Harrison Chase, co-founder and CEO of LangChain, says harness engineering is a crucial challenge in developing AI agents. A harness is an environment where an AI model can loop, call tools, and perform long-running tasks.
In the past, models struggled with loops because they weren’t powerful enough to run them reliably. However, recent advancements have improved this issue. To address it, LangChain released Deep Agents, a general-purpose harness that provides planning, file management, and more.
Deep Agents allow sub-agents to work in parallel using different tools and settings. This enables large-scale tasks to be processed efficiently, with results compressed into a single output. For complex 200-step tasks, Chase emphasizes the need for a structure that can track progress and maintain consistency, which is what harness engineering provides.
Source: https://www.digitaltoday.co.kr/en/view/28196/langchain-ceo-ai-agent-success-depends-on-context-engineering