Building a Production AI Agent in Spring Boot: A/B Testing Prompts With an LLM Judge (Part 9)

Last week I changed a system prompt based on a feeling. It was the first prompt change after the evaluation harness from Part 8 went live, and I was completely sure about it. The target was the markdown table. Part 8's first nightly run caught the agent answering price comparisons with a markdown table that renders broken in the chat frontend. The fix looked obvious: add one line to the system prompt demanding plain text. I checked six conversations by hand. All six looked better. I was ready to ship it to production. Then I ran the comparison the way Part 8 promised: the same 40 cases, the same judge, two prompts. The old prompt won. Not by a little. It won 18 pairs, lost 10, and tied 12, and the judge's rationales made the reason visible. The plain-text line had also made the agent terse, and terse answers dropped the order summary that customers actually need. My confidence was a sample size of one. The dataset was the jury. This part is about the pattern that settled that argument: pairwise comparison, the LLM-as-a-judge pattern for A/B testing prompts and tool descriptions before they reach production. It is the harness from Part 8, upgraded to answer "which version is better?" instead of "is this version good?" The Problem With Ship-by-Feeling Every prompt edit is an experiment with one sample. You notice one conversation where the agent is verbose, you add "be concise", and the change ships because that one conversation got better. The dataset from Part 8 makes the agent measurable, but a nightly score cannot tell you whether a change helped. One night is noise, three nights is a signal, and by the time you have three nights of data you have already shipped the change to every user. The variable itself is the problem. A system prompt and a tool description are the two things in an agent you cannot unit test. Part 6 proved the code is bug-free. Part 8 proved the answers are good on a fixed dataset. Neither says anything about whether your new wording is better