Your AI agent is only as good as the harness around it

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An agent can give a convincing answer in a demo. Especially when the question is clear, the documents are up to date, and the handful of tools behave exactly as expected. The responses often appear genuinely useful, which gives everyone watching an immediate sense of amazement, and a little too much confidence in how the system will perform outside the demo.

Then a user asks a question that’s close to the one from the demo, worded slightly differently. The account record is incomplete. A tool returns an error. A policy changed last week. Or the agent discovers a capability boundary—it can read an invoice but can’t change it. This is often where the real work begins.

Most agent projects are much harder than the demos suggest. The model is one part of the service. The agent harness is the rest—the scaffolding the application builds around the model to feed it the right inputs and check its outputs, helping catch failures before they spread. Developers already know this idea from test harnesses, which wrap code to run under controlled conditions. A production agent needs the same wrapper, so it can decide what data the agent sees, which actions it can take, and what happens when a required fact is missing.

“The model is one part of the service. The agent harness is the rest—the scaffolding the application builds around the model.”

Good model output matters, but it doesn’t prove an agent is ready for real work. Proving that is the harness’s job: tool contracts that limit what a wrong call can do, permissions enforced outside the model even when an instruction attempts to …

Vollständiger Originalartikel ↗

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