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Start with the event’s actor fields, not the account name alone. In Microsoft Entra, inspect agentType under initiatedBy, performedBy, and targetResources; any value other than notAgentic indicates agent involvement. Then identify the agent and distinguish the originating principal from the executor by checking delegation data. Finally, correlate the audit record with runtime telemetry through a shared request, trace, thread, or run identifier. If the record contains only a shared service account and no agent or delegation context, it cannot establish which agent acted.
What an audit log can—and cannot—tell you
An audit event normally records an operation, time, resource, and principal. That principal might be a person, application, service account, or service agent. An AI agent can appear as any of those identity classes, so the presence of an application ID or non-human account is not proof that an AI system performed the action.
Reliable attribution requires three separate answers:
- Was an agent involved? Look for an explicit agent marker in the identity objects recorded with the event.
- Which identity executed the operation? Separate the runtime or service-agent identity from the original user, application, or workflow that initiated the work.
- Which run produced this specific change? Correlate the audit event with the agent’s tool-call and decision telemetry using a shared identifier.
These answers are platform-specific. Microsoft Entra, Google Cloud, Azure Application Insights, Microsoft 365 audit, and OpenTelemetry expose different fields and cover different parts of an agent workflow. Always verify the schema emitted by your identity provider, agent runtime, and destination application before making a finding.
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Evidence that an event involved an AI agent
Use explicit agent markers first
Microsoft Entra documents an agentType property on the event’s initiatedBy, performedBy, and targetResources identity objects. Microsoft’s rule is straightforward: a value other than notAgentic indicates agent involvement. Check all three locations because an agent can be the initiator, the performer, a target resource identity, or more than one of them.
Do not infer agent involvement from a friendly display name such as “automation,” an application registration, or a service principal. Those labels describe an identity, not how the identity was used. An application may run ordinary deterministic code, while an agent may execute through a service identity.
Read the identity type and supporting fields
Entra agent activity can expose different identity types for an agent, its blueprint, and an agent user account. The documented auditActivityPerformer structure can include agentType, appId, and blueprintId. Capture those values in your incident notes rather than reducing the event to a display name. They can distinguish two agents that use similar names or the same human-facing application.
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A marker establishes that the event is agentic; it does not by itself tell you what the model decided, which tool was called, or who asked for the work. Those questions require runtime and delegation records.
A practical investigation workflow
1. Preserve the raw event
Export the unmodified event before filtering or transforming it. Record the timestamp and time zone, action name, resource, tenant or project, initiating identity, performing identity, target resources, correlation fields, and the log source. Keep the original JSON alongside any normalized case record so that a later schema interpretation does not erase evidence.
Start with the event that changed the resource. A chat transcript or orchestration record can explain intent, but it is not a substitute for the destination system’s audit event.
2. Inspect every actor location
- Open
initiatedByand note the principal type, identifier, and anyagentType. - Open
performedByand repeat the check. This is often the service or runtime identity that actually called the API. - Inspect
targetResourcesfor agent identity objects and agent markers. A target can carry useful identity context even when the initiating actor is a user. - Record
appId,blueprintId, and equivalent identifiers exactly as emitted.
In Microsoft Entra, any agentType value other than notAgentic is the explicit signal. If the property is absent, do not convert that absence into a “human” classification; it means only that this event does not expose the documented marker.
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3. Separate origin from execution
An agent may execute an operation for an originating principal. Find delegation history, “on behalf of” data, or an equivalent chain that links the requestor to the executor. Google Cloud’s documented serviceDelegationHistory can show the original principal, the service agent that executed the action, and the service to which that agent belongs.
Write the chain in plain language: original principal → orchestrator or agent → service agent → destination API. If one link is missing, mark it as unknown instead of filling it with the nearest account name.
4. Correlate with runtime telemetry
Search the agent runtime for a request, trace, thread, or run identifier that also appears in the application audit record. Azure SRE Agent uses a trace ID to correlate events within a request. OpenTelemetry GenAI semantic conventions include gen_ai.agent.id and gen_ai.thread.run.id; these attributes provide a useful structure for telemetry when the instrumented systems emit them.
Correlation is stronger when several independent records agree on the same identifier, time window, resource, and action. A timestamp match alone is weak evidence in a busy environment. Also verify that the identifier survives each hop; a runtime may create a trace that the destination application never stores.
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5. Check the correct log surface
Agent execution and cloud resource management are often logged separately. In the Azure SRE Agent example, Application Insights contains tool calls, model invocations, incident handling, and approval decisions. Azure Activity Log covers Azure Resource Manager operations on the agent and related resources. Search both surfaces and join them with the trace or request identifier where available.
Similarly, a Microsoft 365 audit record for a Copilot or other AI application may document application identity activity, while the downstream service keeps its own operation log. The absence of a tool call in the identity provider’s event does not prove that no tool call occurred; it may be recorded only by the runtime or destination service.
