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169 lines
4.9 KiB
Markdown
169 lines
4.9 KiB
Markdown
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## **1. Project Overview**
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**Goal:**
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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.
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**Core Features:**
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* **Two-stage Reasoning Loop** (State Machine):
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* **Stage 1:** Capture client intent, map to AI code action.
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* **Stage 2:** Validate AI-generated code, detect misalignment, log corrections.
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* **Hook System Integration**:
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* Identify injection points in **Roo Code** for tracking.
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* Pre-commit, post-commit, and runtime hooks for tracing execution.
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* **`.orchestration/` directory**:
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* Stores intent metadata, execution logs, and reasoning states.
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* **Intent-Code Mapping**:
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* Links user intent → AI agent decisions → generated code → execution results.
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* **Auditability**:
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* Every code change is traceable to its originating intent.
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---
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## **2. Architecture Layers**
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### **A. Input Layer (Intent Capture)**
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* **Source:** User commands in the IDE, chat prompts, or code requests.
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* **Components:**
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* Intent Parser (NLP model / regex-based)
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* Preprocessing Engine (normalize ambiguous input)
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* **Output:** Structured intent objects (`JSON/YAML`).
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### **B. Hook System Layer**
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* **Integration Points:** Roo Code Extension
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* **Pre-commit hook:** Captures intent vs proposed AI code.
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* **Post-commit hook:** Logs executed code and execution result.
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* **Custom Reasoning hooks:** Intercepts AI agent output for validation.
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* **Responsibilities:**
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* Validate AI output before commit.
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* Trigger state updates in Reasoning Loop.
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* Maintain orchestration logs.
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### **C. Orchestration & Reasoning Layer**
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* **State Machine (Two-Stage Loop)**:
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* **Stage 1: Intent → Proposed Code**
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* AI agent generates code based on captured intent.
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* Hook system verifies structure and alignment.
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* **Stage 2: Code Validation**
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* Execute test cases or lint checks.
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* Detect mismatches and suggest corrections.
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* **Data Storage:** `.orchestration/` directory
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* Stores:
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* Intent metadata
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* AI decisions and reasoning traces
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* Validation results
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* Hook system logs
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### **D. Storage & Traceability Layer**
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* **File System:** `.orchestration/` for local tracking
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* **Optional DB:** Lightweight database (SQLite/PostgreSQL) for:
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* Intent history
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* AI agent output logs
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* Validation state
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* **Purpose:** Allows historical analysis and auditability.
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### **E. Output & Feedback Layer**
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* **Developer Feedback:**
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* Misalignment alerts
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* Suggested corrections
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* Intent-Code mapping visualizations
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* **Metrics & Analysis:**
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* Traceability coverage
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* Reasoning loop success rate
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* Hook system performance
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---
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## **3. Development Plan / Workflow**
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1. **Phase 0: Prep**
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* Review `ARCHITECTURE-NOTES.md` for Roo Code injection points.
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* Map the cognitive and trust debt decisions → reasoning logic.
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* Setup Git repo with **Git Speck Kit**.
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2. **Phase 1: Hook System Implementation**
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* Identify Roo Code extension points for:
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* pre-commit
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* post-commit
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* runtime reasoning interception
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* Build hook scripts.
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* Unit test hooks independently.
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3. **Phase 2: Reasoning Loop**
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* Implement two-stage state machine.
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* Connect hooks to Reasoning Loop states.
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* Implement intent validation logic.
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4. **Phase 3: Orchestration Directory**
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* `.orchestration/` for:
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* intent.json
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* reasoning_state.json
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* validation_results.json
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* Implement read/write APIs for traceability.
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5. **Phase 4: Logging & Traceability**
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* Implement audit logs for every hook event.
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* Integrate with Git Speck Kit for code snapshots.
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* Enable metrics collection for AI alignment tracking.
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6. **Phase 5: Testing & Validation**
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* Create sample AI-generated code scenarios.
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* Test traceability pipeline end-to-end.
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* Measure coverage of intent-code alignment.
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7. **Phase 6: Documentation**
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* Maintain `ARCHITECTURE_NOTES.md` and `README.md`.
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* Document hook usage, state machine, and orchestration structure.
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---
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## **4. Tech Stack / Tools**
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* **Git & Git Speck Kit:** Source control, snapshots, hooks.
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* **Python / Node.js:** For hooks and orchestration logic.
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* **JSON/YAML:** Intent and traceability storage.
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* **Roo Code Extension:** Injection points for hook system.
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* **Lightweight DB (Optional):** SQLite or PostgreSQL for logs.
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* **NLP / Parsing:** Optional intent parsing models.
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* **Testing Frameworks:** pytest / Jest for automated validation.
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---
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## **5. Key Architectural Decisions (From Cognitive & Trust Debt)**
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* Track only **AI-generated code relevant to intent** instead of all outputs.
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* Enforce **two-stage validation loop** to prevent drift between intent and code.
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* Maintain **self-contained orchestration directory** to simplify tracing and rollback.
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* Use **hooks as checkpoints** rather than full code reviews to scale traceability.
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* **Metrics-driven design:** Log reasoning steps to improve future AI alignment.
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