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To monitor and audit an AI agent, connect its identity and each run to a timestamped, end-to-end record of model calls, retrieved information, tool actions, permissions, policy decisions, and outcomes. Watch both system health and agent behavior, protect the resulting evidence from tampering, and keep a tested way to stop the agent. The goal is to answer two questions: “What happened?” and “Who or what authorized it?”
What should an AI agent audit trail capture?
Use a consistent event schema and trace IDs to connect activity across the orchestrator, model services, retrieval systems, tools, and connected applications. Microsoft recommends OpenTelemetry-aligned GenAI conventions and telemetry sufficient to reconstruct incidents. Adapt the fields to the agent’s actual risk and data policy rather than logging every piece of content by default.
| Event area | Record | Why it matters |
|---|---|---|
| Identity and context | Timestamp; agent identity; human or service principal involved; conversation, session, and run IDs; model and relevant version details. | Lets an investigator attribute an action and connect it to the request that initiated it. |
| Request and model activity | Inputs and outputs when permitted by the data policy; model-call timing and status; relevant evaluation or policy results. | Provides context for the agent’s behavior without assuming that uptime or error logs reveal answer quality. |
| Retrieval | Sources or records retrieved, their identifiers or references, and the retrieval result needed to understand what information informed the run. | Helps establish whether an answer or action was grounded in expected material. |
| Tools and connected systems | Tool name, arguments appropriate to the data policy, granted permissions, call result, and any relevant sandbox, access, proxy, or network events. | Shows what the agent attempted and what external systems actually did. |
| Controls and outcome | Approval or denial, policy decision, blocked action, task result, and whether the run completed, failed, or was stopped. | Preserves evidence of safeguards as well as successful actions. |
Record enough to explain consequential decisions and actions, but do not treat private model reasoning as a routine logging requirement. Define which prompt and response content may be stored, who can view it, and how sensitive values should be minimized or redacted.
What should you monitor beyond uptime?
Track conventional service health alongside measures of task quality and behavior. Microsoft Learn’s Observability for Generative AI and agentic AI systems, last updated March 17, 2026, notes that uptime and error rates alone are not good indicators of AI quality and reliability.
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- Service health: latency, errors, request volume, token use, resource consumption, and tool-call volume.
- Task performance: task outcomes, evaluations of quality or groundedness where applicable, and whether tool use was correct for the task.
- Security and policy: denied actions, policy violations, unusual permission use, and unexpected access or network activity.
- Behavioral change: meaningful deviations in tool-call rates, resource use, action patterns, or outcomes compared with a baseline for that agent and workload.
Set thresholds and alerts around expected behavior for each agent rather than treating every deployment as if it had the same normal pattern. A sudden increase in calls to a sensitive tool, for example, may merit investigation even when the service is returning successful responses.
How do you roll out monitoring and auditing?
- Inventory the agents. Document each agent’s owner, purpose, model, tools, connected systems, data access, and permissions.
- Assign distinct identities. Give each agent its own identity, separate from human users and other agents, and scope credentials and permissions to the task. Prefer short-lived credentials where the environment supports them.
- Define the event schema. Specify the identity, run IDs, timestamps, tool and retrieval events, policy decisions, and outcomes to be captured. Set data-handling rules for inputs, outputs, and tool arguments.
- Connect the execution path. Propagate trace IDs through orchestration, model calls, retrieval, tools, approvals, and outcomes. Include relevant sandbox, access, proxy, and network events.
- Instrument safeguards as events. Log approvals, denials, blocked actions, and failures alongside successful actions so an investigation can see what the agent tried and which controls intervened.
- Build dashboards and alerts. Cover service health, evaluations, unusual tool activity, resource consumption, denied actions, and deviations from the agent’s baseline.
- Test incident reconstruction. Use realistic failure and threat scenarios to check whether an investigator can determine what happened, which identity acted, what authorized the action, and how the run ended.
- Set evidence and response procedures. Agree on access, integrity protections, retention and deletion rules with security, privacy, compliance, and legal owners; exercise the procedure for stopping an agent and restricting its communications.
How do you protect audit evidence without over-collecting?
Agent telemetry can contain personal, confidential, or regulated information. Define a data contract that states what is collected, its purpose, who can access it, how it is encrypted, where it is stored, and how long it is retained. Minimize sensitive content while preserving the context needed to investigate consequential actions. Retention periods depend on jurisdiction, sector, data, and organizational policy; there is no universal period established here.
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Restrict log access and separate it from the identities or services that generate the records. Consider immutable storage or other integrity controls that make unauthorized alteration or deletion detectable. Connect agent events to security operations and incident response rather than leaving them only in an application dashboard.
How should you respond to suspicious agent behavior?
Decide in advance who can contain an agent and how. The UK National Cyber Security Centre’s August 20, 2026 guidance on managing agentic AI cyber risk says agents should have unique identities and that organizations should be able to halt autonomous activity immediately if an incident is detected or reported.
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- Stop or disable the active run using the organization’s tested shutdown path.
- Revoke or suspend the agent’s credentials and restrict its network or model communications as needed to prevent further actions.
- Preserve the relevant trace, tool, access, and policy events for investigation, with access limited to authorized responders.
- Review what the agent attempted, what systems acted, what safeguards intervened, and whether credentials or connected systems need remediation.
Can a platform audit log replace agent-level observability?
No single product log should be assumed to cover every custom agent, tool, or connected service. Microsoft Purview documentation describes capturing prompts and responses for supported AI apps in a unified audit log, with interaction timing and potentially service and file references. Its audit search, eDiscovery, and retention features apply within the documented product scope; they do not establish that every custom agent action is captured. Use platform audit features as one evidence source, then verify coverage across the full execution path.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which standards and platform capabilities should you check?
OpenTelemetry GenAI semantic conventions provide a way to align traces and metrics with an interoperable telemetry approach. The OWASP Agent Observability Standard project describes desired properties as instrumentable, traceable, and inspectable, and identifies OpenTelemetry and OCSF for tracing and CycloneDX, SWID, and SPDX for inspectability. It is an evolving project, not a settled, mature compliance specification.
When assessing a platform or implementation, verify these capabilities in its documentation and actual event exports:
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- Coverage of prompts, retrieval, tool calls, policy checks, and outcomes.
- Identity attribution and correlation across agents and services.
- Integrity protections, access controls, and exportability.
- Alerting, evaluation, and integration with incident response.
- Privacy, data residency, retention, and deletion controls.
- Interoperability through documented schemas and trace formats.
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