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docs(conductor): Synchronize docs for track 'MMA Dashboard Visualization Overhaul'

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Edward R. Gonzalez
2026-02-27 22:57:45 -05:00
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@@ -16,6 +16,10 @@ To serve as an expert-level utility for personal developer use on small projects
- **Tier 3 (Worker):** Surgical code implementation and TDD using `gemini-2.5-flash` or `deepseek-v3`. Operates statelessly with tool access and dependency skeletons. - **Tier 3 (Worker):** Surgical code implementation and TDD using `gemini-2.5-flash` or `deepseek-v3`. Operates statelessly with tool access and dependency skeletons.
- **Tier 4 (QA):** Error analysis and diagnostics using `gemini-2.5-flash` or `deepseek-v3`. Operates statelessly with tool access. - **Tier 4 (QA):** Error analysis and diagnostics using `gemini-2.5-flash` or `deepseek-v3`. Operates statelessly with tool access.
- **MMA Delegation Engine:** Route tasks, ensuring role-scoped context and detailed observability via timestamped sub-agent logs. Supports dynamic ticket creation and dependency resolution via an automated Dispatcher Loop. - **MMA Delegation Engine:** Route tasks, ensuring role-scoped context and detailed observability via timestamped sub-agent logs. Supports dynamic ticket creation and dependency resolution via an automated Dispatcher Loop.
- **MMA Observability Dashboard:** A high-density control center within the GUI for monitoring and managing the 4-Tier architecture.
- **Track Browser:** Real-time visualization of all implementation tracks with status indicators and progress bars.
- **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.
- **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, supporting automated topological sorting and robust cycle detection.
- **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).