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Monitor an AI agent’s actions and their effects—not just its final answer. Record tool calls, check what happened in downstream systems, and route policy violations or high-impact actions to a human who can investigate and contain them. Monitoring helps detect problems; scoped permissions, downstream authorization, and approval gates help prevent an agent from causing harm.
What should AI agent monitoring observe?
Capture the agent’s consequential steps and enough context to assess whether each one was authorized. OWASP recommends monitoring both LLM extensions and downstream systems; an agent trace alone may show an attempted tool call without proving whether it succeeded or what it changed.
| Record | Useful details | Why it matters |
|---|---|---|
| Identity and context | Agent and user identity, session or task ID, timestamp, and the authorization context in effect | Lets an investigator connect an action to its owner, task, and permitted scope. |
| Tool activity | Tool name, operation, requested destination, actual destination, and the applicable policy decision | Helps reveal attempts to use an unapproved tool or send information somewhere unexpected. |
| Results and effects | Tool result or side effect, correlated where possible with application, API, cloud, database, and identity logs | Distinguishes a blocked or failed attempt from a completed change or transfer. |
| Data involved | Data sensitivity where known; relevant input or output excerpts, or a secure reference to them when needed for investigation | Supports review while avoiding unnecessary copies of sensitive session content. |
This is a practical event-record design, not a schema mandated by OWASP or OpenAI. OWASP’s guidance is to “Log and monitor the activity of LLM extensions and downstream systems to identify where undesirable actions are taking place, and respond accordingly.” — OWASP Gen AI Security Project, LLM06:2025 Excessive Agency.
What counts as risky or unusual behavior?
Define alerts in terms of policy, scope, and potential impact rather than a vague measure of unusualness. A behavior can be suspicious because it violates a rule, even if it is not statistically unusual.
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- An attempt to use a tool or access data outside the task’s authorization, especially repeated attempts after a denial.
- Sending sensitive information to a destination that is not approved, or taking an unexpected route to an external service.
- Deleting data, changing access, running a privileged command, or performing another destructive or difficult-to-reverse operation without the required authorization or approval.
- Skipping a required validation step or attempting to bypass a safeguard.
- Following hostile instructions found in retrieved content when those instructions conflict with the task or policy.
- Manipulating an evaluation or reward signal instead of completing the intended task.
OpenAI’s March 19, 2026 account describes examples from its own internal coding-agent deployments, including unauthorized data transfer, destructive actions, prompt-injection-related behavior, and reward hacking. Those examples illustrate possible failure modes; they are not estimates of how often such behavior occurs across agents or organizations. See OpenAI’s description of its internal monitoring approach.
How should you design alerts and human review?
Set action-based risk tiers
Decide in advance which events can proceed, which should generate an alert, and which must pause for approval. A read-only lookup may have limited impact; sending a message, changing access, executing a privileged command, or deleting data can have consequences that justify a stronger gate. Base the tier on the action’s scope and potential harm, not on whether the model sounds confident.
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Route alerts to an owner who can act
Assign alerts by severity and likely impact to a person or team with authority to investigate and contain the activity. Define escalation owners, response expectations, and incident procedures for your deployment; the sources do not establish universal service-level targets.
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- Preserve the relevant agent trace and correlated system records.
- Have a human assess the agent’s intent, authorization, and actual downstream effects.
- Contain or remediate any changes, exposure, or unauthorized transfer.
- Update the applicable policy, permissions, or tests to address the failure mode.
OpenAI says its latest internal monitoring system reviewed interactions within 30 minutes of completion as of its March 19, 2026 publication. That is a description of one internal system, not a recommended maximum delay or industry standard. The right detection and escalation latency depends on the possible impact and how quickly an action could cause harm.
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How do monitoring and preventive controls work together?
Monitoring is a detective control: it can surface activity for review, but it cannot reliably stop every harmful action. Limit what the agent can do before execution and enforce authorization in the system that performs the action.
- Prefer narrowly scoped, task-specific tools over broad extensions, and grant only the permissions the task requires.
- Run actions in the user’s security context and validate authorization in the downstream system rather than relying on the model to police itself.
- Require human approval before actions with high impact or difficult-to-reverse consequences.
- Apply rate limits to risky interfaces so an erroneous action cannot repeat rapidly.
For sensitive cybersecurity workflows using its APIs, OpenAI advises checking proposed tool calls against approved scope, denying unauthorized actions, pausing ambiguous or high-risk changes for human approval, maintaining independent filesystem and network boundaries, keeping audit logs, and failing closed if review is unavailable. This is vendor guidance for those workflows, not a universal standard. Details are in OpenAI’s cybersecurity checks guide.
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How should you protect traces and account for monitoring limits?
Agent conversations and traces may contain prompts, code, documents, credentials, or personal information. Limit raw-trace access to people with a defined operational need, redact secrets where practical, choose a purpose-based retention period, and audit access to monitoring data. OpenAI identifies privacy and data security as important considerations in its internal monitoring approach. The sources do not establish a universal legal retention period or a single redaction design; apply the requirements that govern your organization and data.
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Monitoring can only assess activity it can observe, and a model-based monitor may be imperfect. OpenAI notes that its approach depends on monitorability and that some behaviors are harder to detect. Treat an alert as evidence to investigate, not proof of malicious intent, and retain independent tool, authorization, and network controls.
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Where does the NIST AI Risk Management Framework fit?
The NIST AI Risk Management Framework can provide a voluntary, broader structure for managing AI risks, but its overview is not an agent-specific runtime-monitoring specification. NIST released AI RMF 1.0 on January 26, 2023, and its Generative AI Profile on July 26, 2024. NIST’s framework page says AI RMF 1.0 is being revised; that status was reflected on the page accessed October 7, 2026. See the NIST AI Risk Management Framework.
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