It's the perfect solution for organizations wanting to standardize their AI infrastructure across multiple providers. 
\ No newline at end of file
+The LiteLLM Proxy transforms how you deploy and manage LLM APIs, providing enterprise-grade features while maintaining OpenAI compatibility. It's the perfect solution for organizations wanting to standardize their AI infrastructure across multiple providers.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `litellm`, `config`, `proxy` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: LiteLLM Proxy` as an operating subsystem inside **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `yaml`, `model`, `localhost` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: LiteLLM Proxy` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `litellm`.
+2. **Input normalization**: shape incoming data so `config` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `proxy`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [LiteLLM Repository](https://github.com/BerriAI/litellm)
+  Why it matters: authoritative reference on `LiteLLM Repository` (github.com).
+- [LiteLLM Releases](https://github.com/BerriAI/litellm/releases)
+  Why it matters: authoritative reference on `LiteLLM Releases` (github.com).
+- [LiteLLM Docs](https://docs.litellm.ai/)
+  Why it matters: authoritative reference on `LiteLLM Docs` (docs.litellm.ai).
+
+Suggested trace strategy:
+- search upstream code for `litellm` and `config` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Cost Tracking](06-cost-tracking.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/litellm-tutorial/08-production.md b/tutorials/litellm-tutorial/08-production.md
index 7a10eec8..1e55bfcf 100644
--- a/tutorials/litellm-tutorial/08-production.md
+++ b/tutorials/litellm-tutorial/08-production.md
@@ -8,6 +8,9 @@ parent: LiteLLM Tutorial
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Deploy LiteLLM applications to production with monitoring, scaling, security, and operational best practices.
 
 ## Overview
@@ -798,4 +801,53 @@ Before going live:
 - [ ] Cost budgets and alerts set
 - [ ] Performance benchmarks established
 
-This production setup ensures LiteLLM can handle enterprise-scale workloads while maintaining reliability, security, and cost efficiency. The modular architecture allows for easy scaling and maintenance as your AI usage grows. 
\ No newline at end of file
+This production setup ensures LiteLLM can handle enterprise-scale workloads while maintaining reliability, security, and cost efficiency. The modular architecture allows for easy scaling and maintenance as your AI usage grows.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `litellm`, `name`, `model` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **LiteLLM Tutorial: Unified LLM Gateway and Routing Layer**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `proxy`, `redis`, `config` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `litellm`.
+2. **Input normalization**: shape incoming data so `name` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [LiteLLM Repository](https://github.com/BerriAI/litellm)
+  Why it matters: authoritative reference on `LiteLLM Repository` (github.com).
+- [LiteLLM Releases](https://github.com/BerriAI/litellm/releases)
+  Why it matters: authoritative reference on `LiteLLM Releases` (github.com).
+- [LiteLLM Docs](https://docs.litellm.ai/)
+  Why it matters: authoritative reference on `LiteLLM Docs` (docs.litellm.ai).
+
+Suggested trace strategy:
+- search upstream code for `litellm` and `name` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: LiteLLM Proxy](07-proxy.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/liveblocks-tutorial/01-getting-started.md b/tutorials/liveblocks-tutorial/01-getting-started.md
index eb603d3c..22e809fc 100644
--- a/tutorials/liveblocks-tutorial/01-getting-started.md
+++ b/tutorials/liveblocks-tutorial/01-getting-started.md
@@ -8,6 +8,9 @@ parent: "Liveblocks - Real-Time Collaboration Deep Dive"
 
 # Chapter 1: Getting Started
 
+Welcome to **Chapter 1: Getting Started**. In this part of **Liveblocks - Real-Time Collaboration Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 Real-time collaboration has become a table-stakes feature for modern applications. Users expect to see each other's cursors, edits, and presence in real time -- the experience pioneered by Google Docs and refined by Figma and Notion. Building this from scratch requires deep expertise in WebSockets, conflict resolution algorithms, and distributed systems. Liveblocks abstracts all of that complexity into a developer-friendly SDK.
@@ -628,3 +631,52 @@ In the next chapter, we will dive deep into **[Presence & Awareness](./02-presen
 
 ---
 *Built with insights from the [Liveblocks](https://liveblocks.io) platform.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `liveblocks`, `color`, `client` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started` as an operating subsystem inside **Liveblocks - Real-Time Collaboration Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `name`, `cursor`, `status` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `liveblocks`.
+2. **Input normalization**: shape incoming data so `color` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `client`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Liveblocks GitHub Repository](https://github.com/liveblocks/liveblocks)
+  Why it matters: authoritative reference on `Liveblocks GitHub Repository` (github.com).
+- [Liveblocks Product Site](https://liveblocks.io)
+  Why it matters: authoritative reference on `Liveblocks Product Site` (liveblocks.io).
+- [Liveblocks Documentation](https://liveblocks.io/docs)
+  Why it matters: authoritative reference on `Liveblocks Documentation` (liveblocks.io).
+
+Suggested trace strategy:
+- search upstream code for `liveblocks` and `color` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Presence & Awareness](02-presence-awareness.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/liveblocks-tutorial/02-presence-awareness.md b/tutorials/liveblocks-tutorial/02-presence-awareness.md
index f4616657..ce2975a6 100644
--- a/tutorials/liveblocks-tutorial/02-presence-awareness.md
+++ b/tutorials/liveblocks-tutorial/02-presence-awareness.md
@@ -8,6 +8,9 @@ parent: "Liveblocks - Real-Time Collaboration Deep Dive"
 
