# Implementation Plan: MMA Data Architecture & DAG Engine

## Phase 1: Track-Scoped State Management
- [x] Task: Define the data schema for a Track (Metadata, Discussion History, Task List). [2efe80e]
- [x] Task: Update `project_manager.py` to create and read from `tracks/<track_id>/state.toml`. [e1a3712]
- [x] Task: Ensure Tier 2 (Tech Lead) history is securely scoped to the active track's state file. [b845b89]

## Phase 2: Python DAG Engine
- [x] Task: Create a `Task` class with `status` (Blocked, Ready, In Progress, Review, Done) and `depends_on` fields. [a3cfeff]
- [x] Task: Implement a topological sorting algorithm to resolve execution order. [f85ec9d]
- [x] Task: Write robust unit tests verifying cycle detection and dependency resolution. [f85ec9d]

## Phase 3: Execution State Machine
- [x] Task: Implement the core loop that evaluates the DAG and identifies "Ready" tasks. [154957f]
- [x] Task: Create configuration settings for "Auto-Queue" vs "Manual Step" execution modes. [154957f]
- [x] Task: Connect the state machine to the backend dispatcher, preparing it for GUI integration. [2429b7c]

## Phase: Review Fixes
- [x] Task: Apply review suggestions [6548ce6]