When to Build Your Own Agent Harness | Harrison Chase, LangChain

Harrison Chase

Talks worth your time · AI in business · 2026-10-10 · 24 min · 11 pages

An agent is a harness orchestrating a model and context. If you want to own your intelligence, you probably want to own all three. LangChain co-founder and CEO Harrison Chase focuses on the harness: the loop that brings the right context to the model at the right time, and how to customize it with middleware, hooks, and sub-agents. At Sequoia Capital's Own Your IntelligenceI event, Harrison explains when an off-the-shelf harness is enough and when to build your own (the more out-of-distribution your task, the more customization you'll want) and why even custom harnesses should keep in-distribution pieces, like file editing, close to how each model was trained. He also covers building benchmarks, why observability is underrated for debugging agents, and the data flywheel that turns production traces into harness, model, and context improvements. 00:00 Introduction 00:58 The three parts of an agent: harness, model, context 02:12 What a harness actually does 03:25 Customizing the core loop with middleware 04:41 Sandboxes, file systems, sub-agents, summarization 05:47 Cognitive architectures — and when you still need them 07:03 Build your own harness or use off the shelf? 08:24 In-distribution vs. out-of-distribution: the file-editing example 09:39 Why evals define what "good" means in an organization 11:04 Harbor: what an eval task actually looks like 12:11 Comparing harnesses and models on accuracy, latency, and cost 13:20 Why observability is underrated — it's usually the co

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