Harnesses vs Agent Demos

Most AI agent demos prove that a model can call a tool once. Agentic harness engineering asks a harder question: can the system keep doing useful work with goals, memory, tools, permissions, budgets, and evidence that other people can inspect?

A harness is the operating layer around the model. It decides which tools are available, which instructions matter, what state is carried forward, how expensive a run is allowed to become, and what proof is saved after the run. The model is important, but the harness is what turns a model call into a trustworthy system.

An agent demo usually says, "Look, it completed the task." A harness asks:

• What was the task?
• What tools were allowed?
• What did each tool return?
• What did the agent claim?
• What did the evidence prove?
• What should happen next if the claim was false?

That is the difference between a clever prototype and a system someone can trust with real work.

In the certification program, you will use this distinction constantly. Your portfolio proof packet should not merely show that an agent produced a nice answer. It should show how the harness constrained the agent, recorded the run, measured the outcome, and made the result reviewable.