agentic-engineering

Manage AI agent costs and quality by using eval-first execution, task decomposition, and cost-aware model routing. This approach routes work to different model tiers based on complexity, enforcing regression evals and human risk controls before implementation.

Eval-first development for AI-generated code defines capability evaluation and regression checks before implementation begins. This mechanism ensures invariants, edge cases, and security risk controls are verified against unit decomposition prior to agent execution.

Decompose large development workflows by breaking work into independently verifiable agent-sized units. This task decomposition strategy supports continuing sessions for closely-coupled tasks and fresh starts after milestones to maintain execution safety.

You can route AI engineering tasks to different model tiers using cost-aware model routing. Tasks are distributed across Haiku, Sonnet, or Opus tiers based on complexity, tracking model usage, retries, and wall-clock time to manage spending and risk.

The best way to review AI-generated code is to emphasize invariants, edge cases, security, and risk controls. Using an eval-first loop ensures regression checks are defined before implementation, maintaining quality and governance across complex projects.