Teams often test an AI agent by trying a few prompts, reading the final answers and deciding that the demo “looks good.” That misses failures caused by tool order, timeouts, incomplete evidence, memory, user context and inconsistent behaviour across repeated runs.

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> This practical session demonstrates a compact release-evidence workflow from beginning to end. We start with one concrete product claim and translate it into observable success criteria and unacceptable actions. We then run a small set of representative and boundary cases against a synthetic tool-using agent, repeat selected cases, and inspect task completion, latency, errors, tool-use trajectory and critical failures. One failure is classified, reproduced and converted into a reusable regression case.

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> A second comparison varies controlled user-context lenses, including relevant behavioural and psychometric dimensions. The purpose is not to claim that synthetic profiles represent or predict real people. It is to expose conditions under which the system changes behaviour and to show where human research and domain evidence remain necessary.

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> Participants can follow the method through Construct’s public browser-based demonstration. The session closes with a plain-language release checklist covering: what to test; what evidence to retain; when a failure should block release; and when human review is required.

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> The demonstration is educational and uses synthetic material. It is not a certification, legal opinion, penetration test or guarantee that all failures have been found. The aim is to give founders and technical teams a method they can use immediately, before a client delivery or public release.