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To determine what an AI agent did and who started it, begin with the session or request record, follow its trace through turns and spans, and then match the execution identity with any initiating user or workflow identity. Timestamps, parent-child span links, correlation IDs, tool arguments, results, status, and duration reconstruct the technical sequence. Attribution fields establish whether a person delegated the work, an automated workflow started it, or the agent acted autonomously. If a record or field is missing, treat that as an evidence gap caused by logging, access, retention, or redaction—not as proof that the action never happened.
What does an AI agent activity log contain?
An activity log is a structured record of related operations rather than a single line saying “the agent did this.” A useful trace has several navigation levels:
- Session or request: the overall conversation, job, or API request being investigated.
- Turn: one exchange or execution cycle inside the session. A session can contain multiple turns.
- Span: a timed operation inside a turn, such as a root-agent run, delegated subagent run, model generation, or tool call.
- Event details: timestamps, status, duration, model or tool name, inputs, outputs, arguments, and results when content recording is enabled.
OpenAI’s Agents API tracing documentation describes this session–turn–span structure and the timeline view used to navigate it: Agents API tracing. Other platforms use different labels, but the investigative principle is the same: identify the outer request, then descend through related operations until the consequential tool call is visible.
How do I find the session or request that contains the action?
Start with the narrowest reliable identifier you have: request ID, session ID, conversation ID, job ID, ticket number, approximate timestamp, or the affected resource. Searching by a broad time window first can return unrelated runs, especially when an agent executes scheduled work.
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Using an Agents API dashboard
In the OpenAI dashboard, the documented path is Logs → Agents. Select the project, search for the session, and narrow the results by model, status, or date. Open the matching timeline and expand its turns and spans to see the root agent, any subagent, model generations, and tools.
When the dashboard does not show the trace
Trace export must be enabled for the OpenAI tracing workflow, and the API key used to read traces needs trace-read permission or broader agent-read permission. A reviewer who can see application data but lacks trace access may conclude incorrectly that no run exists. Verify the project, time range, environment, and permissions before declaring the record absent.
Preserve the original identifiers
Copy the session or request ID, the first and last timestamps, and the project or environment name into the case record. Do not rely on a screenshot alone; identifiers let another reviewer retrieve the same event sequence and detect whether a later export has been filtered or redacted.
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Correlation fields show which events belong together and how one operation caused another. In Azure SRE Agent events, Microsoft documents the following relationships:
| Field | What it helps establish |
|---|---|
TraceId |
Groups events belonging to one request or end-to-end execution. |
SpanId |
Identifies one operation within that trace. |
ParentSpanId |
Links an operation to the span that invoked it, revealing nesting and delegation. |
ThreadId |
Connects an event to the conversation or thread context. |
LogTimestamp |
Places the event in chronological order. |
CorrelationId |
Provides an additional grouping key used by the service. |
The field definitions and audit examples are documented by Microsoft at Audit agent actions. Sort events by timestamp, then verify parent-child links rather than assuming that adjacent log lines are causally related. A tool call may finish after a later-looking bookkeeping event, and parallel child spans can overlap.
Build a minimal event tree
- Use the request’s
TraceIdor equivalent to collect all related events. - Identify the root span and record its start and end times.
- Attach each child span to its
ParentSpanId; flag spans whose parent is missing. - Order siblings by timestamp and duration, noting overlaps that indicate parallel execution.
- Mark the span that performed the consequential action, such as a write, send, approval, or deletion.
A missing parent link weakens the sequence you can prove, but it does not automatically invalidate every event in the trace. Record exactly which relationship is unavailable.
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How should I inspect model and tool events?
Read the events surrounding the consequential tool call, not just the final assistant message. The model output may describe an intended action while the tool span shows what was actually submitted and whether the system accepted it.
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Check the model name or deployment, start and end times, status, and recorded input and output. Inputs may include system instructions, retrieved context, user text, and previous tool results; outputs may contain a tool-selection decision or arguments. If content recording is disabled or fields are blank, state that the trace proves a generation occurred but not the unrecorded content.
