docs(conductor): Synchronize docs for track 'Asynchronous Tool Execution Engine'.

conductor/tech-stack.md

@@ -42,11 +42,12 @@
 - **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.
 - **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.
 
 ## Architectural Patterns
 
 - **Event-Driven Metrics:** Uses a custom `EventEmitter` to decouple API lifecycle events from UI rendering, improving performance and responsiveness.
-- **Asynchronous Event Bus:** Employs an `AsyncEventQueue` based on `asyncio.Queue` to manage the communication between the UI and the backend multi-agent orchestrator without blocking.
+- **Synchronous Event Queue:** Employs a `SyncEventQueue` based on `queue.Queue` to manage communication between the UI and backend agents, maintaining responsiveness through a threaded execution model.
 - **Synchronous IPC Approval Flow:** A specialized bridge mechanism that allows headless AI providers (like Gemini CLI) to synchronously request and receive human approval for tool calls via the GUI's REST API hooks.
 - **Interface-Driven Development (IDD):** Enforces a "Stub-and-Resolve" pattern where cross-module dependencies are resolved by generating signatures/contracts before implementation.