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.