agentic-engineering

Eval-first task decomposition is an agentic engineering approach that splits complex software development into independent, verifiable units. It enforces explicit capability and regression evaluations upfront, ensuring safer and more controllable AI-driven code generation.

You route AI automation tasks using a tiered model routing scheme. This cost-aware approach directs tasks to Haiku, Sonnet, or Opus level models based on complexity, balancing performance and cost tracking across multi-step development pipelines.

To manage risk in AI-automated software development, apply guardrails that enforce explicit capability evaluation and regression checks. This eval-first workflow verifies outcomes for each decomposed task, maintaining control during end-to-end production pipeline execution.

Yes, you can use this agentic workflow for automated code generation in production pipelines. It coordinates end-to-end from concept to validated implementation, applying session management and cost tracking to ensure safe, verified outcomes.

The best way to verify AI-generated architecture designs is using an eval-first evaluation process. By breaking work into verifiable units and enforcing regression checks, you maintain controllable development and validate outcomes before deployment.