 # Chapter 2: Presence & Awareness
 
+Welcome to **Chapter 2: Presence & Awareness**. In this part of **Liveblocks - Real-Time Collaboration Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 Presence is the foundation of every collaborative experience. It answers the fundamental question: "Who else is here, and what are they doing?" When you see live cursors gliding across a Figma canvas or a list of active users in a Google Doc, you are seeing presence in action.
@@ -733,3 +736,53 @@ Now that users can see each other, it is time to let them collaborate on shared
 
 ---
 *Built with insights from the [Liveblocks](https://liveblocks.io) platform.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `color`, `user`, `cursor` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Presence & Awareness` as an operating subsystem inside **Liveblocks - Real-Time Collaboration Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `name`, `info`, `style` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Presence & Awareness` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `color`.
+2. **Input normalization**: shape incoming data so `user` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `cursor`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Liveblocks GitHub Repository](https://github.com/liveblocks/liveblocks)
+  Why it matters: authoritative reference on `Liveblocks GitHub Repository` (github.com).
+- [Liveblocks Product Site](https://liveblocks.io)
+  Why it matters: authoritative reference on `Liveblocks Product Site` (liveblocks.io).
+- [Liveblocks Documentation](https://liveblocks.io/docs)
+  Why it matters: authoritative reference on `Liveblocks Documentation` (liveblocks.io).
+
+Suggested trace strategy:
+- search upstream code for `color` and `user` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started](01-getting-started.md)
+- [Next Chapter: Chapter 3: Storage & Conflict Resolution](03-storage-conflict-resolution.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/liveblocks-tutorial/03-storage-conflict-resolution.md b/tutorials/liveblocks-tutorial/03-storage-conflict-resolution.md
index f629eb50..c269ea93 100644
--- a/tutorials/liveblocks-tutorial/03-storage-conflict-resolution.md
+++ b/tutorials/liveblocks-tutorial/03-storage-conflict-resolution.md
@@ -8,6 +8,9 @@ parent: "Liveblocks - Real-Time Collaboration Deep Dive"
 
 # Chapter 3: Storage & Conflict Resolution
 
+Welcome to **Chapter 3: Storage & Conflict Resolution**. In this part of **Liveblocks - Real-Time Collaboration Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 Presence tells you who is here. Storage tells you what they are working on together. Liveblocks Storage is a persistent, real-time data layer that automatically resolves conflicts when multiple users edit the same data simultaneously. It is powered by CRDTs (Conflict-free Replicated Data Types), the same class of algorithms used by Figma, Apple Notes, and other world-class collaborative tools.
@@ -704,3 +707,53 @@ With shared data synchronized, it is time to add discussion capabilities. In **[
 
 ---
 *Built with insights from the [Liveblocks](https://liveblocks.io) platform.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `LiveObject`, `fill`, `todos` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Storage & Conflict Resolution` as an operating subsystem inside **Liveblocks - Real-Time Collaboration Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `shape`, `shapes`, `todo` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Storage & Conflict Resolution` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `LiveObject`.
+2. **Input normalization**: shape incoming data so `fill` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `todos`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Liveblocks GitHub Repository](https://github.com/liveblocks/liveblocks)
+  Why it matters: authoritative reference on `Liveblocks GitHub Repository` (github.com).
+- [Liveblocks Product Site](https://liveblocks.io)
+  Why it matters: authoritative reference on `Liveblocks Product Site` (liveblocks.io).
+- [Liveblocks Documentation](https://liveblocks.io/docs)
+  Why it matters: authoritative reference on `Liveblocks Documentation` (liveblocks.io).
+
+Suggested trace strategy:
+- search upstream code for `LiveObject` and `fill` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Presence & Awareness](02-presence-awareness.md)
+- [Next Chapter: Chapter 4: Comments & Threads](04-comments-threads.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/liveblocks-tutorial/04-comments-threads.md b/tutorials/liveblocks-tutorial/04-comments-threads.md
index e31a9178..5069f99d 100644
--- a/tutorials/liveblocks-tutorial/04-comments-threads.md
+++ b/tutorials/liveblocks-tutorial/04-comments-threads.md
@@ -8,6 +8,9 @@ parent: "Liveblocks - Real-Time Collaboration Deep Dive"
 
 # Chapter 4: Comments & Threads
 
+Welcome to **Chapter 4: Comments & Threads**. In this part of **Liveblocks - Real-Time Collaboration Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 Modern collaborative applications do more than let users edit together -- they let users discuss their work in context. Think of comments pinned to a specific location in a Figma design, threaded discussions in a Google Doc, or inline code review comments on GitHub. Liveblocks provides a complete commenting system with threads, replies, mentions, and rich-text support out of the box.
@@ -814,3 +817,53 @@ Comments create conversations, but users need to know when someone mentions them
 
