Agentic Engineering

Engineering workflows where AI agents perform most implementation and humans enforce quality and risk controls.

When to Activate

• Setting up AI-assisted development workflows
• Planning agent task decomposition
• Optimizing model selection for cost/quality
• Reviewing AI-generated code

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 independently verifiable
• Each unit has a single dominant risk
• Each unit exposes a clear done condition

Model Routing

Tier	Use For	Cost
Haiku	Classification, boilerplate, narrow edits	Lowest
Sonnet	Implementation, refactors	Medium
Opus	Architecture, root-cause analysis, multi-file invariants	Highest

Escalate model tier only when lower tier fails with a clear reasoning gap.

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

Skip: Style-only disagreements when automated format/lint already enforces style.

Cost Discipline

Track per task:

• Model used
• Token estimate
• Retries
• Wall-clock time
• Success/failure