agentic-engineering

Yes, you can route tasks through tiered models like Haiku for boilerplate, Sonnet for implementation, and Opus for root-cause analysis. This model-routing strategy matches computational effort to task complexity, optimizing cost and performance.

An eval-first loop for AI workflow quality control is a governance process that baselines, executes, and re-evaluates tasks to ensure regression safety. It applies continuous evaluations to guard quality and enforce risk controls across iterations.

You track token usage and model costs for agentic workflows by measuring model usage, retries, and outcomes. This cost-aware routing measurement supports deterministic improvement by mapping resource consumption directly to workflow results.

Yes, you can use agentic engineering pipelines for tasks requiring human review by structuring tasks so humans evaluate critical decisions while AI handles repeatable implementation. This ensures risk controls are enforced at key decision points.

A limitation of tiered model routing is the overhead of matching model tiers to task complexity. If tasks are misclassified, routing to lower-tier models for complex refactors can cause quality regressions that eval-first loops must catch.