## **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.

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## **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

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## **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.

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## **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.

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## **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.