Monitor an AI agent by connecting its identity and each run to timestamped traces, tool and data-access events, policy decisions, and outcomes. Then use live alerts to detect failures or unusual behavior and protect the resulting audit records so investigators can reconstruct what happened. Uptime monitoring alone is not enough: an agent can be functioning technically while producing poor results or taking unsafe actions.
What should an AI agent audit trail capture?
Build the trail around a traceable run, not just a stream of model logs. Each event should be attributable to an agent and, where applicable, the person or service that initiated or approved the activity. Use consistent run and trace identifiers to connect the request to downstream model, retrieval, tool, and service events.
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| Event area | Record | Why it matters |
|---|---|---|
| Identity and context | Timestamp; agent identity; initiating human or service principal when applicable; conversation, session, and run IDs; model and relevant version information. | Shows which agent acted, for whom, and within which execution. |
| Request and response | Inputs and outputs to the extent allowed by the organization’s data policy; task outcome and relevant evaluation results. | Helps assess what the agent was asked to do and whether it completed the task. Do not collect sensitive content or internal reasoning indiscriminately. |
| Retrieval and data access | Sources or records accessed, references to files or services where available, and access events from relevant systems. | Establishes what information may have influenced the agent or been exposed to it. |
| Tool activity | Tool or service name, arguments or action parameters as policy permits, granted permissions, result, and whether the action succeeded. | Reconstructs external effects, including changes made outside the model itself. |
| Controls and approvals | Policy decision, denied or blocked action, approval request and decision, and the identity of the approver where applicable. | Shows both what safeguards allowed and what they prevented. |
| Execution and security context | Relevant orchestration, sandbox, access-control, proxy, and network events, linked by trace or run IDs. | Connects agent behavior to the surrounding infrastructure and security investigation. |
Capture operational evidence that explains decisions and actions; an audit trail does not require indiscriminate collection of every prompt or private reasoning trace. Microsoft’s guidance on observability for generative and agentic AI recommends telemetry sufficient for incident reconstruction and OpenTelemetry-aligned GenAI conventions. Adapt the fields to the agent’s risk, architecture, and data policy.
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Use dashboards and alerts for both conventional service signals and AI-specific outcomes. Microsoft notes that uptime and error rates alone are not good indicators of AI quality and reliability.
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System and resource signals
- Latency, errors, request volume, and tool-call volume.
- Token use and other relevant resource consumption.
- Failed integrations, timeouts, and unexpected changes in call volume.
Quality, safety, and action signals
- Task outcomes, quality evaluations, and groundedness checks where applicable.
- Whether tool selection and tool use match the task and allowed action schema.
- Denied actions, policy violations, approval outcomes, and repeated attempts to perform blocked actions.
- Changes in tool-call rates, data access, or other behavior compared with a baseline for that agent.
Set baselines by agent and workload rather than treating every system as interchangeable. Alert on meaningful deviations and control failures, not merely on high activity: a burst of legitimate work may be normal for one agent and suspicious for another. Pair alerts with enough trace context to identify the affected run and investigate it.
How do you attribute actions and limit the blast radius?
Give each agent a distinct identity, separate from human users and other systems. The UK National Cyber Security Centre’s August 20, 2026 guidance on managing the cyber risk of agentic AI recommends unique agent identities in a class distinguishable from people and individual systems.
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- Grant only the credentials, data access, and tools required for the assigned task; use short-lived credentials where possible.
- Constrain tool inputs with explicit allowed-action schemas and record the permissions available at the time of an action.
- Require deterministic approval for high-risk or irreversible actions, and preserve the approval decision in the run trail.
- Maintain an immediate shutdown mechanism and a way to restrict the agent’s network or model communications during an incident.
The NCSC advises that organizations should be able to halt autonomous agent activity immediately if an incident is detected or reported. Make that capability part of the operating design, not an assumed feature of a monitoring dashboard.
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How do you make audit records trustworthy and privacy-conscious?
Make telemetry available during execution for monitoring and preserve relevant records for later investigation. Protect logs against unauthorized modification and deletion; immutable storage and separation between log administration and agent operation can strengthen integrity. Control who may view sensitive records, encrypt them as appropriate, and document what is collected, where it is stored, and how it can be exported or deleted.
Rank #3
Set retention with privacy, compliance, legal, and security owners. There is no universal retention period: applicable jurisdiction, sector, data type, and organizational policy all matter. Minimize sensitive content while keeping enough context to investigate actions and their effects. Include relevant sandbox, access, proxy, and network records rather than relying on model or orchestration logs alone.
How should you roll out monitoring and audit?
- Inventory the agent. Record its owner, model, tools, connected systems, data access, permissions, and intended tasks.
- Establish identity and limits. Assign a distinct identity, narrow credentials and tool access, define allowed action schemas, and identify actions requiring approval.
- Define the event schema. Choose timestamps, principal and agent identifiers, run and trace IDs, and the event fields needed to connect requests, model calls, retrieval, tools, approvals, and outcomes.
- Instrument the execution path. Connect telemetry across the orchestrator and dependent services. Record denials and blocked attempts as well as successful actions.
- Build dashboards and alerts. Track service health, resource use, evaluation outcomes, unusual tool-call rates, denied actions, policy violations, and deviations from the agent’s normal behavior.
- Protect and govern the records. Apply access and integrity controls, and establish storage, encryption, retention, and deletion rules with the relevant privacy, compliance, legal, and security owners.
- Test coverage and response. Exercise realistic failures and threat scenarios. Verify that an investigator can reconstruct what happened, identify who initiated or authorized it, and use the shutdown and network-restriction procedures.
What standards and platform features can help?
OpenTelemetry and AI observability conventions
Microsoft recommends OpenTelemetry-aligned GenAI traces and metrics alongside an organization’s existing monitoring and security operations stack. A shared trace structure can help connect agent runs with orchestration, model, retrieval, and tool events, but verify that the instrumentation actually covers the events your investigation requires.
OWASP Agent Observability Standard project
The OWASP Agent Observability Standard project describes observability in terms of being instrumentable, traceable, and inspectable. Its project page identifies OpenTelemetry and OCSF for tracing, and CycloneDX, SWID, and SPDX for inspectability. Treat it as an evolving project, not an established, mature, versioned compliance standard.
Microsoft Purview audit features
Microsoft Purview documentation describes capturing prompts and responses for supported AI applications in a unified audit log, with interaction timing and potentially service and file references. It also describes audit search, eDiscovery, and retention features. This is product-scoped coverage: do not assume it records every custom agent, tool invocation, approval, or infrastructure event in an organization’s environment.
Questions to ask about any platform
- Which prompts, retrieval events, tool calls, policy checks, approvals, and outcomes does it capture?
- Can actions be attributed to distinct agents and correlated across agents and services?
- Can records be altered or deleted, and what integrity protections and access controls are available?
- Can the system alert on behavior and evaluation results, and integrate with incident response?
- What are the controls for privacy, data residency, retention, and deletion?
- Can telemetry be exported using documented, interoperable schemas?
Validate these points against the platform’s documented scope and your own end-to-end tests; product documentation alone does not establish coverage for a particular deployment.
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