agentic-engineering

You coordinate AI agents for software engineering workflows by decomposing tasks into independently verifiable units and routing them by model tiers to manage risk and cost.

Eval-first verification is an approach where you define capability and regression evals, run baselines, and capture failure signatures to validate AI agent changes early in the engineering process.

You manage AI model routing costs by tracking model usage, tokens, retries, and wall-clock time to decide when to escalate tasks across tiered models like Haiku, Sonnet, and Opus.

You decompose software tasks for AI agents by breaking work into independently verifiable units that include clear risk assessments and defined done criteria before implementation.

You enforce quality guardrails for AI coding sessions by maintaining focused sessions across milestones and applying thorough code review checks to ensure security and capability standards.

Yes, you can apply eval-first agents to existing projects by initializing a workflow that sets up baseline evals and routing rules for the current tasks requiring implementation.