Tool spans
Capture the exact tool name, argument payload, invocation time, duration, status, and returned result when available. Compare the arguments with the resource that changed. A successful tool response establishes that the tool reported success; it may not by itself prove that an external system committed the change unless that system provides its own audit record.
Failures, retries, and parallel calls
Look for retry attempts, timeout statuses, and child spans created after an error. Distinguish an attempted call from a completed call, and distinguish a tool response from a later compensating action. Preserve both failed and successful attempts because the first call may have partially changed an external system even if the agent later reported an error.
How can I tell who initiated an agent action?
Attribution requires two different identities: the actor that executed the operation and the principal that initiated or delegated it. Do not collapse them into one “user” field.
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For an action started by a person, look for a human user principal together with the agent or service identity. Google Cloud documents this dual attribution model for agentic actions: execution and audit logs can record both the SPIFFE-based agent identity and the initiating human user. Filter for is_agentic=true, then narrow by the agent identity or user principal using the guidance at Gemini Enterprise Agent Platform observability.
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Autonomous or scheduled work
A scheduled job, event trigger, or policy-driven run may have no initiating human for that particular execution. In that case, the unique agent or service identity is the actor. Trace the trigger record—such as a schedule, queue message, or upstream workflow—to identify the system that launched the run, but do not invent a person when the logs do not name one.
Identity checks
- Verify the identity’s tenant, project, environment, and credential scope.
- Match the principal’s timestamp with the session start, not merely with the tool completion time.
- Check whether the agent delegated to another agent; the child agent is an executor, not necessarily the initiator.
- Use the external system’s audit log to confirm which credential actually performed the write.
If the trace records only an agent ID, you can attribute execution to that agent but cannot prove which person requested it. If it records a person but not the agent span, you can establish a request or approval without proving how the action was executed.
What if the action is missing from the log?
An absent event has several possible explanations: logging was disabled, the event category was never captured, retention expired, the reviewer lacks permission, content was redacted, or the action occurred in another project or service. Treat the absence as a limitation on what can be established.
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Check logging coverage and defaults
Google Cloud states that Agent Platform Data Access audit logs are disabled by default, while System Event and Admin Activity logs are always enabled. The distinction is documented at Agent Platform audit logging. Confirm that the category covering the operation was enabled during the relevant period.
Check permissions and monitoring resources
Microsoft’s Foundry tracing guidance notes that querying traces may require permissions on the monitoring resource, separate from permissions on the application itself. Review the access requirements at Trace agent frameworks, then test the query with the same identity used by the investigator.
Check scope, retention, and clock differences
- Search the correct project, subscription, region, workspace, and production or non-production environment.
- Account for UTC offsets and clock skew when joining records from different services.
- Determine whether the platform retained metadata but removed prompts, arguments, or outputs.
- Look for downstream audit records from the database, messaging system, identity provider, or business application.
Document the search scope and the first unavailable link. “No matching record in the queried workspace” is more accurate than “the agent did nothing.”
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Why do technical traces need application-level audit context?
Platform telemetry can show the sequence of calls without explaining the business reason for the run. Salesforce Architects summarizes the requirement this way: “When agents take actions, audit trails must reconstruct not just what happened but why.” The guidance is available at Agentic enterprise trust.
Add an application-level audit record at the point where a business event starts the workflow. Include the initiating event or input, relevant grounding data, requested action, resulting outcome, and any human-review decision. Link that record to the trace or correlation ID. This makes it possible to answer both “which tool call changed the record?” and “which approved business event caused the workflow to run?”
Context worth recording
- Business object and immutable object ID.
- Trigger type, such as user request, schedule, webhook, or policy.
- Initiating principal and approval state.
- Agent and workflow version.
- Decision, action, outcome, and any reviewer override.
Keep business context separate from raw prompts where possible. It gives investigators a durable explanation even when sensitive model content is intentionally not retained.
How should sensitive prompts, arguments, and outputs be handled?
Agent traces can contain personal data, credentials accidentally placed in prompts, confidential retrieved documents, and complete tool payloads. Microsoft recommends disabling content recording in production when it is not required and redacting sensitive data before telemetry capture; see Microsoft’s tracing guidance.
Design the capture policy
- Record identifiers, timestamps, status, and hashes or references when full content is unnecessary.
