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docs(conductor): Synchronize docs for track 'Architecture Boundary Hardening'

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Edward R. Gonzalez
2026-03-02 18:49:42 -05:00
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- **Hierarchical Task DAG:** An interactive, tree-based visualizer for the active track's task dependencies, featuring color-coded state tracking (Ready, Running, Blocked, Done) and manual retry/skip overrides. - **Hierarchical Task DAG:** An interactive, tree-based visualizer for the active track's task dependencies, featuring color-coded state tracking (Ready, Running, Blocked, Done) and manual retry/skip overrides.
- **Strategy Visualization:** Dedicated real-time output streams for Tier 1 (Strategic Planning) and Tier 2/3 (Execution) agents, allowing the user to follow the agent's reasoning chains alongside the task DAG. - **Strategy Visualization:** Dedicated real-time output streams for Tier 1 (Strategic Planning) and Tier 2/3 (Execution) agents, allowing the user to follow the agent's reasoning chains alongside the task DAG.
- **Track-Scoped State Management:** Segregates discussion history and task progress into per-track state files (e.g., `conductor/tracks/<track_id>/state.toml`). This prevents global context pollution and ensures the Tech Lead session is isolated to the specific track's objective. - **Track-Scoped State Management:** Segregates discussion history and task progress into per-track state files (e.g., `conductor/tracks/<track_id>/state.toml`). This prevents global context pollution and ensures the Tech Lead session is isolated to the specific track's objective.
- **Native DAG Execution Engine:** Employs a Python-based Directed Acyclic Graph (DAG) engine to manage complex task dependencies, supporting automated topological sorting and robust cycle detection. **Native DAG Execution Engine:** Employs a Python-based Directed Acyclic Graph (DAG) engine to manage complex task dependencies. Supports automated topological sorting, robust cycle detection, and **transitive blocking propagation** (cascading `blocked` status to downstream dependents to prevent execution stalls).
- **Programmable Execution State Machine:** Governing the transition between "Auto-Queue" (autonomous worker spawning) and "Step Mode" (explicit manual approval for each task transition). - **Programmable Execution State Machine:** Governing the transition between "Auto-Queue" (autonomous worker spawning) and "Step Mode" (explicit manual approval for each task transition).
- **Role-Scoped Documentation:** Automated mapping of foundational documents to specific tiers to prevent token bloat and maintain high-signal context. - **Role-Scoped Documentation:** Automated mapping of foundational documents to specific tiers to prevent token bloat and maintain high-signal context.
- **Tiered Context Scoping:** Employs optimized context subsets for each tier. Tiers 1 & 2 receive strategic documents and full history, while Tier 3/4 workers receive task-specific "Focus Files" and automated AST dependency skeletons. - **Tiered Context Scoping:** Employs optimized context subsets for each tier. Tiers 1 & 2 receive strategic documents and full history, while Tier 3/4 workers receive task-specific "Focus Files" and automated AST dependency skeletons.

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- **Taxonomy & Artifacts:** Enforces a clean root by redirecting session logs to `logs/sessions/`, sub-agent logs to `logs/agents/`, and error logs to `logs/errors/`. Temporary test data is siloed in `tests/artifacts/`. - **Taxonomy & Artifacts:** Enforces a clean root by redirecting session logs to `logs/sessions/`, sub-agent logs to `logs/agents/`, and error logs to `logs/errors/`. Temporary test data is siloed in `tests/artifacts/`.
- **ApiHookClient:** A dedicated IPC client for automated GUI interaction and state inspection. - **ApiHookClient:** A dedicated IPC client for automated GUI interaction and state inspection.
- **mma-exec / mma.ps1:** Python-based execution engine and PowerShell wrapper for managing the 4-Tier MMA hierarchy and automated documentation mapping. - **mma-exec / mma.ps1:** Python-based execution engine and PowerShell wrapper for managing the 4-Tier MMA hierarchy and automated documentation mapping.
- **dag_engine.py:** A native Python utility implementing `TrackDAG` and `ExecutionEngine` for dependency resolution, cycle detection, and programmable task execution loops. - **dag_engine.py:** A native Python utility implementing `TrackDAG` and `ExecutionEngine` for dependency resolution, cycle detection, transitive blocking propagation, and programmable task execution loops.
## Architectural Patterns ## Architectural Patterns