Human judgment in the agent improvement loop

Rahul Verma, a Deployed Engineer at LangChain, emphasizes the importance of incorporating both documented and tacit human knowledge into AI agents to improve their performance and reliability. Using a financial services firm's "Copilot for traders" as a real-life example, the text illustrates how AI agents can automate workflows, like generating SQL queries, to free up data scientists and provide traders with quicker responses. To ensure these AI systems work effectively, they must integrate both financial domain knowledge and technical database insights, requiring input from domain experts. The text outlines a comprehensive approach to designing AI agents, including using deterministic code for critical steps, configuring tools with the right parameters, and employing context engineering for better information retrieval. It highlights the significance of incorporating human judgment into an iterative improvement loop involving development, monitoring, and testing, using automated evaluations aligned with expert judgment to efficiently refine agent performance. The LangSmith platform is mentioned as a tool to facilitate this process by providing features like Align Evaluator, annotation queues, and Insights Agent to gather real-time data and insights, ultimately creating a continuous cycle of improvement that leverages human expertise and automated evaluations to enhance AI agent functionality.