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

conductor/product.md

@@ -29,7 +29,8 @@ For deep implementation details when planning or implementing tracks, consult `d
     - **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.
-  - **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).
   - **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.