While working on another AI agent, I realized something that should have been obvious: most teams still don’t treat evaluation as a core part of the development process.
It’s a bit like unit testing in traditional software development: “We’ll do it later,” or “It only slows us down.” Yet with AI agents, no evaluation means no progress.
Without systematic evaluation, we can’t tell whether an agent behaves reliably, answers the same questions consistently, or handles edge cases correctly. It’s not just about catching major errors. It’s about measuring the quality and consistency of its behavior, because that’s what allows us to make informed improvements.
I’ve also noticed how much evaluation changes conversations within an organization. Instead of guessing what went wrong, we have concrete data, charts, and specific examples. Meetings stop revolving around gut feelings and start focusing on facts. Only then can we see how the agent actually performs and where it needs improvement.
New technologies aren’t the problem in themselves. The problem is that we often lack the tools to keep them under control. Evaluation is one such tool: a way to bring order to the chaos of language models.
Without it, agent development is guided by intuition alone.
With it, development becomes a process.