Agent Harness

An agent without a harness is just a demo with confidence.

What makes an AI agent usable in production is not the prompt.
It’s the harness around it.

In practice, that means designing the layers the prototype usually ignores:

• API contracts for tools and downstream systems
• input/output validation and schema enforcement
• retrieval and data freshness checks
• evaluation against real task traces, not just synthetic examples
• monitoring for latency, token spend, tool failures, and drift
• security boundaries, permissioning, and audit logs
• fallback paths when the model is wrong, slow, or unavailable

The hard part is that these layers trade off against each other.

Tighter validation improves reliability, but can reduce flexibility.
More tools increase capability, but also failure surface.
Heavier guardrails improve safety, but can hurt task completion.
Better observability costs more, but is the only way to debug behavior in production.

That is why I think “agent engineering” is really harness engineering.

If you are building agents for real users, the question is not “what can the model do?”
It is “what happens when it fails, and how do I know?”

How are you thinking about the agent harness in your own systems?