What is an agent harness?

AI agents have evolved beyond simple standalone models to become part of a complex ecosystem that includes tools, memory stores, and orchestrated workflows, enabling them to perform intricate tasks. Central to this development is the concept of the "agent harness," a software infrastructure that surrounds a large language model (LLM) and manages everything except the model itself, such as intent capture, execution, and persistence. This harness connects AI models to the outside world, allowing them to use tools, remember information across sessions, and interact with complex environments. The emergence of harnesses has been driven by the need to address limitations of LLMs, such as fixed context windows and the inability to perform external actions, by integrating tool use and external systems. Harnesses enhance AI capabilities by providing structure, maintaining continuity over long tasks, and ensuring reliable and safe outputs, all without altering the LLM's internal training. This architecture allows for greater task success, efficiency, and the ability to extend functionalities without the need for retraining, making harness engineering a critical component of modern AI system design.

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