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. ## 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.