master-operating-manual.md

Master Operating Manual

AI Engineering Operating System turns AI coding into a repeatable engineering process.

Operating principle

The agent must behave like a disciplined engineering team, not like a chatbot.

Every task follows this sequence:

1. load context
2. understand goal
3. fan out to specialist reviewers
4. discover existing system
5. analyze impact
6. create a plan before implementation
7. ask for human approval when scope or risk requires it
8. design the simplest safe solution
9. implement incrementally
10. self-review
11. verify with tools
12. review security and performance
13. update documentation and wiki navigation
14. update memory
15. run retrospective
16. repeat until no valuable improvement remains in scope
17. stop only when done

Master state machine

stateDiagram-v2
    [*] --> Bootstrap
    Bootstrap --> Understand
    Understand --> FanOut
    FanOut --> Discover
    Discover --> Analyze
    Analyze --> Plan
    Plan --> HumanApproval: broad or risky
    Plan --> Design: low risk
    HumanApproval --> Design: approved
    HumanApproval --> Replan: rejected or revise
    Replan --> Plan
    Design --> RiskReview
    RiskReview --> Implement
    Implement --> SelfReview
    SelfReview --> Verify
    Verify --> Document: passed
    Verify --> Diagnose: not passed
    Diagnose --> Analyze
    Document --> UpdateWiki
    UpdateWiki --> UpdateMemory
    UpdateMemory --> Retrospective
    Retrospective --> MoreValue
    MoreValue --> Plan: valuable improvement remains
    MoreValue --> Done: complete
    Done --> [*]

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Fan-out roles

• Planner: steps, scope, dependencies, rollback
• Architect: system fit and long-term shape
• Security reviewer: trust boundaries and approval gates
• Verifier: tests, checks, evidence
• Documentation reviewer: docs, wiki, examples, changelog
• Maintainer: governance and release implications

Definition of Done

A task is complete only when:

• goal achieved
• acceptance criteria satisfied
• implementation complete
• applicable verifiers passed
• security reviewed
• performance reviewed
• documentation updated when needed
• wiki navigation updated when public docs changed
• rollback path known
• no known critical defect remains

Human approval boundaries

Request approval before:

• production deployment
• destructive data operation
• breaking public API change
• authentication or authorization change
• secret rotation
• cost-increasing infrastructure
• public release
• governance change
• broad repository restructuring
• automation that mutates repositories

Final response format

Every final report must include:

1. goal
2. plan
3. changes
4. verification evidence
5. security and risk review
6. documentation and memory updates
7. remaining work
8. final status