6. State the confidence and the gap
Conclude with what the records establish and what they do not. For example: “The Entra event is agentic because agentType is present; delegation history identifies the requesting user; the trace links the change to run 8f…; the destination log does not preserve the agent ID.” This is more defensible than labeling the action simply “AI-generated.”
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Microsoft Entra: fields to inspect
| Location | What it can show | How to use it |
|---|---|---|
initiatedBy |
The identity that started the activity, including an agent marker when emitted | Check agentType; capture principal and application identifiers |
performedBy |
The identity that carried out the operation | Compare it with the initiator; determine whether a service or agent executed on someone else’s behalf |
targetResources |
Resources and identity objects affected by the event | Look for additional agent markers and resource-level context |
auditActivityPerformer |
Agent-oriented performer details documented by Entra | Record agentType, appId, and blueprintId when present |
Agent identity, blueprint, and agent user account activities can appear as different identity types. Preserve the type as well as the ID so that two records with similar names are not mistakenly merged.
Delegation: why the executor may not be the requester
Delegation is the key distinction between “the agent changed the resource” and “the agent decided to change the resource.” A user, ticketing system, or workflow can initiate a task; an agent or service agent can then obtain a token and perform the API call. The audit event may show only the executor unless the platform records the delegation chain.
Google Cloud service delegation history
Google Cloud’s service-account examples illustrate the fields to seek: serviceDelegationHistory can carry the original principal, the executing service agent, and the service associated with that agent. Use those values to attribute the request and execution separately. If the history names only a service agent, you can attribute execution to that service but not necessarily to a particular user or agent run.
When delegation data is absent
Do not infer the origin from a nearby login event or from the owner of a service account. Mark the initiating principal as unknown, then look for an upstream workflow record with a shared request or trace ID. If no such record exists, the evidence supports only the executor identity.
Shared service accounts and ambiguous attribution
Multiple agents using one service account create an attribution boundary that the audit log cannot cross. The account may prove that an automated component had permission, but it cannot identify which agent, version, prompt, or run used the credential. Human users can create the same ambiguity when they share a break-glass or operations account.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsAWS Agentic AI Lens guidance recommends distinct service identities for agents, with trust boundaries that prevent an agent from assuming a human-designated role. Treat that as design guidance, not a universal compliance rule. The practical benefit is a one-to-one mapping between an identity and an agent, which makes downstream audit records easier to interpret.
- Prefer a separate identity for each production agent or narrowly defined agent role.
- Keep development, test, and production agents on different identities.
- Preserve the originating principal in delegated credentials or request metadata.
- Do not recycle an identity immediately after an incident; retain its mapping in your case records.
Distinct identities improve attribution but do not replace runtime correlation. An identity can still run many concurrent tasks, so a run or trace identifier is needed to connect one action to one execution.
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Correlating agent decisions with downstream changes
What to correlate
- Request or trace ID: joins events across services participating in one request.
- Thread or run ID: identifies a particular agent conversation or execution.
- Agent ID: identifies the logical agent, independent of a short-lived worker.
- Timestamp and resource: validates that the records describe the same operation.
- Approval decision: shows whether a human approved an action before execution.
OpenTelemetry’s GenAI conventions include gen_ai.agent.id and gen_ai.thread.run.id. These are telemetry attributes, not a guarantee that every API or audit destination will store them. Test the complete path from prompt or event, through orchestration and tool call, to the destination application.
Azure SRE Agent example
Azure SRE Agent documentation states that the agent logs tool calls, model invocations, incident handling, and approval decisions to Application Insights. Use the documented trace ID to join those records within a request. Then check Azure Activity Log for the Azure Resource Manager operation that changed the resource. A matching trace, action, resource, and time window provides a much stronger attribution than either log alone.
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When identifiers do not survive
Some connectors drop custom headers or create a new trace at each hop. If the destination audit record lacks the run or trace ID, search by a narrow time window and exact resource operation, but label the result as a correlation rather than a direct link. If several agent runs could have produced the same operation, the available logs do not establish which one did.
Comparing platform approaches
| Platform or layer | Actor specificity | Delegation visibility | Correlation and coverage |
|---|---|---|---|
| Microsoft Entra audit | agentType can explicitly mark agent involvement; identity types and IDs add detail |
Depends on the event’s initiating and performing identity fields | Use alongside runtime telemetry and destination logs |
| Google Cloud audit examples | Service and service-agent identities identify the executor | serviceDelegationHistory can show original principal, executing service agent, and associated service |
Verify which downstream systems retain the delegation fields |
| Azure SRE Agent telemetry | Runtime records include agent actions such as tool calls and model invocations | Approval and incident-handling events can provide workflow context | Application Insights trace IDs correlate events; Azure Activity Log covers resource-manager operations |
| OpenTelemetry GenAI conventions | Attributes such as gen_ai.agent.id describe instrumented telemetry |
Delegation is not automatically supplied by the convention | Useful for cross-service structure, but destination systems may omit the attributes |
| Shared service account | Identifies only the shared account | Usually insufficient without separate delegation records | Cannot provide per-agent attribution by itself |
Three common investigation outcomes
Explicitly agentic and fully correlated
The Entra event has agentType set to an agent value, auditActivityPerformer includes an appId and blueprintId, delegation history names the requesting principal, and Application Insights contains the same trace ID for the tool call and approval. You can attribute the operation to that agent run for that principal, subject to the destination log retaining the same resource and action.
