agentic-engineering

Topics in the source

• Agentic Engineering
• Operating Principles
• Eval-First Loop
• Task Decomposition
• Model Routing
• Session Strategy
• Review Focus for AI-Generated Code
• Cost Discipline

Use this skill for engineering workflows where AI agents perform most implementation work and humans enforce quality and risk controls. 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. 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: 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: Bad decomposition: 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. Continue session for closely

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