Specify acceptance tests before hiring an AI agency

Describe the complete user outcome

Write a case from the initiating request to the point where the business accepts the result. A correct summary may be the intended outcome, or it may only prepare an approved update elsewhere. Make that distinction explicit. Include the source evidence or destination record the reviewer must inspect. This gives the agency a concrete target without prescribing every internal implementation choice.

Include cases that should stop

A useful system should recognize missing information, denied access, and unsupported requests. Select examples where a safe handoff is the correct result. Ask what the employee sees and how work continues manually. For an AI agency serving several Tennessee departments, verify that a user cannot cross a department boundary simply by asking the assistant for a different source or customer record.

Separate output quality from operating behavior

Assess whether the answer is useful and supported, then separately examine permissions, destination validation, interruption, and recovery. NIST’s AI risk framework can inform the measurement questions, but the acceptance cases must reflect your actual workflow. A single average score can obscure serious failures. Define which errors require correction before launch and who has authority to judge ambiguous results.

Reference: NIST: AI Risk Management Framework

Keep acceptance repeatable after handover

Store the approved cases and their expected outcomes in a form the next maintainer can use. Record the relevant application and configuration version. Agentix custom agent delivery can include evaluation and handover artifacts in the agreed scope. When a later change affects behavior, rerun the relevant cases and document the result. Acceptance should establish a maintainable baseline rather than a one-time ceremony that cannot be reproduced.

Reference: Agentix (publisher): Agentix services

Common questions