A helpful AI demonstration is a starting point. Release confidence comes from checking how the application behaves across realistic work, difficult questions and changing conditions.
Define the tasks the AI is meant to perform, the information it may use and the actions it may take. Agree which mistakes are tolerable, which require a human, and which must block release.
For an assistant that answers questions, the concern may be incorrect advice. For an agent connected to business systems, it may be an inappropriate action. The test approach should reflect those consequences.
Test both retrieved information and the answer it supports. Check relevance, source support, missing evidence and uncertainty. An accurate answer from an outdated document can still be wrong for the business.
Inspect tool selection, parameters, permissions and handoffs alongside the final outcome. Include interrupted tasks, repeated requests and recovery. A report of success needs evidence that the intended action happened.
Challenge hostile instructions in documents, access outside a user’s role and actions beyond the agreed scope. Cover the application and integrations as well as the model, including human approval points.
Compare approved examples across relevant languages, users and conditions. Investigate gaps hidden by an overall score. Check exposure through prompts, outputs, retrieved documents and logs. A passing set cannot establish freedom from bias or privacy risk.
Keep representative cases and expected outcomes. Compare changes to prompts, models, data and tools. Repeat variable tasks and report inconsistency. Check latency, capacity, cost, dependency failure and recovery.
Calibrate automated scoring against reviewed examples. Domain experts should assess ambiguous and consequential results. Report scope, material failures, limitations and residual risk. Predictive models still need task-appropriate measures.
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