Building a Production AI Agent in Spring Boot: The LLM Judge That Scores Your Agent (Part 8)

Last week I ran a demo of the agent for a colleague who was deciding whether to bet a feature on it. The approval gate from Part 7 worked exactly as designed. The agent searched, added to cart, asked for the address, and stopped at the confirmation link. My colleague nodded and asked one question: "OK, but is it actually good?" I did not have an answer. I had 31 passing tests from Part 6, which prove the agent is bug-free. I had a state machine, which proves it cannot place an order without a human. Neither of those proves the agent answers customers well. A bug-free agent can still tell a customer the shop ships within two days when the shipping partner takes five. No unit test catches that, because no unit test reads the answer. That is the gap this part closes. Part 7 ended with a promise: the next part would build an evaluation harness, so "is it good" stops being a feeling and becomes a score. This is that part. I built an LLM-as-a-judge harness for the same e-commerce agent as Parts 1 through 7: same nine tools, same supervisor, same memory. It runs 40 real conversations from production logs against five metrics every night and prints a score for each one. The first run was uncomfortable, and that is exactly why it exists. I am a Senior Software Engineer II at BS23 in Dhaka, and I have been building production AI agents with Spring Boot and Spring AI for over a year. Everything below is the harness as I actually run it. The Difference Between Tested and Good Part 6 tested the agent without an LLM: 31 tests, zero model calls, asserting on tool calls, services, and the order state machine. That suite answers "did the agent call the right tool, in the right order, with the right arguments?" It cannot answer "was the answer right?" because you cannot write an assertion for an LLM's wording. Evaluation is a different layer. You cannot assert on the answer, but you can judge it, and you can use an LLM to do the judging. Spring AI documents this pattern in its LLM-as