The agent harness (and how to improve it)

Ship agents that stay safe and get better from real runs, not from model hype.

Define a harness, name its six layers, sketch a small harness, and run an improve loop using traces + golden checks.

The model gets the hype. The harness gets the results.

What you'll learn

Easy: a harness is everything around the model. An agent is a controlled workflow. The model is the smallest, swappable part. The recipe is instructions, scoped context, tools, a verifier, and guardrails.
Hard: six layers. Trigger, Orchestration, Tools, Trusted context (about 80% of success), Control, and Runtime. Model swap is a config change.
God: the harness is the moat. Production to traces to golden checks to a harness change. Write what good looks like before you fine-tune.

Notes that shaped this framing: Mark Ajzenstadt (@mardehaym) on harnesses. The rest of the course stays on a small harness you can improve from real runs.

The harness wraps the model. An agent is trusted context, bounded tools, evaluation, human judgment, and ops ownership.
Six layers: Trigger, Orchestration, Tools, Trusted context, Control, Runtime. Trusted context does most of the work.
A small harness has recipe instructions, a scoped context pack, fixed tools, a verifier, guardrails, and a loop limit.
Improve from production: read traces as receipts, add a golden check, change the harness, re-run the eval. Fine-tune later.

You can name what the harness owns and swap the model without rewriting the product.
You can sketch a tiny Python harness on a Mac or any CPU and grade it with a verifier.
You can turn a bad run into a golden case and a harness change, not a training job.
You can write the bar in the control layer, with a named owner and a stop-and-ask rule.