An agent harness is the working structure around AI models. It sets the stages of work, decides which tools can be used, defines what must be saved and checked, and determines when the task is complete.

A simple way to frame it is if models are the engines, the harness is the system infrastructure that keeps them on track.

In practice, that means things such as:

• stages and sequencing
• tool use
• memory and saved state
• verification and repair
• delegation
• stopping rules

They matter because agent failures are not always failures of intelligence. They can also be failures of structure such as lost context, weak sequencing, poor checks, unclear roles, or no durable record of what has already been done.

It is the part that turns model capability into a controlled process. In a sense, it is an attempt to make non-deterministic systems more governable.

Natural-Language Agent Harnesses argues that while harness design has a major effect on agent performance, it is often buried in controller code and framework behaviour, which makes it hard to compare, improve, or reuse.

The paper proposes that the harness itself could become a more visible, editable artefact, written in structured natural language rather than hidden entirely in code.

This would make the harness easier to inspect, refine, and transfer across tasks.

Natural-Language Agent Harnesses https://arxiv.org/abs/2603.25723