agentic-engineering

name agentic-engineering description Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when AI agents perform most implementation work and humans enforce quality and risk controls. metadata {"origin":"ECC"} 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: 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 variable, add type annotation, format code Sonnet : Implementation and refactors Example: Implement feature, refactor module, write tests Opus : Architecture, root-cause analysis, multi-file invariants Example: Design system, debug complex issue, review architecture Cost discipline: Escalate model tier only when lower tier fails with a clear reasoning gap. Session Strategy Continue session for closely-coupled units Example: Implementing related functions in same module Start fresh session after major phase transitions Example: Moving from implementation to testing Compact after milestone completion , not during active debugging Example: After feature complete, before starting next feature Review Focus for AI-Generated Code Prioritize: Invariants and edge cases Error boundaries Security and auth assumptions Hidden coupling and rollout risk Do not waste review cycles on style-only disagreements when automated format/lint already enforce style. Review checklist: Edge cases handled (null, empty, boundary values) Error handling comprehensive Security assumptions validated No hidden coupling between modules Rollout risk assessed (breaking changes, migrations) Cost Discipline Track per task: Model tier used Token estimate Retries needed Wall-clock time Success/failure outcome Example tracking: Task: Implement user login Model: Sonnet Tokens: ~5k input, ~2k output Retries: 1 (initial implementation had auth bug) Time: 8 minutes Outcome: Success When to Use This Skill Managing AI-driven development workflows Planning agent task decomposition Optimizing model tier selection Implementing eval-first development Reviewing AI-generated code Tracking development costs Integration with Other Skills tdd-workflow : Combine with eval-first loop for test-driven development verification-loop : Use for continuous validation during implementation search-first : Apply before implementation to find existing solutions coding-standards : Reference during code review phase