Before you build another agent, ask what it knows

Agents are becoming the visible face of enterprise AI. They can interpret a request, coordinate work and produce an answer in seconds.

That is useful. It can also create a false sense of completeness.

A well-designed agent may still be working from partial product records, disconnected documents or general information that lacks the context of a specific device, component or material.

The problem becomes more serious when a fluent answer is mistaken for a well-supported decision. Speed at the interface cannot compensate for evidence that is absent, outdated or unrelated to the product in question.

An agent can automate a task. It cannot manufacture missing knowledge.

Before evaluating another agent, ask:

• What does it know about our products?
• What evidence supports its conclusions?
• Can it distinguish a known fact from an unresolved gap?
• Where must an expert review the result?
• What does our organization retain after the work is complete?

Building an internal agent can be a valuable first step. The important distinction is between an agent that helps retrieve information and an intelligence system that helps experts make a defensible decision.

That distinction should shape procurement as well as development. Teams should test the quality and relevance of the evidence, the visibility of unresolved questions and the role of accountable experts, not simply whether the experience feels fast.

For regulated manufacturers, the interface is only the visible layer. Trust depends on the product and material understanding behind it.