To monitor an autonomous AI agent, record what it actually does at the point where it uses a tool, API, or resource—not just what it says in a conversation. Connect each action to the agent and run, capture whether it was allowed and what happened, watch the runtime for unexpected activity, and preserve protected records that investigators can correlate with other system logs.
How do I monitor what an AI agent is doing?
Monitor both the agent’s runtime environment and its external actions. A chat transcript can show what the model proposed or reported, but by itself may not establish which external action was accepted, whether it completed, or what system state changed.
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NIST’s December 2025 initial preliminary draft of NIST IR 8596 says that because AI can create and execute its own code, new monitoring is needed to track actions taken by AI. Its examples of activity worth observing include unexpected file writes, API calls, and generated binaries. This is preliminary-draft guidance, not a finalized standard.
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Build monitoring into the agent lifecycle rather than treating it as a logging task added after deployment. NIST’s AI RMF Core includes production monitoring of functionality and behavior, safety evaluation, and tracking risks over time. NIST’s AI Risk Management Framework is voluntary and intended to be tailored to an organization’s context; its Playbook offers suggestions, not a mandatory checklist.
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1. Define scope and accountability
For each agent, document its owner, deployment environment, business process, intended task, connected tools and data, and granted permissions. Define prohibited actions, actions that need human approval, risk-based alert thresholds, and who is responsible for responding.
2. Capture events where actions occur
Instrument the orchestration layer and tool or API gateways. Record an event when an agent requests an action and when the system accepts, denies, or completes it. Where feasible, capture the actual resource change or a reference to evidence of that change. For multi-agent workflows, connect parent and child tasks so the sequence can be reconstructed.
These record fields are practical implementation recommendations derived from monitoring and event-correlation goals; NIST does not prescribe this as a required agent-log schema. Model chain-of-thought is not a substitute for execution evidence: retain action and execution metadata that shows what the system attempted and what the connected resource did.
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3. Monitor runtime behavior and alert on meaningful deviations
Set alerts against the agent’s intended tools, permissions, and normal task boundaries. Depending on the system’s risk, useful signals include:
- Unexpected API calls, file writes, or access to sensitive data.
- Unusual API volume, repeated retries, or a sudden change in action patterns.
- Denied actions, privilege changes, or attempts to use tools outside the approved scope.
- Agent-created or executed code, including generated binaries.
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. It notes that such behavior could indicate manipulation, exfiltration, or exploitation; an alert is a signal to investigate, not proof that an incident occurred.
4. Protect records and make them useful for investigation
Send records to a centralized logging system with access controls, retention rules, synchronized timestamps, and integrity protections appropriate to the risk. Correlate agent events with relevant identity, application, infrastructure, and security records. NIST IR 8596’s draft profile calls for analyzing adverse events and correlating information from multiple sources where needed to understand them.
Logs may contain prompts, personal information, or sensitive business data. Restrict who can see them and minimize or redact content where possible, while retaining enough evidence to investigate actions and outcomes.
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Before production, exercise representative allowed and disallowed actions, adversarial inputs, tool failures, suspicious access, and interrupted runs. Verify that events are recorded, alerts reach an accountable person, containment is possible, and investigators can retrieve the relevant records. Repeat the checks after changes to the model, prompt, tools, permissions, or workflow. NIST AI RMF Core calls for production behavior monitoring and regular safety and security evaluation, tailored to organizational risk.
What should an AI agent audit log include?
A useful event record should let a reviewer determine who or what initiated an action, what it targeted, what decision was made, and what outcome was observed. The following is an implementation checklist, not a NIST-mandated format.
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- Identity and correlation: stable agent identifier, run identifier, and parent or child task reference when relevant.
- Time: timestamp with enough precision and synchronization to line up with other system events.
- Action and target: tool or API used, requested operation, and target resource or a privacy-conscious reference to it.
- Authorization context: applicable permission or policy decision, and any required approval or approver.
- Execution and outcome: whether the request was accepted, denied, or completed, plus the observed result or evidence of a resulting change where feasible.
- Evidence handling: source system, retention and access controls, and any redaction needed to protect sensitive content.
Keep enough request and result detail to support reconstruction, but avoid indiscriminately storing secrets or entire data payloads. A record that says only “tool called” is difficult to investigate; a full copy of every sensitive payload can create a separate privacy and security risk.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I know what an AI agent changed?
Compare the agent’s execution records with evidence from the system it acted on. For a file operation, that may mean a file-change record or version history; for an API action, the target service’s audit event or resource state; for an application change, the application’s own event log. Link those records to the agent run and timestamps wherever possible.
Do not infer a completed change solely from the model’s message or an attempted tool call. The connected system may have denied, failed, or only partially completed the request. Investigators should distinguish a request, an authorization decision, an execution result, and an observed state change.
How should teams assess agent-monitoring tools?
Agent observability or tracing products, and centralized logging or SIEM systems, may be part of an implementation. Assess coverage and fit rather than assuming that a transcript or vendor label provides a complete audit trail.
- Does it capture tool calls, API requests, and relevant file or data changes?
- Can records be tied to stable agent, run, and task identities?
- Are timestamps reliable, and can events be correlated with existing identity, application, infrastructure, and security logs?
- Does it preserve authorization and approval context, outcomes, and evidence of state changes?
- Can teams alert, investigate, export, and retain records under appropriate access controls?
- Can sensitive content be minimized or redacted, and can the agent or a compromised component bypass the monitoring path?
These are evaluation criteria derived from monitoring and event-correlation goals, not a vendor benchmark or a report of hands-on testing.
Which NIST guidance applies to autonomous-agent audits?
NIST AI RMF 1.0 and the 2024 Generative AI Profile, NIST AI 600-1, are lifecycle risk-management resources; neither is an agent-specific audit-log specification. NIST reports that AI RMF is being revised. NIST IR 8596 IPRD provides more direct autonomous-runtime examples, but it is labeled an initial preliminary draft dated December 2025, not a final standard. NIST has also described development of proposed single-agent and multi-agent security control overlays; those should not be treated as finalized requirements.
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