# Review by Andrej Karpathy (Round 2)

## Review Complete

I've completed my review of the Parallel Implement Mode implementation from the perspective of Andrej Karpathy (AI Engineering & LLM Applications).

### Summary

**VERDICT: approve**

The implementation is solid from an AI engineering perspective. The key strengths:

1. **Prompts treated as programs**: Clear instruction templates with explicit variable binding (`{task_id}`, `{prd_path}`), scoped constraints, and structured context injection
2. **Right level of autonomy**: Each agent gets isolated scope (one task, one worktree), preventing agents from stomping on each other
3. **Structured data handoffs**: Uses typed dataclasses and JSON artifacts rather than hoping models output parseable text
4. **Blast radius containment**: Per-task budgets, merge locks with timeouts, graceful degradation
5. **Explicit failure modes**: CircularDependencyError, WorktreeError, MergeLockTimeout all clearly defined

### Key Findings

- **[conflict_resolve.md]**: Lacks structured output for test verification - agent could hallucinate "tests pass"
- **[parallel_orchestrator.py]**: Manual conflict strategy raises exception but caller behavior is underspecified (PRD noted this as open question)
- **[dag.py]**: Regex parsing could handle malformed annotations more gracefully
- **[parallel_orchestrator.py]**: No retry semantics for transient failures

### Final Assessment

All 1399 tests pass, all PRD requirements (FR-1 through FR-13) are implemented, and the code follows existing project conventions. The graceful degradation to sequential mode is particularly important - it means this feature can be enabled by default without risk. The main gaps are around the conflict resolution path and observability, which are polish items for future iterations rather than blockers.