Agent executor identified, originating user unknown
The event identifies a service agent and the runtime trace links it to a resource change, but no delegation history or upstream request record names the requester. Report the service agent and run as established facts. Report the original principal as unknown; do not assign the action to the account that happened to be active nearby in time.
Shared account with no agent marker
The event lists one service account, has no agentType, and the destination application does not preserve a run or trace ID. The correct conclusion is that the account performed the operation, while the specific human, agent, or workflow cannot be determined from the available logs. Treat this as an attribution gap and improve identity isolation and correlation for future events.
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Identity design
- Assign distinct service identities to agents where practical.
- Use separate identities for agents with different privileges or trust boundaries.
- Document the mapping from identity ID to agent name, version, environment, and owner.
- Prevent an agent identity from assuming roles intended for human operators unless that path is explicitly approved and logged.
Telemetry design
- Propagate a stable request or trace ID through orchestration, model calls, tool calls, and destination APIs.
- Emit an agent ID and run or thread ID in runtime telemetry.
- Log model invocations, tool calls, approvals, retries, and failures, not only the final resource mutation.
- Confirm that connectors and destination applications preserve the identifiers instead of replacing them.
Validation tests
- Run a harmless test action through each agent and record every expected log surface.
- Verify that the initiating principal, performing identity, agent marker, and run or trace ID appear where documented.
- Check that the destination audit event can be joined back to the runtime record without relying solely on timestamps.
- Repeat with concurrent runs to ensure the identifiers distinguish them.
- Document any hop that drops identity or correlation context and treat it as an attribution limitation.
Troubleshooting an inconclusive audit trail
The event shows an application but no agent marker
Check the agent runtime and destination application logs. The identity provider may not emit agent-specific fields for that event type. If those systems also lack agent or run context, classify the event as unproven rather than human-generated.
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The trace IDs do not match
Determine whether a proxy, queue, connector, or separate worker created a new trace. Search for a documented parent ID, message ID, thread ID, or run ID. If no stable relationship exists, record the records as time-based correlation only.
Several agents use the same identity
Use runtime logs, deployment metadata, and tool-call records to narrow the candidate set, but do not claim per-agent certainty unless a unique run or agent identifier is present. Plan distinct identities for future deployments.
The destination application has no agent fields
Ask whether it accepts propagated request metadata or exposes its own audit extension. If it does not, preserve the upstream trace and state that the destination log cannot independently establish agent attribution.
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A useful finding names the evidence, not just the conclusion. Include:
- the event ID, source, timestamp, action, and resource;
- the initiating and performing identities, including identity types;
- the exact
agentType,appId,blueprintId, or delegation fields observed; - the runtime agent ID and run, thread, request, or trace ID;
- the log surfaces consulted and any missing hop;
- a confidence statement explaining whether attribution is direct, correlated, or indeterminate.
For example: “The resource update was executed by service agent X. Entra marks the event as agentic, and the runtime trace links it to agent Y’s run Z. Delegation history identifies principal P as the requester. The destination system does not retain the run ID, so its record alone would not support per-agent attribution.” This wording keeps the executor, originator, and evidence limits distinct.
Limits of current documentation
Official platform documentation describes implementation fields and product-specific logging behavior; it does not provide a cross-platform statistic for how often audit logs identify AI-agent actions. There is no general prevalence percentage to apply to an organization’s logs.
Likewise, the documented fields do not guarantee that every downstream application preserves agent identity, delegation history, or OpenTelemetry attributes. Treat the live schema emitted by each system as authoritative for your deployment, and test the full tool-call path. When the required context is missing, the technically correct result is “not established,” followed by a logging or identity-isolation improvement—not an inference based on a shared account or timing alone.
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- Locate and preserve the raw destination audit event.
- Inspect
initiatedBy,performedBy, andtargetResources. - Check Microsoft Entra
agentType; values other thannotAgenticindicate agent involvement. - Capture agent identity type,
appId, andblueprintIdwhen present. - Find delegation history, including Google Cloud
serviceDelegationHistorywhere applicable. - Separate the original principal from the executing service or agent.
- Search Application Insights, agent runtime logs, and resource-management logs as separate surfaces.
- Join records with a request, trace, thread, or run ID; validate resource, action, and time.
- Check whether the destination application preserved the identifiers.
- State exactly what is proven, what is correlated, and what remains unknown.
Bottom line
To identify AI-agent actions, require an explicit agent marker or equivalent runtime evidence, then prove the execution chain with delegation data and cross-system correlation. Microsoft Entra’s agentType is a direct signal when present; service-agent delegation explains who executed work for whom; trace and run identifiers connect that work to a specific agent execution. A shared service account without those fields identifies automation, not the individual agent behind it.
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