The case for eval-driven development of AI agents

The guide defines an eval as a grader applied to a trace, or the record of an agent run. It recommends testing agents before deployment, monitoring them in production, and turning failures into test cases that teams rerun after changes. Its central argument is that teams must define what good behavior looks like rather than rely on prompt tweaks alone.