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To monitor AI agents safely, define what each agent is allowed to do, enforce those limits outside the model before every tool call, and keep a trace that lets you reconstruct what happened. Alert on policy violations and unusual behavior, and require human approval for high-impact or irreversible actions. Monitoring helps you detect and investigate problems; it is not a substitute for access controls that block unauthorized actions.
Start by defining what each agent is allowed to do
Write down each agent’s intended task, accountable owner, permitted data, approved tools, and allowed operations. Treat authority as specific to the agent and task—not as a general permission to use every tool or resource available to the application. Grant only what the role needs, and deny access by default where practical.
Classify actions by their potential impact. These examples are illustrative; the system owner must set classifications for its own environment.
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| Action class | Illustrative examples | Typical control |
|---|---|---|
| Lower impact | Read-only lookup of an approved resource | Allow only the named tool and permitted targets; log the authorization decision and result. |
| Higher impact | Sending an external message, changing access, deleting data, moving funds, or deploying code | Apply stricter checks and require explicit human approval where the action is high-impact or irreversible. |
Do not rely on the agent to decide whether its own request is authorized. Microsoft’s guidance on reducing autonomous-agent risk recommends approval for high-risk or irreversible actions.
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How do you stop an agent from making unauthorized tool calls?
Put a deterministic authorization check at the execution boundary: the orchestrator or application layer that can allow or block a tool call before it runs. Check the agent identity and delegated authority, the requested operation, the target resource, the parameters, and the applicable policy. Use explicit action schemas and allowlists rather than treating a plausible model response as permission.
- Identify the caller and authority. Confirm which agent and user or delegated identity initiated the action, and whether that authority covers the requested operation.
- Validate the request. Check the tool, operation, target, and parameters against the task’s policy and permitted scope.
- Route risky actions for approval. For high-impact or irreversible actions, pause execution until an authorized human explicitly approves it. Implement this in orchestration logic, not as a prompt asking the model to remember to seek approval.
- Allow or block the call. Run only permitted requests. Deny out-of-scope actions, and fail closed if authorization or approval validation is unavailable.
Also fail closed if required audit logging fails: an action that cannot be authorized or recorded should not proceed. OpenAI’s API guidance for cybersecurity checks similarly recommends denying unauthorized actions, pausing ambiguous or high-risk changes for approval, enforcing independent filesystem and network boundaries, keeping audit logs, and failing closed when review is unavailable. The exact controls depend on your tools and environment.
What should an AI-agent audit log record?
Record enough linked events to reconstruct a run and answer who or what acted, under which authority, what it attempted, what the system allowed, and what happened next. Microsoft’s secure-agent guidance recommends capturing plans, tool calls, decisions, and outcomes for audit and incident response.
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- Agent and user or delegated identity, plus the task or request identifier.
- Relevant plan or context needed to understand the action.
- Tool name, proposed operation, target, and parameters.
- Authorization and risk decisions, including the policy applied and whether approval was required.
- Whether a human approved, denied, paused, or stopped the action.
- Tool result, final outcome, and timestamps for the linked steps.
Link events across tool calls and services so an investigation can follow a multi-step task rather than see unrelated entries. Microsoft recommends end-to-end traces and alignment with OpenTelemetry. Preserve the integrity of audit events, and monitor for missing or tampered records; otherwise, an incomplete trace can obscure the very behavior you need to investigate.
Which behaviors should trigger an alert?
Begin with direct policy violations, which are generally easier to interpret than a statistical anomaly alone. Then add application-specific behavioral signals and validate them against normal activity before treating them as evidence of misconduct.
- Policy violations: attempts to use a denied tool, act on an unapproved target, access sensitive data outside scope, change privileges without authority, or bypass an approval step.
- Unexpected behavior: a sudden shift in tool-call volume, unusual destinations, unexpected write operations, repeated retries, or actions inconsistent with the assigned task.
- Control failures: missing or altered audit events, or an authorization or review check that is unavailable when an action requires it.
