The race to build reliable AI agents isn’t solely about larger, more sophisticated large language models (LLMs), according to Harrison Chase, co-founder and CEO of LangChain. Instead, he argues that the “harnesses” – the underlying infrastructure – that control and guide these models are becoming increasingly critical for moving AI applications from demonstration to production. This shift in focus, Chase explains, is about giving LLMs the autonomy to manage their own context and execute complex tasks over extended periods.
Chase’s insights come as the AI landscape rapidly evolves, with companies like OpenAI pushing the boundaries of model capabilities. However, he suggests that simply having a more powerful model isn’t enough. Successful deployment requires a robust “harness engineering” approach – an extension of context engineering – that allows agents to interact more independently and effectively. Here’s particularly relevant as enterprises look to operationalize AI agents and move beyond experimental phases.
The traditional approach to AI harnesses often constrained models, limiting their ability to run in loops or utilize tools. But Chase believes the trend is now toward granting LLMs greater control over their own context, enabling them to determine what information is relevant and when. “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 see,” Chase said. “Now, this idea of a long-running, more autonomous assistant is viable.”
The Pitfalls of Premature Autonomy
Although the concept of autonomous agents sounds straightforward, Chase cautions that it’s historically been difficult to achieve reliably. Early attempts, like the now-faded AutoGPT – once the fastest-growing GitHub project – demonstrated the architectural framework for today’s agents, but lacked the underlying model power to execute tasks consistently. “For a while, models were ‘below the threshold of usefulness’ and simply couldn’t run in a loop,” Chase noted, leading developers to rely on complex graphs and chains to work around the limitations.
However, with the continued improvement of LLMs, building environments where models can plan over longer horizons and continuously refine their processes is becoming increasingly feasible. Previously, Chase explained, “you couldn’t really develop improvements to the harness because you couldn’t actually run the model in a harness.”
LangChain’s Deep Agents: A Customizable Harness
LangChain’s response to this challenge is Deep Agents, a customizable, general-purpose harness built on LangChain and LangGraph. Deep Agents offers planning capabilities, virtual filesystem access, context and token management, code execution, and both skills and memory functions. A key feature is its ability to delegate tasks to specialized “subagents” that can operate in parallel, with isolated contexts to prevent interference and efficient compression of large subtask results.
These agents can create and track to-do lists, maintaining coherence across complex, multi-step processes. “When it goes on to the next step…it has a way to track its progress and keep that coherence,” Chase said. “It comes down to letting the LLM write its thoughts down as it goes along, essentially.” He emphasized the importance of designing harnesses that allow models to maintain context over extended tasks and proactively manage information.
Context Engineering: Understanding the LLM’s Perspective
Chase defines context engineering as understanding “What is the LLM seeing?” – a perspective that differs significantly from a developer’s view. Analyzing agent traces – the record of an agent’s decision-making process – allows developers to step into the AI’s “mindset” and assess the system prompt, tool availability, and how responses are presented. “When agents mess up, they mess up because they don’t have the right context; when they succeed, they succeed because they have the right context,” Chase stated.
Providing agents with access to code interpreters and BASH tools enhances flexibility, while equipping them with “skills” – on-demand information – rather than pre-loaded tools allows for a more dynamic and efficient approach. “So rather than hard code everything into one big system prompt,” Chase explained, “you could have a smaller system prompt, ‘This is the core foundation, but if I need to do X, let me read the skill for X. If I need to do Y, let me read the skill for Y.'”
The Future of AI Agent Development
As AI agents become more sophisticated, Chase anticipates the emergence of code sandboxes as a crucial security feature. He also foresees a shift in user experience (UX) to accommodate agents that operate over longer intervals or continuously. Observability and detailed tracing will remain core to building reliable and effective agents.
The development of robust harnesses, Chase argues, is not simply about improving existing AI models, but about creating an environment where those models can truly thrive. This focus on infrastructure and control is likely to shape the next phase of AI agent development, moving beyond impressive demos to practical, scalable applications.
What are your thoughts on the role of harness engineering in the future of AI? Share your insights in the comments below.