 ---
 *Built with insights from the [Liveblocks](https://liveblocks.io) platform.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `thread`, `liveblocks`, `resolved` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Comments & Threads` as an operating subsystem inside **Liveblocks - Real-Time Collaboration Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `text`, `threads`, `Thread` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Comments & Threads` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `thread`.
+2. **Input normalization**: shape incoming data so `liveblocks` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `resolved`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Liveblocks GitHub Repository](https://github.com/liveblocks/liveblocks)
+  Why it matters: authoritative reference on `Liveblocks GitHub Repository` (github.com).
+- [Liveblocks Product Site](https://liveblocks.io)
+  Why it matters: authoritative reference on `Liveblocks Product Site` (liveblocks.io).
+- [Liveblocks Documentation](https://liveblocks.io/docs)
+  Why it matters: authoritative reference on `Liveblocks Documentation` (liveblocks.io).
+
+Suggested trace strategy:
+- search upstream code for `thread` and `liveblocks` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Storage & Conflict Resolution](03-storage-conflict-resolution.md)
+- [Next Chapter: Chapter 5: Notifications](05-notifications.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/liveblocks-tutorial/05-notifications.md b/tutorials/liveblocks-tutorial/05-notifications.md
index 1e2f8e48..d7b61052 100644
--- a/tutorials/liveblocks-tutorial/05-notifications.md
+++ b/tutorials/liveblocks-tutorial/05-notifications.md
@@ -8,6 +8,9 @@ parent: "Liveblocks - Real-Time Collaboration Deep Dive"
 
 # Chapter 5: Notifications
 
+Welcome to **Chapter 5: Notifications**. In this part of **Liveblocks - Real-Time Collaboration Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 Collaboration does not happen only when users are online at the same time. When someone mentions you in a comment, replies to your thread, or makes a change you care about, you need to know -- even if you are not looking at the app. Liveblocks provides a complete notification system that covers in-app inbox notifications, email delivery, and custom notification triggers.
@@ -790,3 +793,53 @@ With presence, storage, comments, and notifications covered, it is time to see h
 
 ---
 *Built with insights from the [Liveblocks](https://liveblocks.io) platform.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `notification`, `liveblocks`, `roomId` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Notifications` as an operating subsystem inside **Liveblocks - Real-Time Collaboration Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `kind`, `user`, `email` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Notifications` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `notification`.
+2. **Input normalization**: shape incoming data so `liveblocks` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `roomId`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Liveblocks GitHub Repository](https://github.com/liveblocks/liveblocks)
+  Why it matters: authoritative reference on `Liveblocks GitHub Repository` (github.com).
+- [Liveblocks Product Site](https://liveblocks.io)
+  Why it matters: authoritative reference on `Liveblocks Product Site` (liveblocks.io).
+- [Liveblocks Documentation](https://liveblocks.io/docs)
+  Why it matters: authoritative reference on `Liveblocks Documentation` (liveblocks.io).
+
+Suggested trace strategy:
+- search upstream code for `notification` and `liveblocks` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Comments & Threads](04-comments-threads.md)
+- [Next Chapter: Chapter 6: React Integration](06-react-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/liveblocks-tutorial/06-react-integration.md b/tutorials/liveblocks-tutorial/06-react-integration.md
index b791568e..6f3ba2cf 100644
--- a/tutorials/liveblocks-tutorial/06-react-integration.md
+++ b/tutorials/liveblocks-tutorial/06-react-integration.md
@@ -8,6 +8,9 @@ parent: "Liveblocks - Real-Time Collaboration Deep Dive"
 
 # Chapter 6: React Integration
 
+Welcome to **Chapter 6: React Integration**. In this part of **Liveblocks - Real-Time Collaboration Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 Liveblocks was designed with React as a first-class citizen. Its hook-based API aligns naturally with React's component model, and its Suspense support enables clean loading states without manual state management. This chapter provides a comprehensive guide to every major React hook, demonstrates Suspense integration patterns, and covers performance optimization techniques for building collaborative React applications at scale.
@@ -768,3 +771,53 @@ Now that you have mastered the React integration, let us explore advanced patter
 
 ---
 *Built with insights from the [Liveblocks](https://liveblocks.io) platform.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `shape`, `root`, `cursor` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: React Integration` as an operating subsystem inside **Liveblocks - Real-Time Collaboration Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `name`, `Suspense`, `shapes` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: React Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `shape`.
+2. **Input normalization**: shape incoming data so `root` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `cursor`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Liveblocks GitHub Repository](https://github.com/liveblocks/liveblocks)
+  Why it matters: authoritative reference on `Liveblocks GitHub Repository` (github.com).
+- [Liveblocks Product Site](https://liveblocks.io)
+  Why it matters: authoritative reference on `Liveblocks Product Site` (liveblocks.io).
+- [Liveblocks Documentation](https://liveblocks.io/docs)
+  Why it matters: authoritative reference on `Liveblocks Documentation` (liveblocks.io).
+
+Suggested trace strategy:
+- search upstream code for `shape` and `root` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Notifications](05-notifications.md)
+- [Next Chapter: Chapter 7: Advanced Patterns](07-advanced-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/liveblocks-tutorial/07-advanced-patterns.md b/tutorials/liveblocks-tutorial/07-advanced-patterns.md
index c4b30ebb..7df03bf1 100644
--- a/tutorials/liveblocks-tutorial/07-advanced-patterns.md
+++ b/tutorials/liveblocks-tutorial/07-advanced-patterns.md
@@ -8,6 +8,9 @@ parent: "Liveblocks - Real-Time Collaboration Deep Dive"
 
 # Chapter 7: Advanced Patterns
 
+Welcome to **Chapter 7: Advanced Patterns**. In this part of **Liveblocks - Real-Time Collaboration Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 Once you have the basics of presence, storage, and comments working, real-world applications demand more sophisticated capabilities. Users expect to undo and redo changes. Apps need to work when the network drops. Access control must ensure users can only see and modify data they are authorized to access. And for rich text collaboration, Yjs integration unlocks Google-Docs-style co-editing.
@@ -751,3 +754,53 @@ Your collaborative app is feature-complete. Now it needs to be production-ready.
 
