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To monitor an autonomous AI agent, record what it actually does at runtime—not just what it says. Capture tool requests and their outcomes at the orchestration or API boundary, correlate them with changes in connected systems, and retain protected records that let an authorized reviewer reconstruct a run. Then alert on risky or unexpected behavior and test that your team can investigate and contain it.
How do I monitor what an AI agent is doing?
Observe both the agent’s runtime environment and its external actions. A conversation transcript can show the model’s plan or a tool call it proposed, but it may not establish whether the call was accepted, what resource it reached, or what changed. Those facts are best captured by the system that executes the action and, where possible, by the system whose state may have changed.
NIST’s December 2025 initial preliminary draft of NIST IR 8596 gives examples of runtime behavior worth monitoring, including unexpected file writes, API calls, and generated binaries. The publication is a preliminary draft, not a finalized standard. It states: “Because AI can autonomously create and augment data as well as create and execute its own code, new monitoring is needed to track actions taken by AI.”
Define the agent’s scope and accountable owner
Before deployment, document each agent’s owner, environment, business process, connected tools and data, and granted permissions. Specify its intended task, prohibited actions, actions requiring human approval, and who must respond to an alert. Set risk-based thresholds for the system’s context rather than treating every event as equally serious.
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NIST’s AI Risk Management Framework is voluntary and intended to be tailored to organizational needs. Its Playbook offers suggested actions, not a mandatory checklist.
Record actions where they are executed
Instrument the orchestration layer and tool or API gateways so a record is created when an agent requests an action and updated or supplemented when the system accepts, denies, or completes it. When feasible, capture the resulting resource change or a durable reference to evidence held by the target system. For a multi-agent workflow, preserve parent-child task links so investigators can follow which agent initiated or delegated an action.
Do not treat model chain-of-thought as an audit record. The operational evidence is the request, authorization decision, execution result, and observed outcome—not private reasoning text. The fields below are practical implementation guidance; NIST does not prescribe this as a required schema.
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What should an AI agent audit log include?
A useful record should connect an event to an identifiable agent run, the action attempted, the authority under which it ran, and the result. A minimal event design can include:
- Identity and correlation: stable agent ID, run or session ID, event ID, and parent or child task ID where relevant.
- Time: timestamp from a synchronized clock, plus the event stage, such as requested, authorized, denied, started, or completed.
- Action and target: tool or API name, operation, target resource, and enough request detail to identify what was attempted.
- Authorization context: service identity, applicable permission or policy decision, and whether human approval was required or granted.
- Result and outcome: accepted, denied, failed, or completed status; error details where useful; and observed changes or references to them.
- Evidence and handling: source system, integrity and retention controls, and any redaction or minimization applied to sensitive content.
Log enough request and result detail to investigate, but avoid indiscriminate storage of prompts, secrets, or personal data. Restrict access to records containing sensitive content and use redaction or minimization where it still leaves adequate evidence. A transcript can supplement action records; it should not replace them.
How can I tell whether an agent did something unexpected?
Compare observed behavior with the agent’s declared scope, permissions, and normal workflow. Alert on deviations that could indicate misuse, a compromised workflow, or an unsafe outcome, including:
- Calls to tools or resources outside the approved set, or unusual API volume.
- Unexpected file writes, sensitive data access, or changes to permissions.
- Denied actions, repeated retries, or a sequence of failures followed by a successful attempt.
- Agent-created or executed code, including generated binaries that are not expected for the task.
NIST IR 8596’s initial preliminary draft describes monitoring AI systems and runtime environments for anomalous behavior such as unexpected file writes, API calls, and generated binaries, which could indicate manipulation, exfiltration, or exploitation. Treat these as detection examples to adapt to your environment, not as a complete alert catalog.
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Do not infer a state change solely from a model response such as “Done.” Correlate the agent’s action event with records from the target application, identity provider, infrastructure, and security systems as needed. For example, an agent log may show a requested update and a successful API response, while the application’s own audit record identifies the object and final value. Together, those sources can support a more reliable timeline than either source alone.
Send relevant records to a centralized logging system protected with access controls, retention rules, time synchronization, and integrity protections appropriate to the risk. Preserve enough detail to link events across sources and retrieve supporting evidence during an investigation. NIST IR 8596’s draft profile discusses analyzing adverse events and correlating information from multiple sources.
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How should monitoring fit into AI risk management?
Monitoring is an ongoing part of operating an AI system, not a one-time launch check. NIST’s AI RMF Core includes outcomes for monitoring functionality and behavior in production, evaluating safety, and tracking risks over time. NIST’s AI RMF 1.0 is voluntary and is being revised; its companion Generative AI Profile, NIST AI 600-1, published July 26, 2024, is a cross-sector resource rather than an agent audit specification.
NIST has also described development of proposed single-agent and multi-agent security control overlays. Those proposals should not be presented as final controls. For practical governance, revisit the agent’s scope, permissions, alerts, and evidence requirements as its model, prompt, tools, or workflow changes.
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Before production, exercise the full monitoring loop with representative scenarios. Include allowed and disallowed actions, adversarial inputs, tool failures, suspicious access, and interrupted runs. Check that events appear with the right identity and outcome, alerts reach an accountable person, containment controls work, and investigators can retrieve records and correlate them with other systems.
Repeat relevant checks after changes to the model, prompt, tools, permissions, or workflow. Review incidents and near misses to adjust thresholds and controls. The aim is not merely to collect logs; it is to demonstrate that the organization can detect, investigate, and respond to consequential actions.
What to evaluate in agent monitoring tools
Agent tracing and observability products, as well as centralized logging or SIEM platforms, can help only if they capture the events your risk requires and integrate with the systems where actions execute. Evaluate options against these criteria rather than relying on a product label:
Quick Recap
- Coverage of tool calls, API requests, and file or data changes.
- Agent, run, identity, and parent-child correlation.
- Timestamp quality and visibility into authorization or approval decisions.
- Retention, export, investigation workflow, and integration with existing logs.
- Alerting, access control, privacy and redaction options.
- Whether the agent or an administrator could bypass or alter the monitoring path.
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