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.

원문

• 영문 원문

탐색

• 한국어 문서 안내
• 기여 가이드