 ---
 *Built with insights from the [Liveblocks](https://liveblocks.io) platform.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `user`, `liveblocks`, `editor` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Advanced Patterns` as an operating subsystem inside **Liveblocks - Real-Time Collaboration Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `button`, `session`, `yDoc` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Advanced Patterns` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `user`.
+2. **Input normalization**: shape incoming data so `liveblocks` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `editor`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Liveblocks GitHub Repository](https://github.com/liveblocks/liveblocks)
+  Why it matters: authoritative reference on `Liveblocks GitHub Repository` (github.com).
+- [Liveblocks Product Site](https://liveblocks.io)
+  Why it matters: authoritative reference on `Liveblocks Product Site` (liveblocks.io).
+- [Liveblocks Documentation](https://liveblocks.io/docs)
+  Why it matters: authoritative reference on `Liveblocks Documentation` (liveblocks.io).
+
+Suggested trace strategy:
+- search upstream code for `user` and `liveblocks` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: React Integration](06-react-integration.md)
+- [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/liveblocks-tutorial/08-production-deployment.md b/tutorials/liveblocks-tutorial/08-production-deployment.md
index 01f7636d..18ea7064 100644
--- a/tutorials/liveblocks-tutorial/08-production-deployment.md
+++ b/tutorials/liveblocks-tutorial/08-production-deployment.md
@@ -8,6 +8,9 @@ parent: "Liveblocks - Real-Time Collaboration Deep Dive"
 
 # Chapter 8: Production Deployment
 
+Welcome to **Chapter 8: Production Deployment**. In this part of **Liveblocks - Real-Time Collaboration Deep Dive**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 ## Introduction
 
 Building a collaborative app in development is one thing. Running it in production with real users, at scale, with security and observability, is another challenge entirely. Liveblocks handles the hard infrastructure problems -- WebSocket scaling, CRDT resolution, data persistence -- but you still need to configure authentication properly, handle webhooks reliably, monitor your integration, and follow security best practices.
@@ -862,3 +865,52 @@ You now have the knowledge to build collaborative applications that rival the be
 
 ---
 *Built with insights from the [Liveblocks](https://liveblocks.io) platform.*
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `roomId`, `liveblocks`, `event` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Deployment` as an operating subsystem inside **Liveblocks - Real-Time Collaboration Deep Dive**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `status`, `storage`, `Liveblocks` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `roomId`.
+2. **Input normalization**: shape incoming data so `liveblocks` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `event`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [Liveblocks GitHub Repository](https://github.com/liveblocks/liveblocks)
+  Why it matters: authoritative reference on `Liveblocks GitHub Repository` (github.com).
+- [Liveblocks Product Site](https://liveblocks.io)
+  Why it matters: authoritative reference on `Liveblocks Product Site` (liveblocks.io).
+- [Liveblocks Documentation](https://liveblocks.io/docs)
+  Why it matters: authoritative reference on `Liveblocks Documentation` (liveblocks.io).
+
+Suggested trace strategy:
+- search upstream code for `roomId` and `liveblocks` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Advanced Patterns](07-advanced-patterns.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/llama-cpp-tutorial/01-getting-started.md b/tutorials/llama-cpp-tutorial/01-getting-started.md
index 7bc461e8..83a15880 100644
--- a/tutorials/llama-cpp-tutorial/01-getting-started.md
+++ b/tutorials/llama-cpp-tutorial/01-getting-started.md
@@ -8,6 +8,9 @@ parent: llama.cpp Tutorial
 
 # Chapter 1: Getting Started with llama.cpp
 
+Welcome to **Chapter 1: Getting Started with llama.cpp**. In this part of **llama.cpp Tutorial: Local LLM Inference**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Build llama.cpp from source and run your first LLM locally with optimized C/C++ inference.
 
 ## Overview
@@ -312,4 +315,51 @@ Now that you can run models locally, let's explore model formats and quantizatio
 ./llama-cli -m model.gguf --verbose-prompt --prompt ""
 ```
 
-This setup gives you a fast, local LLM inference engine that works offline and respects your privacy. The pure C/C++ implementation means quick startup times and efficient resource usage. 
\ No newline at end of file
+This setup gives you a fast, local LLM inference engine that works offline and respects your privacy. The pure C/C++ implementation means quick startup times and efficient resource usage.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `llama`, `model`, `build` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with llama.cpp` as an operating subsystem inside **llama.cpp Tutorial: Local LLM Inference**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `gguf`, `cmake`, `prompt` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with llama.cpp` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `llama`.
+2. **Input normalization**: shape incoming data so `model` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `build`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ggerganov/llama.cpp)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `llama` and `model` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Model Formats and GGUF](02-model-formats.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/llama-cpp-tutorial/02-model-formats.md b/tutorials/llama-cpp-tutorial/02-model-formats.md
index 8af7225d..88abbe35 100644
--- a/tutorials/llama-cpp-tutorial/02-model-formats.md
+++ b/tutorials/llama-cpp-tutorial/02-model-formats.md
@@ -8,6 +8,9 @@ parent: llama.cpp Tutorial
 
 # Chapter 2: Model Formats and GGUF
 
+Welcome to **Chapter 2: Model Formats and GGUF**. In this part of **llama.cpp Tutorial: Local LLM Inference**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Understand GGUF format, quantization levels, and how to choose the right model for your hardware.
 
 ## Overview
@@ -339,4 +342,52 @@ file model.gguf  # Should show "GGUF" format
 # Use smaller models
 ```
 
