An AI agent is software that can use tools and take steps on someone's behalf. When it sends a document or changes a record, its conversation transcript is only part of the story. The more useful question is whether the action was allowed and what happened next.
A reviewer should be able to answer that question from one linked record. That record does not need to contain every prompt or every byte of data the agent saw. It needs to connect the request, the rule, and the result.
Five questions one action record should answer
- Step 01Who acted? Record the agent's stable identifier and, when relevant, the person or service that authorized the task. A display name alone is too easy to change.
- Step 02What was it trying to do? Name the tool or destination, the operation, and the data or resource involved. Use a document ID or category where the full content is not needed.
- Step 03What authority did it have? Keep the permission or policy version that applied at that moment. A later policy change must not rewrite the story of an earlier action.
- Step 04What did the control decide? Record whether the action was allowed, blocked, or sent for human approval, plus the reason or rule identifier.
- Step 05What actually happened? Record the tool's outcome, including failure or cancellation. An allowed request is not proof that the action succeeded.
Tie those five answers together with a time and reference numbers linking related steps. This is a suggested operating pattern, not a universal standard. The right fields depend on the action and the system.
What a reviewable record looks like
Imagine a support agent asked to send a customer file to an outside address. A useful record could say: agent support-17, acting for case 4821, tried to use the email tool with file 729; policy version 6 blocked external recipients; the email tool was never called. A reviewer can see the attempted action, the decision, and the outcome without storing the file or the email body in the audit log.
If the request was approved instead, record who approved it, the scope of that approval, and whether the send completed. Treat approval and execution as separate events. This small distinction matters when an action is retried or fails after approval.
Keep the evidence useful and the data limited
Logs can become a second store of customer data. OpenTelemetry's guidance for AI activity warns that tool arguments, tool results, and messages may contain sensitive information. Start with identifiers, categories, decisions, and outcomes. Capture raw content only when a specific investigation or legal requirement justifies it, with access controls and a retention rule.
A trace is also not proof that a control worked. It may show that a tool was called, but not which rule was evaluated or whether the call was stopped before reaching the tool. Keep the control decision alongside the activity it governed.
The legal point is narrower than many headlines suggest
Article 12 of the EU AI Act requires automatic event recording for systems classified as high risk; it does not prescribe this five-field template for every AI agent. The current EU timetable says the Annex III high-risk rules apply from 2 December 2027. Security teams should build useful records now, while checking which legal duties apply to each system.
A practical test for this week
- Choose one agent action that can change data or send information outside your organization.
- Run one allowed case, one blocked case, and one failed case. Try to reconstruct each from the records alone.
- If you cannot tell who authorized the action, which rule applied, or whether it completed, add that missing link before expanding the agent's access.
This is the same sequence HikmaAI is built around for supported interactions: assess the path, apply a control at the protected boundary, and keep the resulting evidence. The record earns its value when someone else can understand the decision without asking the original engineer to explain it.
