Human judgment in the agent improvement loop

To build truly effective AI agents, it's crucial to capture the unwritten, tacit knowledge that lives inside human domain experts. This expert judgment helps refine key agent components, including its workflow design, the tools it uses, and the contextual information it receives for decision-making. The most successful teams achieve this through a rapid, iterative "improvement loop" of building an agent, deploying it to get user data, and then refining it based on that feedback. Instead of relying solely on slow manual reviews, the most scalable approach is to translate human insights into automated evaluations that can continuously test and improve the agent's performance.