Route LLM tasks by output contract, failure cost, deadline, and trajectory signals, then optimize cost per completed task.
AI agent benchmark scores mix models, harnesses, tasks, tests, and environments. Use this framework to build a reproducible coding-agent eval.
A controlled method for making GPT-5.6 prompts leaner without deleting the constraints that keep production agents reliable.
Convert Claude Cookbook examples into versioned agent regression tests with deterministic assertions, repeated trials, cost budgets, and release gates