How to build a reliable evaluation system for AI agents
Original: Do you need to learn how to properly evaluate your agent? Here’s a step-by-step guide for how to do this, informed by best practices in recent research…
Source: x.com ↗
Who: Posted by @cwolferesearch (Cameron R. Wolfe, AI researcher and writer known for applied deep learning explainers), sharing his own original guide on evaluating .
What's new: Wolfe lays out a six-step framework for reliably measuring whether an actually does its job, drawing on patterns he sees across current research. The core argument is that evaluation is not a one-time checklist but an ongoing engineering discipline that must grow alongside the agent itself.
How it works: The process starts by defining success in two layers: outcome goals (did the right thing happen, such as a database entry being created) and process goals (did the agent take the right steps along the way). From there, you build a small set of hand-picked test tasks, then keep adding harder ones whenever the agent fails in the real world. Grading starts with simple, deterministic checks — did it call the right tool, did it return the right answer — and escalates to or human review for anything subjective. All of this runs inside an , a controlled environment that mirrors real-world conditions as closely as possible.
Why it matters: Agent evaluations go stale fast — once a system passes all the tests, the tests stop being useful. Wolfe's key prescription is to treat the test suite as a living document, continuously refreshed with new failure cases and harder tasks, keeping older easier tests in a to catch backsliding.