How to Build a Self-Improving LangChain Agent

CopilotKit’s tutorial describes how to make an existing LangChain agent improve from user conversations using CopilotKit Intelligence, Product Insights, Automatic Learning, and Skills. The process involves connecting the application to Intelligence, creating a focused Learning space, importing prior conversations from a LangGraph Server or LangSmith Deployment, and selecting relevant threads that contain recurring requests, corrections, or workflow friction. Automatic Learning analyzes these conversations manually at first and then nightly when enough eligible threads exist, producing Product Insights for product decisions and proposed reusable Skills for agent behavior. Developers review evidence and approve Skills before enabling delivery to the LangChain agent through middleware, allowing published instructions to become available in future invocations without restarting the application. The tutorial also recommends testing a fresh request to confirm that the agent loads and applies an approved Skill, while continuing to monitor proposed lessons and use conversation evidence to guide both agent and product improvements.

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