docs: Add Architecture.md for challenge documentation

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## **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.