AI Agent acceptance: answers, human handover and authorised actions
Before an enterprise AI Agent pilot goes live, test three things separately: whether answers use approved knowledge, whether difficult cases reach the right person, and whether actions stay inside granted permissions. A convincing demonstration is not a substitute for a recorded acceptance test.
This is a proposed pilot worksheet for business owners and delivery teams, not an industry certification or a claim that a particular product has already passed it.
Define the test boundary
Choose one workflow, one accountable owner and a documented knowledge version. Record the users, languages, systems and permitted actions. Agree which failures block launch and which require revision. Passing the agreed sample does not establish correctness for every future question.
Test three independent gates
| Gate | What to test | A launch-blocking example |
|---|---|---|
| Answers | Expected facts, source references and handling of missing or conflicting information | An invented product specification is presented as confirmed |
| Human handover | The correct owner receives the reason, conversation context and unresolved question | A sensitive case is sent to an unattended inbox with no acknowledgement |
| Authorised action | Role limits, required approvals and an audit record of the requested action | An agent changes a customer record or makes a commitment without required permission |
Include ordinary enquiries, ambiguous requests, outdated documents, conflicting sources and attempts to bypass permissions. Use controlled test data rather than real customer records where possible.
For answers, distinguish factual accuracy from tone or fluency. For handover, verify receipt, not merely that a notification was sent. For actions, inspect the actual system state and approval record rather than relying on the agent's account of what happened.
Record a decision, not just a score
For each case, record the input, expected outcome, actual outcome, source version, reviewer and corrective action. Mark an unavailable test as not tested, not passed. Keep critical failures visible even when the average score looks good.
For example, an agent might answer nine routine catalogue questions well but invent a value for a safety-sensitive attribute. That illustrative result should trigger correction and retesting of the affected workflow; it is not an AIagent customer result or a universal pass threshold.
The accountable owner then chooses to accept the scoped pilot, revise it or stop. Retest affected cases when knowledge, permissions, integrations or workflow rules change.
Company context and limits
AIagent Technology Limited describes its work as enterprise AI agents for Hong Kong and Southeast Asia. Its published delivery process includes configuring approved boundaries and validating accuracy, handover and permissions. These statements provide company context; they do not validate this worksheet as an external standard. See the company overview.
The worksheet does not replace security assessment, legal review or specialist sign-off. High-risk personnel, financial, legal, quality, regulatory and safety decisions remain with responsible people. No search visibility, conversion or accuracy guarantee is made.