Short answer: An AI agent can shine in a demonstration but fail on a corrected invoice or manipulated document. A robust pilot therefore combines functional tests, targeted adversarial checks and user tests with documented results.
A successful demonstration is only a first indication
An AI agent reads a purchasing document, identifies its main fields and prepares the next steps. The workflow looks convincing. It remains unclear, however, whether the agent also handles a different document version, multiple delivery dates, incomplete information or deliberately misleading text correctly.
Anyone integrating AI into real business processes needs more than an impressive demo. The tasks, boundaries and response to errors must be tested in a traceable way.
Three kinds of evaluation
The NIST ARIA Evaluation Planning Manual, published in September 2026, describes a comprehensive evaluation of AI from three complementary perspectives: Model Testing, Red Teaming and User Testing. It provides a basis for planning specific tests; it is not a certification of an individual application.
1. Functional model test: does the agent solve the task?
Check whether documents are read correctly and whether the extracted values are associated with the correct case. Use both routine and challenging cases. A test should show not just that the system produces an output, but how often it is accurate and what errors occur.
2. Targeted adversarial testing: where are the boundaries?
Deliberately supply contradictory, misleading or manipulated information. Does the agent unintentionally change its assignment? Does it confuse instructions inside external documents with authorised commands? Does it attempt to use tools it is not allowed to use?
Not every potential cause of failure can be tested in advance. This makes systematic evaluation of observed failures even more important.
3. User testing: can employees decide safely?
Even excellent technical scores are of little use if employees cannot understand results or recognise errors. Observe how people use notices, source references and exceptions. Can they stop an unsafe action, correct it or escalate it appropriately?
What does this mean for your business?
Choose a clearly bounded pilot. Define in advance what a correct case looks like and which deviations must stop processing. Plan a functional test, a deliberate stress test and a user test. Record the results in writing, particularly failures.
Want to evaluate an AI agent under realistic conditions before deployment? I can help define effective test cases and approval limits.
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