-Next: Master the command-line interface with advanced options and interactive modes. 
\ No newline at end of file
+Next: Master the command-line interface with advanced options and interactive modes.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `gguf`, `model`, `llama` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Model Formats and GGUF` as an operating subsystem inside **llama.cpp Tutorial: Local LLM Inference**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `Q4_K`, `quantization`, `print` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Model Formats and GGUF` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `gguf`.
+2. **Input normalization**: shape incoming data so `model` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `llama`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ggerganov/llama.cpp)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `gguf` and `model` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with llama.cpp](01-getting-started.md)
+- [Next Chapter: Chapter 3: Command Line Interface](03-cli-usage.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/llama-cpp-tutorial/03-cli-usage.md b/tutorials/llama-cpp-tutorial/03-cli-usage.md
index ab037aaa..56339dba 100644
--- a/tutorials/llama-cpp-tutorial/03-cli-usage.md
+++ b/tutorials/llama-cpp-tutorial/03-cli-usage.md
@@ -8,6 +8,9 @@ parent: llama.cpp Tutorial
 
 # Chapter 3: Command Line Interface
 
+Welcome to **Chapter 3: Command Line Interface**. In this part of **llama.cpp Tutorial: Local LLM Inference**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Master llama-cli with advanced options, interactive modes, and conversation management.
 
 ## Overview
@@ -489,4 +492,52 @@ tmux attach-session -t $session_name
 ./llama-cli -m model.gguf --temp 0.8 --top-k 40 --top-p 0.9
 ```
 
-The CLI provides complete control over model behavior. Experiment with different parameters to find what works best for your specific use case and model. 
\ No newline at end of file
+The CLI provides complete control over model behavior. Experiment with different parameters to find what works best for your specific use case and model.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `model`, `llama`, `gguf` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Command Line Interface` as an operating subsystem inside **llama.cpp Tutorial: Local LLM Inference**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `prompt`, `float`, `file` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Command Line Interface` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `model`.
+2. **Input normalization**: shape incoming data so `llama` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `gguf`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ggerganov/llama.cpp)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `model` and `llama` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Model Formats and GGUF](02-model-formats.md)
+- [Next Chapter: Chapter 4: Server Mode](04-server.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/llama-cpp-tutorial/04-server.md b/tutorials/llama-cpp-tutorial/04-server.md
index 8e23609a..94cdc01a 100644
--- a/tutorials/llama-cpp-tutorial/04-server.md
+++ b/tutorials/llama-cpp-tutorial/04-server.md
@@ -8,6 +8,9 @@ parent: llama.cpp Tutorial
 
 # Chapter 4: Server Mode
 
+Welcome to **Chapter 4: Server Mode**. In this part of **llama.cpp Tutorial: Local LLM Inference**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Run llama.cpp as an OpenAI-compatible HTTP server for API access and integration with applications.
 
 ## Overview
@@ -603,4 +606,52 @@ sudo ufw allow 8080
 6. **Updates**: Keep llama.cpp updated for performance improvements
 7. **Testing**: Thoroughly test your API endpoints before production
 
-The server mode makes llama.cpp accessible via standard HTTP APIs, enabling integration with any application that supports OpenAI-compatible endpoints. This is the most practical way to use llama.cpp in production applications. 
\ No newline at end of file
+The server mode makes llama.cpp accessible via standard HTTP APIs, enabling integration with any application that supports OpenAI-compatible endpoints. This is the most practical way to use llama.cpp in production applications.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `llama`, `server`, `model` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Server Mode` as an operating subsystem inside **llama.cpp Tutorial: Local LLM Inference**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `gguf`, `response`, `size` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Server Mode` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `llama`.
+2. **Input normalization**: shape incoming data so `server` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ggerganov/llama.cpp)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `llama` and `server` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Command Line Interface](03-cli-usage.md)
+- [Next Chapter: Chapter 5: GPU Acceleration](05-gpu.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/llama-cpp-tutorial/05-gpu.md b/tutorials/llama-cpp-tutorial/05-gpu.md
index 08d559ed..69ee3d80 100644
--- a/tutorials/llama-cpp-tutorial/05-gpu.md
+++ b/tutorials/llama-cpp-tutorial/05-gpu.md
@@ -8,6 +8,9 @@ parent: llama.cpp Tutorial
 
 # Chapter 5: GPU Acceleration
 
+Welcome to **Chapter 5: GPU Acceleration**. In this part of **llama.cpp Tutorial: Local LLM Inference**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Enable GPU acceleration with CUDA, Metal, and ROCm for dramatically faster inference.
 
 ## Overview
@@ -496,4 +499,52 @@ gpu_vs_cpu_comparison(4.5, 12)
 6. **Test thoroughly**: Validate outputs match CPU results
 7. **Power management**: Use appropriate power limits for stability
 
-GPU acceleration dramatically improves inference speed and enables larger model usage. Choose the right GPU platform for your hardware and optimize layer offloading for maximum performance. 
\ No newline at end of file
+GPU acceleration dramatically improves inference speed and enables larger model usage. Choose the right GPU platform for your hardware and optimize layer offloading for maximum performance.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `layers`, `llama`, `model` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: GPU Acceleration` as an operating subsystem inside **llama.cpp Tutorial: Local LLM Inference**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `gguf`, `size`, `rocm` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: GPU Acceleration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `layers`.
+2. **Input normalization**: shape incoming data so `llama` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ggerganov/llama.cpp)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `layers` and `llama` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Server Mode](04-server.md)
+- [Next Chapter: Chapter 6: Quantization](06-quantization.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/llama-cpp-tutorial/06-quantization.md b/tutorials/llama-cpp-tutorial/06-quantization.md
index 5da52493..957025a1 100644
--- a/tutorials/llama-cpp-tutorial/06-quantization.md
+++ b/tutorials/llama-cpp-tutorial/06-quantization.md
@@ -8,6 +8,9 @@ parent: llama.cpp Tutorial
 
 # Chapter 6: Quantization
 
+Welcome to **Chapter 6: Quantization**. In this part of **llama.cpp Tutorial: Local LLM Inference**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Convert and quantize models to reduce memory usage while maintaining quality.
 
 ## Overview
@@ -451,4 +454,52 @@ echo "=== Quantization Complete ==="
 ls -lh "$output_dir/"
 ```
 
