Why do AI agents require runtime evidence instead of only access logs?

Why access logs are not enough for AI agents

Access logs are useful, but they only show that a system or token was used. For AI agents, that is not enough to explain whether a sequence of actions was authorised, whether the agent stayed within its approved remit, or who should be accountable when the behaviour shifts during execution. Runtime evidence captures the governance chain, not just the activity trail.

That distinction matters because agents often operate across multiple tools, sessions, and decision points. A log entry may tell you that an API was called, but not whether the call was made under the right sponsor, with the right delegated authority, or after a policy decision changed the scope of what the agent could do.

Runtime evidence also makes review possible after the fact. If a task was approved for one purpose and then expanded, branched, or retried in a way that changed its impact, teams need evidence that shows the transition points, not only the final footprint. Without that context, access logs can support forensics, but they cannot prove governance.

What runtime evidence must show

Good runtime evidence connects the agent’s behaviour to the approval model that governed it at the time of execution. That means showing the sponsor or owner, the delegated authority, the policy decision that allowed each step, and the boundaries that were in force when the agent acted. If those elements are missing, you have observation without attribution.

Runtime evidence is most valuable when it includes the sequence of decisions, not just the final outcome. Practitioners should be able to reconstruct why a tool call happened, whether the request was expected, whether human approval was required, and whether the agent remained within its assigned purpose as the task unfolded.

For teams building AI agent controls, this is where AI Agent Authorisation Guide becomes practical: it frames per-action authority, delegated approval, and least privilege as runtime decisions rather than one-time setup choices. That same logic is reinforced by AI Agent Observability, Audit and Incident Response Guide, which focuses on attributing actions and testing what signals show when an agent goes off course.

Runtime evidence is also what separates a clean audit trail from a merely busy one. If you cannot tie each meaningful action back to a current policy decision, then the record may be sufficient for troubleshooting but insufficient for assurance.

Why governance changes during execution

AI agents do not always behave like static automation. They can re-plan, retry, select different tools, or expand a task based on intermediate results. That means the original approval is not always the full story. Runtime evidence records whether the execution path still matched the intent that was approved, or whether the agent drifted into an adjacent action set that should have triggered a new decision.

This is especially important when agents have access to sensitive systems or can chain several benign steps into a material outcome. A single access log rarely shows that the sequence itself crossed a governance boundary. Runtime evidence can show the decision points where scope should have been reassessed, which is why it is a control requirement, not just an observability enhancement.

That is the practical reason the question is not “can we see what happened?” but “can we prove the authority for what happened?” If the answer depends on assembling context from disconnected logs after the fact, governance is too weak for high-impact agent behaviour.

Risk and Threat Considerations

When agents can act across tools, token boundaries, or delegated permissions, the main risk is overreach that looks legitimate in access telemetry. An attacker, or even a faulty agent, can remain within logged access patterns while still exceeding intended authority, especially if the control model does not preserve the approval context for each runtime decision.

Failure mechanism: The organisation records activity but not the policy state that justified each action, so reviewers cannot tell whether a step was authorised, re-authorised, or out of scope once the agent adapted its plan.

Impact: Teams lose the ability to prove governance, contain misuse quickly, or distinguish approved autonomy from privilege creep, which increases the blast radius of both operator error and malicious use.

Practitioner Guidance

What to verify: Make sure each high-impact agent action can be linked to a current sponsor, an approval path, and the policy decision that allowed it. If you cannot reconstruct those three items from evidence alone, the control is incomplete.

Decision rule: If a log shows access but the runtime record cannot explain the authority behind the action, treat that as an assurance gap rather than a logging gap. The right response is to improve runtime attribution and approval capture, not to accept the record as sufficient.

What good looks like: A reviewer can follow the agent from request to execution and see where authority was granted, where scope was constrained, and whether any step required escalation or renewed approval.

Practitioner takeaway: Access logs tell you that something happened; runtime evidence tells you whether it was allowed to happen in the way it did.

Related resources from NHI Mgmt Group

• When is it crucial to implement least-privilege access for AI agents?
• What evidence is needed to understand the impact of shadow AI agents?
• How should security teams govern AI agents that use OAuth access?
• How should security teams limit the risk from AI agents that have access to production systems?