docs(conductor): Synchronize docs for track 'True Parallel Worker Execution'

conductor/tech-stack.md

@@ -43,7 +43,8 @@
 - **Taxonomy & Artifacts:** Enforces a clean root by organizing core implementation into a `src/` directory, and redirecting session logs and artifacts to configurable directories (defaulting to `logs/sessions/` and `scripts/generated/`). Temporary test data and test logs are siloed in `tests/artifacts/` and `tests/logs/`.
 - **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.
-- **dag_engine.py:** A native Python utility implementing `TrackDAG` and `ExecutionEngine` for dependency resolution, cycle detection, transitive blocking propagation, 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. Refined to decouple status management from dependency resolution to support external pool control.
+- **multi_agent_conductor.py:** Orchestrates the concurrent execution of implementation tracks using a non-blocking `ConductorEngine` and a thread-safe `WorkerPool`. Employs configurable concurrency limits and thread-local context isolation to manage multi-agent state.
 - **Thread-Local Context Isolation:** Utilizes `threading.local()` for managing per-thread AI client context (e.g., source tier tagging), ensuring thread safety during concurrent multi-agent execution.
 - **Asynchronous Tool Execution Engine:** Refactored MCP tool dispatch and AI client loops to use `asyncio.gather` and `asyncio.to_thread`, enabling parallel execution of independent tool calls within a single AI turn to reduce latency.