-Quantization is essential for practical LLM deployment. It enables running large models on consumer hardware while maintaining good quality. Always test your specific use case at different quantization levels to find the best balance. 
\ No newline at end of file
+Quantization is essential for practical LLM deployment. It enables running large models on consumer hardware while maintaining good quality. Always test your specific use case at different quantization levels to find the best balance.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `model`, `gguf`, `llama` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Quantization` as an operating subsystem inside **llama.cpp Tutorial: Local LLM Inference**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `quantize`, `Q4_K`, `quality` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Quantization` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `model`.
+2. **Input normalization**: shape incoming data so `gguf` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `llama`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ggerganov/llama.cpp)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `model` and `gguf` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: GPU Acceleration](05-gpu.md)
+- [Next Chapter: Chapter 7: Advanced Features](07-advanced.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/llama-cpp-tutorial/07-advanced.md b/tutorials/llama-cpp-tutorial/07-advanced.md
index edf67885..c8f1ad57 100644
--- a/tutorials/llama-cpp-tutorial/07-advanced.md
+++ b/tutorials/llama-cpp-tutorial/07-advanced.md
@@ -8,6 +8,9 @@ parent: llama.cpp Tutorial
 
 # Chapter 7: Advanced Features
 
+Welcome to **Chapter 7: Advanced Features**. In this part of **llama.cpp Tutorial: Local LLM Inference**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Explore grammar-based generation, embeddings, multimodal models, and custom extensions.
 
 ## Overview
@@ -514,4 +517,52 @@ cmake --build build --config Release
 5. **Extensions**: Keep custom code modular and well-documented
 6. **Updates**: Stay current with llama.cpp developments
 
-These advanced features unlock sophisticated use cases beyond basic text generation, from structured data extraction to multimodal understanding. 
\ No newline at end of file
+These advanced features unlock sophisticated use cases beyond basic text generation, from structured data extraction to multimodal understanding.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `model`, `gguf` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Advanced Features` as an operating subsystem inside **llama.cpp Tutorial: Local LLM Inference**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `prompt`, `llama`, `text` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Advanced Features` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `model` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `gguf`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ggerganov/llama.cpp)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `model` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Quantization](06-quantization.md)
+- [Next Chapter: Chapter 8: Integration](08-integration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/llama-cpp-tutorial/08-integration.md b/tutorials/llama-cpp-tutorial/08-integration.md
index 42056c5c..c98f42ca 100644
--- a/tutorials/llama-cpp-tutorial/08-integration.md
+++ b/tutorials/llama-cpp-tutorial/08-integration.md
@@ -8,6 +8,9 @@ parent: llama.cpp Tutorial
 
 # Chapter 8: Integration
 
+Welcome to **Chapter 8: Integration**. In this part of **llama.cpp Tutorial: Local LLM Inference**, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
+
+
 > Integrate llama.cpp with Python applications, web services, and production systems.
 
 ## Overview
@@ -699,4 +702,51 @@ except Exception as e:
 6. **Security**: Validate inputs and limit resource usage
 7. **Updates**: Keep llama-cpp-python updated for latest features
 
-These integration patterns enable seamless incorporation of llama.cpp into Python applications, from simple scripts to complex production systems. The combination of C++ performance and Python flexibility makes llama.cpp a powerful choice for local LLM deployment. 
\ No newline at end of file
+These integration patterns enable seamless incorporation of llama.cpp into Python applications, from simple scripts to complex production systems. The combination of C++ performance and Python flexibility makes llama.cpp a powerful choice for local LLM deployment.
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `model`, `model_path` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Integration` as an operating subsystem inside **llama.cpp Tutorial: Local LLM Inference**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `response`, `gguf`, `llama` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Integration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `model` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `model_path`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/ggerganov/llama.cpp)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+- [Awesome Code Docs](https://github.com/johnxie/awesome-code-docs)
+  Why it matters: authoritative reference on `Awesome Code Docs` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `model` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Advanced Features](07-advanced.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/llama-factory-tutorial/01-getting-started.md b/tutorials/llama-factory-tutorial/01-getting-started.md
index b28322b7..771fb3ab 100644
--- a/tutorials/llama-factory-tutorial/01-getting-started.md
+++ b/tutorials/llama-factory-tutorial/01-getting-started.md
@@ -363,3 +363,48 @@ Now that you have LLaMA-Factory running, let's explore data preparation and proc
 4. Explore the web interface for visual training setup
 
