agentic-engineering

To enforce test-driven validation for AI-assisted code refactoring, you need an eval-first approach that runs baseline and regression tests before and after implementation. This structured workflow minimizes wasted effort by verifying every code change against defined success criteria.

The best way to decompose complex software engineering tasks for AI agents is to break requirements into verifiable 15-minute units. This task decomposition maintains focus and clarity while enforcing rigorous quality controls for unit verification.

Model routing optimizes cost and performance by matching specific engineering tasks to the appropriate model tier, such as Haiku, Sonnet, or Opus. This cost-aware execution strategy routes complex architectural analysis to the most capable models.

You need an eval-first execution loop for software development when minimizing wasted effort and reducing implementation risk in AI-assisted development. This structured approach ensures every code change is validated against baseline tests before and after implementation.

You can use task decomposition and model routing for legacy module refactoring by breaking the refactor into testable units and routing the architectural analysis to the most capable model tier. This enforces rigorous quality controls throughout the development cycle.