Your agent worked once. That is not a test.

An AI agent can look impressive in a demo and still fail in real use.

That does not mean the demo was fake. It means the demo was incomplete.
A single successful run tells you something:

The agent can work in that example.

It does not tell you whether the agent works reliably, handles messy inputs, respects boundaries or fails safely.
That is why “it worked once” is not enough.

Demos hide the hard parts

A good demo usually shows the clean path.
The input is clear. The tool works. The context is available. The answer looks useful. The agent appears confident.

Real work is different.

In real work:

• instructions are incomplete;
• documents are outdated;
• tools fail;
• permissions block access;
• users ask ambiguous questions;
• source data contains contradictions;
• the agent may not know when to stop.

That is where reliability matters.

Define success before testing

Before testing an agent, decide what a good result means.
Not in general terms like:

The agent should be helpful.

But in observable terms:

The agent should extract all confirmed next actions from the meeting transcript, avoid inventing owners or deadlines, and flag any action that lacks enough information.

Now you can test it.
You can compare the result against a standard instead of relying on a feeling.