What an AI agent harness actually does
An agent harness is the loop around the model — tool access, error recovery, staying on task. Why that layer decides whether AI agents are useful.
Most “AI agent” demos are a model plus a prompt. That holds up until the task has more than one step, a tool call fails, or the model wanders off. What carries an agent through real work isn’t the model — it’s the harness: the loop wrapped around it.
What a harness handles
The harness owns the unglamorous parts that decide whether an agent finishes the job:
- Tool exposure — which tools the agent can call, and how results come back to it.
- Error recovery — what happens when a call times out, fails, or returns garbage.
- Staying on task — keeping the model anchored to the goal across many turns.
- Control flow — when to stop, retry, escalate, or hand off to another agent.
Get the loop right once
Re-implementing that loop in every project is wasted work, and subtle bugs in it are what make agents feel flaky. Treat the harness as real infrastructure — build it once, make it reliable, reuse it — instead of hand-rolling a fragile loop per project.
Give agents a solid harness, good tools, and real access to the systems they work in, and they stop being a demo and start being infrastructure. That’s the whole bet.