Agentic Engineering
Use this skill for engineering workflows where AI agents perform most implementation work and humans enforce quality and risk controls.
Operating Principles
- Define completion criteria before execution.
- Decompose work into agent-sized units.
- Route model tiers by task complexity.
- Measure with evals and regression checks.
Eval-First Loop
- Define capability eval and regression eval.
- Run baseline and capture failure signatures.
- Execute implementation.
- 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:…
