Monitor an AI agent by combining controls that prevent unauthorized actions with traces and alerts that help you detect, investigate, and respond to problems. The agent should not be the authority on what it is allowed to do: enforce permissions in the application or orchestration layer, require human review for consequential actions, and record enough context to reconstruct each run. Monitoring can reduce risk, but it cannot guarantee that every unsafe action will be detected.
Start by defining what each agent is allowed to do
You cannot reliably detect an action as unauthorized until you have defined the agent’s authority. For each agent, document its purpose, accountable owner, permitted data, approved tools, and allowed operations. Specify which user or delegated authority the agent may act for, and which resources fall within scope.
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Use least privilege: grant only the access needed for the assigned task, and deny access by default where practical. Classify actions by the consequences they could have in your environment. For example, reading an approved record may be low risk, while sending an external message, changing access, deleting data, moving funds, or deploying code may be high impact. These are examples, not universal classifications; the system owner must set the tiers.
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How do you stop an AI agent from making unauthorized tool calls?
Put a deterministic authorization check at the point where the application executes a tool call. Treat the model’s proposal as a request, not permission. Before execution, validate the agent’s identity, the user’s or delegate’s authority, the requested operation, the target resource, the parameters, and the applicable policy.
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- Use explicit tool allowlists and schemas that limit which operations and parameters are valid.
- Check that the requested target is within the agent’s assigned scope.
- Deny requests that fail policy checks; do not rely on the agent to self-police.
- Fail closed if authorization, approval validation, or required audit logging is unavailable.
- Keep independent identity, filesystem, and network boundaries so a monitored tool path is not the only control.
Prompt injection is one reason these checks matter. Documents, websites, messages, and tool responses may contain instructions intended to redirect an agent. Treat such content as untrusted data, separate it from trusted instructions, limit available tools, and validate proposed actions and parameters outside the model. Detection can reveal an attempted or successful attack, but it is not a substitute for prevention.
When should a human approve an agent action?
Require a human approval step for actions that are high impact, irreversible, ambiguous, or outside an agent’s routine authority. Microsoft’s guidance, “Reduce autonomous agentic AI risk,” says to “Require approval for high-risk or irreversible actions.” Implement the approval requirement in orchestration logic rather than asking the model to decide whether approval is necessary.
The reviewer should be able to see what will happen, which resource will be affected, why the request is in scope, and what the likely consequence is. Provide a reliable way to pause or stop the run while the decision is pending. If review is unavailable or approval cannot be verified, do not execute the action. For a high-confidence policy violation, block the request and alert the responsible operator; for an uncertain but consequential case, pause for review.
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What should an AI-agent audit log record?
Build a linked execution trace that lets an investigator follow a run across the agent, orchestrator, tools, and other services. Microsoft’s secure-agent guidance, last updated March 19, 2026, recommends capturing plans, tool calls, decisions, and outcomes for audit and incident response. A useful event record includes:
- Agent identity, user or delegated identity, and 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 the request was allowed.
- Human approval, rejection, interruption, or timeout when applicable.
- Tool result, final outcome, and timestamps for each step.
Link events across calls so the sequence and outcome of a multi-step run can be reconstructed. Microsoft recommends end-to-end traces and alignment with OpenTelemetry. Do not assume that recording every prompt and output is necessary: traces can contain personal information, credentials, confidential prompts, and retrieved content.
What to alert on—and how to respond
Begin with direct policy violations, then add behavioral signals that make sense for the application. Possible detections include:
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- Attempts to call a disallowed tool or act on an unapproved target.
- Unexpected privilege changes or sensitive-data access outside the assigned scope.
- Attempts to bypass or manipulate an approval step.
- Missing, altered, or unexpectedly incomplete audit events.
- Unusual tool-call volume, destinations, write operations, retries, or actions inconsistent with the agent’s assigned task.
Behavioral signals are candidates for investigation, not proof of misuse. The reviewed guidance does not establish universal alert thresholds; set and validate thresholds against the agent’s workload and baseline rather than treating a single number as a standard.
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Protect monitoring data as carefully as action permissions
Set a data contract for telemetry before collecting it. Define what is recorded, who can access it, how it is protected, what is redacted, how long it is retained, and how it is deleted. Minimize collection to what is needed for security, operations, and investigation. Microsoft’s guidance highlights the need to balance forensic value with privacy, data residency, retention requirements, data minimization, and legal obligations. The reviewed sources do not prescribe a universal retention period.
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Test safeguards whenever the agent changes
Evaluate both task quality and safe tool use before deployment and after material changes to the model, prompts, tools, memory, retrieval sources, or integrations. Include adversarial tests for prompt injection, unsafe tool selection, attempts to break task boundaries, and data leakage. Red-team results should lead to changes in policy, permissions, or execution controls where needed—not merely to new alerts.
Microsoft recommends evaluation and red teaming, and OWASP warns against skipping adversarial testing after system changes. Also test that authorization failures, approval outages, and logging failures produce the intended safe outcome rather than silently allowing execution.
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OpenAI reported in a 2026 post that its internal coding-agent monitor reviewed interactions within 30 minutes of completion in the version described. That is an organization-specific implementation detail, not an industry benchmark or proof that post-action review is sufficient. For serious consequences, authorization must happen before execution, with human approval where appropriate.
Monitors can miss behavior, trigger false alarms, or receive incomplete logs. An agent may also act through credentials or dependencies outside the monitored path. Independent tool enforcement, identity controls, environment boundaries, audit integrity, and incident exercises help address those blind spots; model output alone should never decide whether an action is authorized.
Choosing monitoring tools
Microsoft names Microsoft 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 one vendor’s ecosystem, not independent rankings or evidence that any one product prevents unsafe behavior. Microsoft labels some Agent 365 observability capabilities as preview in the guidance reviewed; availability and capabilities can change.
When comparing tools, assess whether they can enforce or only observe; whether traces connect identity, authorization, tool calls, decisions, and outcomes; whether operators can pause runs and revoke access; how well privacy and retention are controlled; and how the system fits your telemetry and incident-response operations.
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