agentic-engineering

# Agentic Engineering Use this skill for engineering workflows where AI agents perform most implementation work and humans enforce quality and risk controls. ## Operating Principles 1. Define completion criteria before execution. 2. Decompose work into agent-sized units. 3. Route model tiers by task complexity. 4. Measure with evals and regression checks. ## Eval-First Loop 1. Define capability eval and regression eval. 2. Run baseline and capture failure signatures. 3. Execute implementation. 4. Re-run evals and compare deltas. **Example workflow:** ``` 1. Write test that captures desired behavior (eval) 2. Run test → capture baseline failures 3. Implement feature 4. Re-run test → verify improvements 5. Check for regressions in other tests ``` ## Task Decomposition Apply the 15-minute unit rule: - Each unit should be independently verifiable - Each unit should have a single dominant risk - Each unit should expose a clear done condition **Good decomposition:** ``` Task: Add user authentication ├─ Unit 1: Add password hashing (15 min, security risk) ├─ Unit 2: Create login endpoint (15 min, API contract risk) ├─ Unit 3: Add session management (15 min, state risk) └─ Unit 4: Protect routes with middleware (15 min, auth logic risk) ``` **Bad decomposition:** ``` Task: Add user authentication (2 hours, multiple risks) ``` ## Model Routing Choose model tier based on task complexity: - **Haiku**: Classification, boilerplate transforms, narrow edits - Example: Rename ...