agentic-engineering

To automate engineering workflows using AI agents, provide a task outline to an agentic engineer to decompose work into verifiable units, route models by complexity, and execute iterative regression checks.

An eval-first execution loop defines capability and regression evaluations to run baselines before implementation, capturing failure signatures early and re-running evaluations after code changes to detect regressions.

Model routing assigns tasks by complexity: Haiku handles classification and boilerplate transforms, Sonnet executes implementations and refactors, while Opus manages architecture design and root-cause analysis to optimize cost.

Track AI agent cost metrics per task by monitoring the specific model utilized, token estimates, execution retries, wall-clock time, and the final success or failure status across different model tiers.

You can enforce security assumptions and error handling in AI-generated code by emphasizing invariants during the session strategy and compacting reviews after milestones to maintain human quality and risk controls.