Context Engineering Our Way to Long-Horizon Agents: LangChain’s Harrison Chase

Harrison Chase, cofounder of LangChain and pioneer of AI agent frameworks, discusses the emergence of long-horizon agents that can work autonomously for extended periods. Harrison breaks down the evolution from early scaffolding approaches to today's harness-based architectures, explaining why context engineering - not just better models - has become fundamental to agent development. He shares insights on why coding agents are leading the way, the role of file systems in agent workflows, and how building agents differs from traditional software development - from the importance of traces as the new source of truth to memory systems that enable agents to improve themselves over time. Hosted by Sonya Huang and Pat Grady 00:00 Introduction 01:54 Discussing Long Horizon Agents 03:00 Examples of Long Horizon Agents 04:56 Harness Engineering and Model Integration 07:09 Evolution of Agent Frameworks 18:22 Building Long Horizon Agents vs. Software 19:21 Understanding Non-Deterministic Systems 19:43 The Importance of Tracing in Lang Smith 20:44 Context Engineering and Its Significance 21:14 Testing and Collaboration in Agent Development 22:14 Iterative Nature of Building Agents 23:04 The Role of Memory in Agent Development 23:52 Challenges for Existing Software Companies 27:43 Human Judgment in Evaluating Agents 32:47 Future of Agent Development and Memory 34:37 Async and Sync Modes in Long Horizon Agents 37:29 The Role of Code Sandboxes and File Systems 38:51 Conclusion and Future Predictions