Build a realistic acceptance test for a business AI agent

“Looks good in the demo” is hard to use when a team must decide whether to go live. An acceptance test describes an input and an outcome another person can check. Assemble a small casebook before configuring the agent. Keep some examples hidden from the builder until final review so the test also measures how the design handles unfamiliar cases.

Test the complete business outcome

For an assistant that prepares repair estimates, one example could contain an item code, quantity and a current customer email address. The expected result is a draft tied to the correct job with the same quantity and a reference to the approved parts list. Read that draft in the destination system. A good answer displayed only in a demo window leaves the actual transfer untested.

Include a conflicting case. An email requests five units and an attachment lists six. The expected result might be a flagged quantity awaiting review and no approved estimate. Include a duplicate request too. The team should know whether the result is one updated draft, two linked drafts or an exception sent to an owner.

Score by consequence

Record case ID, expected result, actual result, evidence and severity. Give a wrong account or an unauthorized send its own release rule. A missing comma and a quote attached to the wrong customer should not cancel each other out in an average score.

After a fix, repeat the failed case and neighboring cases that use the same rule. Keep the original expected results unless the business owner explicitly changes the policy. When the pilot ends, retain the casebook and the signoff date. It becomes a quick way to check later integrations, instruction changes or new business lines against the decisions the company already approved.

Put this into practice

Bring a sample of the work and the tools your team uses. We can help define a first project, who reviews it, and how to tell if it works.

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