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 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. 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 Model Routing Haiku: classification, boilerplate transforms, narrow edits Sonnet: implementation and refactors Opus: architecture, root-cause analysis, multi-file invariants Session Strategy Continue session for closely-coupled units. Start fresh session after major phase transitions. Compact after milestone completion, not during active debugging. 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. Cost Discipline Track per task: model token estimate retries wall-clock time success/failure Escalate model tier only when lower tier fails with a clear reasoning gap.