Harrison Chase
Co-founder and CEO of LangChain, the AI agent-engineering company behind the LangChain framework, LangGraph, and LangSmith. He previously led machine-learning work at Robust Intelligence and Kensho.
Private Evals Determine When Agent Harnesses Need Customization
LangChain co-founder and chief executive Harrison Chase argues that an organization seeking to own its AI capability must control not only its models and context, but also the harness that determines what information reaches a model and what happens after it responds. He recommends starting with an off-the-shelf model-tool loop for work models already handle well, then adding middleware, controls or bespoke architectures as tasks become more domain-specific or require greater predictability. Private evaluations and production traces, he says, should determine which model-harness combinations improve accuracy, cost and reliability.
Enterprise AI Agents Need Harnesses, Traces, and Controlled Runtimes
LangChain co-founder and CEO Harrison Chase argues that enterprise AI agents are becoming an architectural problem rather than a question of adding autonomy wherever possible. In an NVIDIA AI Podcast interview, he says systems such as Claude Code, Manus and Deep Research share a common “deep agent” pattern: an LLM in a tool-calling loop, supported by a reusable harness, workspace, subagents and planning. For enterprises, Chase says trust depends on choosing the right level of autonomy and surrounding agents with observability, evaluation, secure runtimes and continued iteration.