Is Your AI Agent Eval Set Actually Testing Anything?

Is your AI agent production-ready?

You shipped it with an eval set of five examples, all of them the demo you already knew worked. Two weeks later production is full of failures none of those five would catch. So you patch the prompt, the demo still passes, and you have no idea whether you fixed the class of problem or just that one screenshot.

That is the question this series opened with. In let you assert on the property you care about: did it refuse the unsafe request, call the right tool, stay under budget, avoid inventing a policy. An eval you learn to ignore is worse than none, because it costs attention and returns false comfort.

If this sounds like curating good test data, that is exactly what it is. An eval set is test data for judgment, and the expensive part is the same as it is for any takes this into reliability: once you can measure the agent, how do you measure something that will not give the same answer twice.

Your prompt is disposable; your eval set is the asset.