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+# MMA Data Architecture & DAG Engine
+
+Restructures manual_slop state and execution into a per-track DAG model.
+
+### Navigation
+- [Specification](./spec.md)
+- [Implementation Plan](./plan.md)
diff --git a/conductor/archive/mma_data_architecture_dag_engine/metadata.json b/conductor/archive/mma_data_architecture_dag_engine/metadata.json
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+{
+  "id": "mma_data_architecture_dag_engine",
+  "title": "MMA Data Architecture & DAG Engine",
+  "status": "planned",
+  "created_at": "2026-02-27T19:20:00.000000"
+}
diff --git a/conductor/archive/mma_data_architecture_dag_engine/plan.md b/conductor/archive/mma_data_architecture_dag_engine/plan.md
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+# 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]
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diff --git a/conductor/archive/mma_data_architecture_dag_engine/spec.md b/conductor/archive/mma_data_architecture_dag_engine/spec.md
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+# Track Specification: MMA Data Architecture & DAG Engine
+
+## Overview
+Restructure how `manual_slop` stores and executes work. The current implementation relies on global state and linear execution, which does not support the complexity of multi-agent, task-based workflows. This track establishes a robust, data-oriented foundation using track-scoped state and a native Python Directed Acyclic Graph (DAG) engine.
+
+## Goals
+1.  **Track-Scoped State:** Transition away from a single global `manual_slop_history.toml` to a per-track state structure (e.g., `tracks/<track_id>/state.toml`) to manage specific discussion history and context.
+2.  **Task DAG Engine:** Implement a topological sorter and DAG execution engine in Python to manage dependencies between tasks.
+3.  **Execution State Machine:** Build an internal state machine that governs whether the DAG auto-advances or waits for manual user intervention to spawn the next worker.
+
+## Constraints
+-   Must integrate seamlessly with the existing project TOML structure.
+-   Avoid external complex dependencies (like Steve Yegge's Beads) for now; rely on standard Python libraries or lightweight custom implementations to ensure tight coupling with the DearPyGui stack.
+
+## Context & Origins
+This track was born from the "Human Verification" phase of the initial MMA Orchestrator prototype (`mma_orchestrator_integration_20260226`). We realized that while the backend API plumbing for the hierarchical MMA tiers (Tiers 1-4) was technically functional, the product lacked the necessary state management, UX visualization, and human-in-the-loop security gates to be usable. 
+
+**Key Takeaways from the Prototype Phase:**
+- The Tier 2 (Tech Lead) needs its own track-scoped discussion history, rather than polluting the global project history.
+- Tasks within a track require a DAG (Directed Acyclic Graph) engine to manage complex dependencies and blocking states.
+- The GUI must visualize this DAG and stream the output of individual workers directly to their associated tasks.
+- We must enforce tiered context subsetting so that Tier 3/4 workers don't receive the massive global context blob, and we need a pre-spawn approval modal so the user can intercept, review, and modify worker prompts/contexts before they execute.
+
+**Instructions for the Implementing Agent:**
+As you execute this track, ensure you maintain alignment with the other Phase 2 tracks. If you learn something that impacts the dependent tracks, please append a similar "Context Summary" to their `spec.md` files before concluding your run.
+
+## Execution Order & Dependencies
+This is a multi-track phase. To ensure architectural integrity, these tracks **MUST** be executed in the following strict order:
+1. **[CURRENT] MMA Data Architecture & DAG Engine:** (Builds the state and execution foundation)
+2. **Tiered Context Scoping & HITL Approval:** (Builds the security and context subsetting on top of the state)
+3. **MMA Dashboard Visualization Overhaul:** (Builds the UI to visualize the state and subsets)
+4. **Robust Live Simulation Verification:** (Builds the tests to verify the UI and state)
+
+**Prerequisites for this track:** None. This must be executed FIRST.
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diff --git a/conductor/tracks.md b/conductor/tracks.md
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+++ b/conductor/tracks.md
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 ---
 
-- [x] **Track: MMA Data Architecture & DAG Engine**
-*Link: [./tracks/mma_data_architecture_dag_engine/](./tracks/mma_data_architecture_dag_engine/)*
-
----
-
 - [~] **Track: Tiered Context Scoping & HITL Approval**
 *Link: [./tracks/tiered_context_scoping_hitl_approval/](./tracks/tiered_context_scoping_hitl_approval/)*