We recently launched an AI agent internally after months of testing, and the results during development looked great. The problem is that once real employees started using it, the behavior became a lot less predictable. It handles some requests perfectly, but on others it takes unnecessary actions, misses important context, or follows workflows differently than expected. Every adjustment seems to improve one category of interactions while making another one worse. We're collecting plenty of real-world conversations now, but it's becoming difficult to evaluate whether changes are actually improving the agent overall or just shifting problems around. How are teams optimizing and measuring agent performance once they're getting large amounts of production data?

The gap between testing and production is one of those things that catches almost every team off guard. You spend months building test suites that cover what you think are the edge cases, and then real users find seventeen new ones in the first week. The core issue with what you're describing is that you're optimizing without a stable baseline to measure against. Before tweaking anything else, lock down a fixed evaluation set of real production conversations that represent the full range of what the agent handles, and score every version against that exact set. That way each change has a measurable before and after rather than just a gut feeling that something got better.

Production data is great for spotting patterns but without the right tooling it just piles up and becomes noise. The tricky part is that every team thinks they'll eventually sit down and properly analyze it, and then the next sprint starts and that analysis never happens. You can run your agent optimization here https://eignex.com/ . It gives you the structure to track behavior across versions and surface which changes are actually moving things in the right direction versus just reshuffling the same problems. Saves a lot of the manual work that usually falls through the cracks.

We've run into similar issues once an AI agent moved from controlled testing into real employee workflows. What helped most was grouping production conversations by task type, failure pattern, and business impact instead of judging performance from a few examples. I also found some useful frameworks on https://slotexo.com.pl/ for evaluating agent behavior with real-world data, especially around regression testing and human review. In my experience, the goal is not just to improve average performance, but to make sure fixes in one workflow do not quietly break another.