 *What kind of model are you most excited to train with LLaMA-Factory?* 🤖
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `json`, `llamafactory`, `output` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 1: Getting Started with LLaMA-Factory` as an operating subsystem inside **LLaMA-Factory Tutorial: Unified Framework for LLM Training and Fine-tuning**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `training`, `model`, `dataset` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 1: Getting Started with LLaMA-Factory` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `json`.
+2. **Input normalization**: shape incoming data so `llamafactory` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `output`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/hiyouga/LLaMA-Factory)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `json` and `llamafactory` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Next Chapter: Chapter 2: Data Preparation](02-data-preparation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/llama-factory-tutorial/02-data-preparation.md b/tutorials/llama-factory-tutorial/02-data-preparation.md
index 3157cbcf..2df52010 100644
--- a/tutorials/llama-factory-tutorial/02-data-preparation.md
+++ b/tutorials/llama-factory-tutorial/02-data-preparation.md
@@ -622,3 +622,49 @@ Ready to start training? In [Chapter 3: Model Configuration](03-model-configurat
 5. Experiment with different data splitting strategies
 
 *What's the most challenging dataset you've prepared?* 📊
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `example`, `Dict` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 2: Data Preparation` as an operating subsystem inside **LLaMA-Factory Tutorial: Unified Framework for LLM Training and Fine-tuning**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `List`, `item`, `instruction` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 2: Data Preparation` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `example` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `Dict`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/hiyouga/LLaMA-Factory)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `example` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 1: Getting Started with LLaMA-Factory](01-getting-started.md)
+- [Next Chapter: Chapter 3: Model Configuration](03-model-configuration.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/llama-factory-tutorial/03-model-configuration.md b/tutorials/llama-factory-tutorial/03-model-configuration.md
index e9513c14..5a558b32 100644
--- a/tutorials/llama-factory-tutorial/03-model-configuration.md
+++ b/tutorials/llama-factory-tutorial/03-model-configuration.md
@@ -582,3 +582,49 @@ Ready to start training? In [Chapter 4: Training Pipeline](04-training-pipeline.
 5. Create automated model configuration scripts
 
 *What's the most complex model configuration you've set up?* ⚙️
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `model`, `torch`, `llama` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 3: Model Configuration` as an operating subsystem inside **LLaMA-Factory Tutorial: Unified Framework for LLM Training and Fine-tuning**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `print`, `quantization`, `tokenizer` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 3: Model Configuration` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `model`.
+2. **Input normalization**: shape incoming data so `torch` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `llama`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/hiyouga/LLaMA-Factory)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `model` and `torch` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 2: Data Preparation](02-data-preparation.md)
+- [Next Chapter: Chapter 4: Training Pipeline](04-training-pipeline.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/llama-factory-tutorial/04-training-pipeline.md b/tutorials/llama-factory-tutorial/04-training-pipeline.md
index 544ba6b7..0e516442 100644
--- a/tutorials/llama-factory-tutorial/04-training-pipeline.md
+++ b/tutorials/llama-factory-tutorial/04-training-pipeline.md
@@ -693,3 +693,49 @@ Ready to evaluate your trained models? In [Chapter 5: Model Evaluation](05-model
 5. Debug and fix common training issues in your pipeline
 
 *What's the most complex training setup you've implemented?* 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `loss`, `config` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 4: Training Pipeline` as an operating subsystem inside **LLaMA-Factory Tutorial: Unified Framework for LLM Training and Fine-tuning**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `step`, `item`, `print` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 4: Training Pipeline` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `loss` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `config`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/hiyouga/LLaMA-Factory)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `loss` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 3: Model Configuration](03-model-configuration.md)
+- [Next Chapter: Chapter 5: Model Evaluation](05-model-evaluation.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/llama-factory-tutorial/05-model-evaluation.md b/tutorials/llama-factory-tutorial/05-model-evaluation.md
index 5ebee2d2..b1bfbf66 100644
--- a/tutorials/llama-factory-tutorial/05-model-evaluation.md
+++ b/tutorials/llama-factory-tutorial/05-model-evaluation.md
@@ -787,3 +787,49 @@ Ready to deploy your evaluated models? In [Chapter 6: Deployment](06-deployment.
 5. Integrate automated benchmarking into your training pipeline
 
 *What's the most important metric for evaluating your models?* 📈
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `report`, `assessment` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 5: Model Evaluation` as an operating subsystem inside **LLaMA-Factory Tutorial: Unified Framework for LLM Training and Fine-tuning**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `model`, `results`, `Dict` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 5: Model Evaluation` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `report` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `assessment`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/hiyouga/LLaMA-Factory)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `report` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 4: Training Pipeline](04-training-pipeline.md)
+- [Next Chapter: Chapter 6: Deployment](06-deployment.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/llama-factory-tutorial/06-deployment.md b/tutorials/llama-factory-tutorial/06-deployment.md
index 855d725b..7a595029 100644
--- a/tutorials/llama-factory-tutorial/06-deployment.md
+++ b/tutorials/llama-factory-tutorial/06-deployment.md
@@ -598,3 +598,49 @@ Ready for advanced techniques? In [Chapter 7: Advanced Techniques](07-advanced-t
 5. Containerize your model for easy deployment
 
