Most teams evaluate their AI agent the way they'd grade a chatbot: does the final answer look right? That question misses almost everything that goes wrong in production, because an agent can land on the correct answer while taking a dangerous or expensive path to get there.
Score the Steps, Not Just the Answer
An agent that pulls the wrong record, retries three times, and burns unnecessary API calls before landing on a correct output is not actually working, even though the eval would mark it a pass. A proper framework for how to evaluate AI agents scores the intermediate steps, tool calls, retries, and decision points, not just whether the last message looked reasonable.
Build the Eval Set Before the Agent
The eval set should exist before serious development starts, not get bolted on right before launch. It needs real cases pulled from actual historical tickets or transactions, including the annoying edge cases nobody wants to deal with, since those are exactly what breaks an agent in the wild.
Gate Every Deploy, Not Just the First One
This is also where a lot of teams stop too early. The eval suite isn't a one-time gate before initial launch, it's a permanent part of the release process, the same way unit tests don't get deleted after the first deploy. The full AI agent implementation checklist treats evaluation as a recurring gate on every change to the agent, not a box you check once.
The teams that get burned by agentic AI are rarely the ones with a bad model. They're the ones who skipped building a real test plan and found out about the gaps from a customer instead.
Top comments (0)