How to Build Agents That Improve Over Time: Model, Harness, Context | Interrupt London

Harrison Chase, Co-Founder and CEO of LangChain, splits an agent into three parts, the model, the harness, and the context, and explains how each one can improve over time: the model's weights through supervised fine-tuning with LangSmith Fine-Tuning and its smithtune CLI, or through RL for more complex tasks; the harness through LangSmith Engine, a deep agent that runs over traces, clusters issues, and proposes fixes with a human in the loop; and the context through memory, where he points to Hex and Profound as companies he thinks do this well and to LangChain's open source OpenWiki project.