## **1. Project Overview**

**Goal:**
Develop an **Intent-Code Traceability system** for the AI-Native IDE that ensures AI-generated code aligns with user intent and can be tracked, reasoned over, and verified.

**Core Features:**

* **Two-stage Reasoning Loop** (State Machine):

  * **Stage 1:** Capture client intent, map to AI code action.
  * **Stage 2:** Validate AI-generated code, detect misalignment, log corrections.
* **Hook System Integration**:

  * Identify injection points in **Roo Code** for tracking.
  * Pre-commit, post-commit, and runtime hooks for tracing execution.
* **`.orchestration/` directory**:

  * Stores intent metadata, execution logs, and reasoning states.
* **Intent-Code Mapping**:

  * Links user intent → AI agent decisions → generated code → execution results.
* **Auditability**:

  * Every code change is traceable to its originating intent.

---

## **2. Architecture Layers**

### **A. Input Layer (Intent Capture)**

* **Source:** User commands in the IDE, chat prompts, or code requests.
* **Components:**

  * Intent Parser (NLP model / regex-based)
  * Preprocessing Engine (normalize ambiguous input)
* **Output:** Structured intent objects (`JSON/YAML`).

### **B. Hook System Layer**

* **Integration Points:** Roo Code Extension

  * **Pre-commit hook:** Captures intent vs proposed AI code.
  * **Post-commit hook:** Logs executed code and execution result.
  * **Custom Reasoning hooks:** Intercepts AI agent output for validation.
* **Responsibilities:**

  * Validate AI output before commit.
  * Trigger state updates in Reasoning Loop.
  * Maintain orchestration logs.

### **C. Orchestration & Reasoning Layer**

* **State Machine (Two-Stage Loop)**:

  * **Stage 1: Intent → Proposed Code**

    * AI agent generates code based on captured intent.
    * Hook system verifies structure and alignment.
  * **Stage 2: Code Validation**

    * Execute test cases or lint checks.
    * Detect mismatches and suggest corrections.
* **Data Storage:** `.orchestration/` directory

  * Stores:

    * Intent metadata
    * AI decisions and reasoning traces
    * Validation results
    * Hook system logs

### **D. Storage & Traceability Layer**

* **File System:** `.orchestration/` for local tracking
* **Optional DB:** Lightweight database (SQLite/PostgreSQL) for:

  * Intent history
  * AI agent output logs
  * Validation state
* **Purpose:** Allows historical analysis and auditability.

### **E. Output & Feedback Layer**

* **Developer Feedback:**

  * Misalignment alerts
  * Suggested corrections
  * Intent-Code mapping visualizations
* **Metrics & Analysis:**

  * Traceability coverage
  * Reasoning loop success rate
  * Hook system performance

---

## **3. Development Plan / Workflow**

1. **Phase 0: Prep**

   * Review `ARCHITECTURE-NOTES.md` for Roo Code injection points.
   * Map the cognitive and trust debt decisions → reasoning logic.
   * Setup Git repo with **Git Speck Kit**.

2. **Phase 1: Hook System Implementation**

   * Identify Roo Code extension points for:

     * pre-commit
     * post-commit
     * runtime reasoning interception
   * Build hook scripts.
   * Unit test hooks independently.

3. **Phase 2: Reasoning Loop**

   * Implement two-stage state machine.
   * Connect hooks to Reasoning Loop states.
   * Implement intent validation logic.

4. **Phase 3: Orchestration Directory**

   * `.orchestration/` for:

     * intent.json
     * reasoning_state.json
     * validation_results.json
   * Implement read/write APIs for traceability.

5. **Phase 4: Logging & Traceability**

   * Implement audit logs for every hook event.
   * Integrate with Git Speck Kit for code snapshots.
   * Enable metrics collection for AI alignment tracking.

6. **Phase 5: Testing & Validation**

   * Create sample AI-generated code scenarios.
   * Test traceability pipeline end-to-end.
   * Measure coverage of intent-code alignment.

7. **Phase 6: Documentation**

   * Maintain `ARCHITECTURE_NOTES.md` and `README.md`.
   * Document hook usage, state machine, and orchestration structure.

---

## **4. Tech Stack / Tools**

* **Git & Git Speck Kit:** Source control, snapshots, hooks.
* **Python / Node.js:** For hooks and orchestration logic.
* **JSON/YAML:** Intent and traceability storage.
* **Roo Code Extension:** Injection points for hook system.
* **Lightweight DB (Optional):** SQLite or PostgreSQL for logs.
* **NLP / Parsing:** Optional intent parsing models.
* **Testing Frameworks:** pytest / Jest for automated validation.

---

## **5. Key Architectural Decisions (From Cognitive & Trust Debt)**

* Track only **AI-generated code relevant to intent** instead of all outputs.
* Enforce **two-stage validation loop** to prevent drift between intent and code.
* Maintain **self-contained orchestration directory** to simplify tracing and rollback.
* Use **hooks as checkpoints** rather than full code reviews to scale traceability.
* **Metrics-driven design:** Log reasoning steps to improve future AI alignment.