LangChain's CEO argues that better models alone won't get your AI agent to production

As models get smarter and more capable, the "harnesses" around them must also evolve. This "harness engineering" is an extension of context engineering, says LangChain co-founder and CEO Harrison Chase in a new VentureBeat Beyond the Pilot podcast episode. Whereas traditional AI harnesses have tended to constrain models from running in loops and calling tools, harnesses specifically built for AI agents allow them to interact more independently and effectively perform long-running tasks.

Chase also weighed in on OpenAI's acquisition of OpenClaw, arguing that its viral success came down to a willingness to "let it rip" in ways that no major lab would — and questioning whether the acquisition actually gets OpenAI closer to a safe enterprise version of the product. “The trend in harnesses is to actually give the large language model (LLM) itself more control over context engineering, letting it decide what it sees and what it doesn't ...