agentic-engineering

Eval-first execution in AI-assisted software development requires defining capability and regression checks before changing code. This approach ensures implementation is faster without losing quality, by requiring explicit completion criteria and iterative evaluation throughout the engineering process.

Task decomposition for multi-file refactoring breaks engineering work into small, independently verifiable units with clear done conditions. This disciplined approach coordinates AI agents to maintain session organization across milestones while validating behavior with targeted evals.

Model routing uses the right model tier for each task while concentrating review on invariants, security, and hidden coupling. This cost-aware execution strategy ensures AI-assisted engineering maintains risk control and measurable outcomes during implementation.

Agentic engineering applies to root-cause analysis and regression validation by requiring iterative evaluation and review of edge cases. It coordinates AI agents to validate behavior with targeted evals, ensuring explicit completion criteria are met before implementation concludes.

The best way to break implementation work into verifiable tasks is eval-first planning with explicit completion criteria. This disciplined task decomposition creates small units of work that allow AI agents to validate behavior independently across multi-file changes.

AI agents for multi-file changes require explicit completion criteria and iterative evaluation to be effective. Without eval-first planning and cost-aware model routing, the risk of losing quality control and missing hidden coupling in security assumptions increases significantly.