LangChain CEO Harrison Chase explains when agent teams should customize their harness

At Sequoia Capital’s Own Your Intelligence event, LangChain co-founder and CEO Harrison Chase explains the agent harness: the loop that brings context to a model, invokes tools and feeds results back. His practical rule is to start with a general-purpose harness when a task resembles what the model was trained to do, then add middleware, model-specific behavior or a more controlled cognitive architecture as the work moves out of distribution. He connects that choice to private evaluations and detailed traces, which let teams compare accuracy, latency and cost, diagnose context failures, and turn production feedback into improvements to the harness, model or context. The talk matters because it gives teams a concrete way to decide what agent infrastructure they should own instead of treating every workload as either fully off the shelf or fully custom.