agentic-engineering

To decompose AI engineering tasks safely, apply the 15-minute unit rule, breaking work into verifiable chunks that ensure risk containment and facilitate an eval-first execution loop before full deployment.

The eval-first loop for AI workflows is a testing process that defines capability and regression evals to catch failure signatures early, ensuring safety and quality controls are validated before agents fully implement code.

Route AI models by task complexity using tiered model routing: Haiku for classification and boilerplate, Sonnet for implementation and refactors, and Opus for architecture and root-cause analysis, tracking tokens and wall-clock time.

Yes, agentic engineering is designed for multi-step refactoring projects and architecture assessments, balancing rapid iteration with safety through risk-aware routing, session continuity, and guardrails across closely-coupled tasks.

Code review for AI-generated code should focus on verifying invariants, error handling, security, and reliability, ensuring the automated implementation meets quality standards before completing the task decomposition cycle.

Maintain session continuity across AI agent tasks by preserving state during closely-coupled work, then resetting the session strategy after major milestones to ensure clean context for new architectural assessments.