 *What's your preferred deployment strategy?* ☁️
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `request`, `model` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 6: Deployment` as an operating subsystem inside **LLaMA-Factory Tutorial: Unified Framework for LLM Training and Fine-tuning**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `prompt`, `response`, `model_path` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 6: Deployment` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `request` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `model`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/hiyouga/LLaMA-Factory)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `request` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 5: Model Evaluation](05-model-evaluation.md)
+- [Next Chapter: Chapter 7: Advanced Techniques](07-advanced-techniques.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/llama-factory-tutorial/07-advanced-techniques.md b/tutorials/llama-factory-tutorial/07-advanced-techniques.md
index f19bff6b..5058caef 100644
--- a/tutorials/llama-factory-tutorial/07-advanced-techniques.md
+++ b/tutorials/llama-factory-tutorial/07-advanced-techniques.md
@@ -628,3 +628,49 @@ Ready for the grand finale? In [Chapter 8: Production Case Studies](08-productio
 5. Implement meta-learning for few-shot adaptation
 
 *What's the most advanced technique you'll implement?* 🧠
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `model`, `tokenizer` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 7: Advanced Techniques` as an operating subsystem inside **LLaMA-Factory Tutorial: Unified Framework for LLM Training and Fine-tuning**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `loss`, `task`, `List` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 7: Advanced Techniques` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `model` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `tokenizer`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/hiyouga/LLaMA-Factory)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `model` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 6: Deployment](06-deployment.md)
+- [Next Chapter: Chapter 8: Production Case Studies](08-production-case-studies.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
diff --git a/tutorials/llama-factory-tutorial/08-production-case-studies.md b/tutorials/llama-factory-tutorial/08-production-case-studies.md
index 74e4d699..4e2da256 100644
--- a/tutorials/llama-factory-tutorial/08-production-case-studies.md
+++ b/tutorials/llama-factory-tutorial/08-production-case-studies.md
@@ -795,3 +795,48 @@ Remember, the most successful AI systems are those that provide real value to us
 5. Set up comprehensive monitoring and alerting
 
 *What's your next production AI project?* 🚀
+
+## What Problem Does This Solve?
+
+Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for `self`, `Step`, `dict` so behavior stays predictable as complexity grows.
+
+In practical terms, this chapter helps you avoid three common failures:
+
+- coupling core logic too tightly to one implementation path
+- missing the handoff boundaries between setup, execution, and validation
+- shipping changes without clear rollback or observability strategy
+
+After working through this chapter, you should be able to reason about `Chapter 8: Production Case Studies` as an operating subsystem inside **LLaMA-Factory Tutorial: Unified Framework for LLM Training and Fine-tuning**, with explicit contracts for inputs, state transitions, and outputs.
+
+Use the implementation notes around `prompt`, `code`, `models` as your checklist when adapting these patterns to your own repository.
+
+## How it Works Under the Hood
+
+Under the hood, `Chapter 8: Production Case Studies` usually follows a repeatable control path:
+
+1. **Context bootstrap**: initialize runtime config and prerequisites for `self`.
+2. **Input normalization**: shape incoming data so `Step` receives stable contracts.
+3. **Core execution**: run the main logic branch and propagate intermediate state through `dict`.
+4. **Policy and safety checks**: enforce limits, auth scopes, and failure boundaries.
+5. **Output composition**: return canonical result payloads for downstream consumers.
+6. **Operational telemetry**: emit logs/metrics needed for debugging and performance tuning.
+
+When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
+
+## Source Walkthrough
+
+Use the following upstream sources to verify implementation details while reading this chapter:
+
+- [View Repo](https://github.com/hiyouga/LLaMA-Factory)
+  Why it matters: authoritative reference on `View Repo` (github.com).
+
+Suggested trace strategy:
+- search upstream code for `self` and `Step` to map concrete implementation paths
+- compare docs claims against actual runtime/config code before reusing patterns in production
+
+## Chapter Connections
+
+- [Tutorial Index](index.md)
+- [Previous Chapter: Chapter 7: Advanced Techniques](07-advanced-techniques.md)
+- [Main Catalog](../../README.md#-tutorial-catalog)
+- [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)

From c53a0bd321cd18b57ec797b8ebbbb422aceb07a5 Mon Sep 17 00:00:00 2001
From: johnxie <john@taskade.com>
Date: Tue, 24 Feb 2026 12:29:09 -0800
Subject: [PATCH 2/2] docs: refresh tutorials README snapshot metrics

---
 tutorials/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/README.md b/tutorials/README.md
index fb3e8a28..4609447e 100644
--- a/tutorials/README.md
+++ b/tutorials/README.md
@@ -13,7 +13,7 @@ This file describes the structure of `tutorials/` and points to the authoritativ
 |:-------|:------|
 | Tutorial directories | 185 |
 | Tutorial markdown files | 1678 |
-| Tutorial markdown lines | 477,603 |
+| Tutorial markdown lines | 583,176 |
 
 ## Content Structure Patterns