What Is an Agent Harness?

An oversimplified but useful equation: agent harness ≈ ai agent − model.

Take the model out of an AI agent, and what’s left is the harness: the software that gives the model context, tools, state, permissions, and an environment to act in. A model can reason and generate text. A harness builds on top of that and does work over multiple steps.

A useful way to think about an agent harness is as a loop:

A query flows from the user through the context builder, the LLM, and tools and runtime, before the result comes back. Memory and skills both feed into the context builder, and tool results loop back to the context builder too. Observability watches every stage, and constraints limit the runtime.

The harness starts with the current state and a new observation, such as a user message or tool result.
The context builder selects the instructions, state, memory, skills, tool definitions, and other information the model needs for this step.
The model produces a proposed next action. This may be a tool call, a message, a state update, or a final answer.
The harness checks whether that action is allowed.
The runtime executes it.
The result becomes a new observation, the state is updated, and the loop continues until exit.

To sum up, The harness decides what the model can see, what actions to execute, what data flows to next step and when to stop the loop.

Further reading