Interrupt NYC: Opening Keynote

Harrison Chase, Co-Founder and CEO of LangChain, uses examples from Rogo, Harvey, and JPMorgan Chase to explain owning your intelligence, which means building domain-specific pieces in and around the model, and the three pillars it takes: an open, model-neutral harness such as LangGraph or Deep Agents, a loop that compounds what you learn from how people use your agents, and governance for internal agents. He then walks through the LangSmith platform layer by layer: a new Managed Deep Agents release with auth primitives, user-level memory, and built-in web search via Parallel, decision models like Jev and SemIf through the LangSmith LLM Gateway, and three launches for observability and evals: LangSmith Trajectories, LangSmith Fine-Tuning with the smithtune CLI, and LangSmith Custom Apps. He closes with LangSmith Engine v2, which adds red teaming and tests its own fixes on LangSmith Deployment, and shares that Engine has scanned over 70 million traces and detected over 21,000 issues. This session was captured at Interrupt New York City, the agent conference by LangChain. LangChain products in this talk: LangSmith, LangSmith Engine, Managed Deep Agents, Deep Agents, LangGraph, LangSmith Deployment, LangSmith Sandboxes, LangSmith LLM Gateway, LangSmith Trajectories, LangSmith Fine-Tuning, LangSmith Custom Apps, SmithDB Speakers: Harrison Chase, Co-Founder and CEO, LangChain Chapters: 0:00 How agents went from prototypes to the application layer 2:43 Why teams want to own their intelligence now 4:25 What it takes to own your intelligence 7:22 How to organize your people and process around agents 9:20 What LangChain builds for runtime, evals, and intelligence 12:42 How Managed Deep Agents handles auth, memory, and web search 15:51 What a decision model like Jev is good for 18:00 Routing every model call through the LangSmith LLM Gateway 19:50 Why trajectories make agent debugging faster 22:57 Turning trajectories into optimized models with smithtune 24:44 Building custom UIs on your LangSmith data 26:03 How LangSmith Engine clusters issues and proposes fixes 28:04 How Engine v2 tests its fixes and red teams your agent 30:45 What LangChain wants Engine to take on next Resources: → LangSmith Engine: https://www.langchain.com/langsmith/engine → Managed Deep Agents: https://docs.langchain.com/langsmith/python/managed-deep-agents-overview → LangSmith Fine-Tuning (smithtune): https://docs.langchain.com/langsmith/smithtune → LangSmith: https://www.langchain.com/langsmith → Interrupt, the agent conference by LangChain: https://interrupt.langchain.com