Do not apply a universal alert threshold: the cited guidance recommends anomaly monitoring but does not establish one for every application. Tune signals to the agent’s task and environment, and investigate whether a deviation is a real policy problem or an expected change in workload.
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When should a human approve an agent action?
Require explicit approval before an action with serious consequences or one that is difficult to reverse. Examples may include external communications, access changes, deletion, financial transfers, and production deployments, depending on the system’s risk classification. Pause ambiguous high-impact requests as well; do not let uncertainty silently become permission.
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How to respond to a suspected violation
- Contain the action. Block a high-confidence policy violation. Pause and seek human review when the signal is uncertain but the potential impact is substantial.
- Preserve the trace. Retain the linked events needed to determine what was requested, authorized, executed, and returned.
- Limit further access. Revoke or narrow credentials when necessary, and check whether the agent could act through other tools, integrations, or credentials.
- Recover and verify. Use a safe shutdown or recovery procedure, then test that the relevant policy and enforcement controls work before resuming the agent.
Exercise these steps in advance. A monitoring alert is useful only if someone can respond, interrupt execution, and contain access through the paths the agent actually uses.
Test the controls when the agent changes
Evaluate prompt injection, unsafe tool selection, broken task intent, and sensitive-data leakage before deployment and after material changes to the model, prompts, tools, memory, retrieval, or integrations. Test whether controls block the action at the execution boundary—not merely whether the model gives a safe-sounding answer. Track safety and tool-use correctness alongside task quality. Microsoft recommends evaluation and red teaming; OWASP warns against skipping adversarial testing after system changes.
Prompt injection is a particular concern when agents consume documents, websites, messages, or tool responses that may contain instructions. Treat this content as untrusted data, separate it from trusted instructions, restrict available tools, and validate proposed parameters deterministically. Monitoring can reveal attempts or outcomes, but it does not replace those preventive controls.
Protect monitoring data as carefully as agent access
Traces and logs can contain personal information, credentials, confidential prompts, or retrieved content. More telemetry is not automatically safer. Define what to collect, how to redact it, who may access it, how it is protected, and when it is deleted. Balance forensic needs with data minimization, retention requirements, data residency, privacy, and legal obligations. The reviewed guidance does not set a universal retention period; choose one that fits the information and obligations involved.
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What monitoring can and cannot tell you
A monitor can miss behavior, raise false alarms, or observe only part of a run. An agent may also act through a dependency or credential outside the monitored path. Reduce these blind spots with independent tool enforcement, identity controls, environment boundaries, audit integrity, and incident exercises. OWASP advises against relying solely on model output for authorization decisions.
Review latency matters, but there is no universal monitoring interval that makes an action safe. In a 2026 post, OpenAI reported that its internal coding-agent monitor reviewed interactions within 30 minutes of completion in the version described, and said the security benefit increases as review approaches real time. That is an organization-specific implementation detail, not an industry benchmark or a substitute for pre-execution authorization when an action could cause serious harm.
Choosing monitoring tools
Compare tools by the control they actually provide, not by the label “agent monitoring.” Microsoft names Foundry tracing and evaluators, Purview for AI-native audit logging, Azure Monitor Application Insights for production monitoring and dashboards, and Sentinel for correlating logs and traces. These are examples from Microsoft’s ecosystem, not independent rankings or proof that a product prevents unsafe actions. Microsoft labels some Agent 365 observability capabilities as preview in the reviewed guidance; check current availability and capabilities before relying on them.
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| Enforcement | Can the system deterministically block a tool call, or does it only record events and alert? |
| Trace completeness | Can it link identity, authorization, tool use, decisions, and outcomes across orchestrators and services? |
| Response path | Can operators pause or stop a run, revoke access, and route an approval quickly? |
| Privacy controls | Can collection be minimized and redacted, with access and retention configured? |
| Operations and integration | Does it fit the existing telemetry and security stack, and can the team manage alert quality and incident response? |
Choose a combination that provides both preventive enforcement and usable visibility. A dashboard that detects a prohibited action after execution is not equivalent to a control that blocks it before execution.
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