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* [831988ba] fix(lifecycle): add rejection_kind to Precondition, PRECONDITION_OWNERSHIP uses not_authorized Add rejection_kind: RejectionKind = 'tracing_gap' field to the Precondition frozen dataclass. PRECONDITION_OWNERSHIP now carries rejection_kind='not_authorized' so ownership failures surface as authorization issues rather than tracing gaps. Update _check_intent_preconditions to dispatch Decision.reject(kind='not_authorized') when the first failing precondition has rejection_kind='not_authorized' — for all other rejection_kinds the existing Decision.tracing_gap path applies. Update test_can_invoke_intent_open_pr_rejects_non_owner to assert not_authorized instead of tracing_gap (90 parity tests in test_lifecycle_consumer_parity.py now agree: choreographer and spec both return not_authorized for owned=False). All 4871 foundation tests pass, 3264 unit tests pass, ruff/mypy green. * [831988ba] docs(architecture): document preconditions and rejection kinds in lifecycle spec Add comprehensive guide explaining how Precondition rejection_kind field works in the lifecycle spec. Documents the distinction between tracing_gap (missing artifact) and not_authorized (identity/role boundary) rejections, includes the dispatch logic in _check_intent_preconditions, and explains agent-visible impact of the change. This context is essential for agents to understand why PRECONDITION_OWNERSHIP failures now return not_authorized instead of tracing_gap, and when to use each rejection_kind for new preconditions. --------- Co-authored-by: Backend Developer 1 <be-dev-1@agents.roboco.dev> Co-authored-by: Backend Documenter <be-doc@agents.roboco.dev>
RAG Knowledge Base Documentation
Optimized documentation for the RoboCo AI agent knowledge base. Each file is sized for effective RAG chunking.
Structure
docs/rag/
├── roles/ # Agent role responsibilities
├── workflows/ # Step-by-step task flows
├── standards/ # Coding, security, testing rules
├── architecture/ # System components
├── tools/ # MCP tools reference
└── troubleshooting/ # Common issues and fixes
Organization Principles
- One topic per file - Each file covers a single concept
- Chunk-friendly - Content fits in 512-1536 token chunks
- Self-contained - Each file provides complete context
- Actionable - Focus on what agents need to DO
For Agents
When searching the knowledge base:
- Use
roboco_kb_search()for semantic search - Use
roboco_rag_query()for AI-synthesized answers - Use
roboco_ask_mentor()for conversational help
See docs/rag/tools/kb-tools.md for the full KB tool reference.