- Redact secrets and regulated data before sending telemetry to a tracing system.
- Separate restricted payload storage from broadly accessible operational metadata.
- Apply retention, export, and deletion rules consistently to traces and application audit records.
- Limit access to investigators who need the content for a defined incident or compliance task.
Redaction changes what the trace can prove. Note whether an argument was fully recorded, partially redacted, or omitted so later reviewers do not mistake a blank field for an empty value.
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| Decision axis | Questions to ask | Why it matters |
|---|---|---|
| Identity attribution | Does the record show only the executing agent, or both agent and initiating user or workflow? | Separates autonomous execution from delegated work. |
| Correlation depth | Are session, trace, span, parent-span, and thread identifiers available? | Allows reviewers to rebuild nesting and parallel branches. |
| Event detail | Are model inputs and outputs, tool arguments and results, timestamps, duration, and status captured? | Shows what was attempted versus what completed. |
| Logging coverage | Which categories are enabled by default, and which require explicit configuration? | Defines what an absence can legitimately mean. |
| Access and export | What roles, monitoring-resource permissions, and export settings are required? | Prevents investigators from confusing an authorization failure with missing telemetry. |
| Content controls | Can recording be disabled, fields redacted, and retention restricted? | Reduces exposure of sensitive prompts and payloads. |
| Business context | Can application events link the trigger, grounding data, approval, action, and outcome to the trace? | Explains why the technical sequence occurred. |
Document the selected behavior for normal, delegated, scheduled, failed, retried, and redacted runs. Microsoft notes that tracing availability can differ by agent type and may change as features move between preview and general availability; verify current status before relying on a particular interface or event category.
What is a practical review procedure for an incident?
Use this sequence when you must produce a defensible account of an agent action.
- Define the disputed outcome. Identify the changed resource, approximate time, environment, and business impact.
- Locate the outer record. Search for the session, request, job, or trigger using the strongest identifier available.
- Export or preserve the trace. Keep the original event IDs, timestamps, and query scope; record any export or permission limitation.
- Construct the event tree. Join correlation IDs, parent spans, thread IDs, and timestamps; mark parallel branches and missing parents.
- Inspect the decisive span. Read the tool name, arguments, status, duration, and result, then compare them with the downstream system’s audit record.
- Separate intent from execution. Treat model text as recorded reasoning or instruction, and the tool or external-system audit as evidence of an attempted or completed operation.
- Resolve attribution. Record the executing agent, initiating user or workflow, credential, and whether the run was autonomous, delegated, scheduled, or retried.
- Join business context. Link the trace to the application event, grounding data, approval, and outcome that explain why the run began.
- State evidence gaps. Identify disabled categories, missing fields, redaction, expired retention, inaccessible projects, and any conclusion that remains unproven.
The final incident note should distinguish four claims: what the agent attempted, what the tool reported, what the external system committed, and who initiated the workflow. Those claims often come from different records.
What limitations should reviewers record?
Platform interfaces, logging defaults, export permissions, and preview features change. A current page may describe a capability that is unavailable in an older deployment or enabled only for certain agent types. Record the platform, project, region, agent version, query time, and documentation version used for the investigation.
Microsoft’s documentation distinguishes generally available prompt and hosted-agent tracing from workflow and external-agent tracing that may still be in preview at the time of its guidance. Treat preview behavior as subject to change and avoid promising identical fields across deployments. Likewise, a trace can establish a technical sequence while leaving the business trigger, human intent, or final external state unresolved.
Frequently Asked Questions
Can an activity log prove that a human approved the action?
Only if the approval event is recorded with an authenticated principal, timestamp, decision, and link to the run. A user message or agent prompt alone does not establish that a formal approval policy was satisfied.
What is the difference between an agent identity and a service account?
An agent identity identifies the software actor or delegated agent in the trace. A service account is the credential used to access a resource; several agents or workflows may use the same account, so the account alone may not identify the initiating run.
How can I investigate an action that crossed several cloud services?
Start with the originating trace ID, then join each service’s audit record by propagated correlation fields, request IDs, timestamps, and resource IDs. If a service drops the parent context, preserve that break and use the downstream credential and object ID as secondary evidence.
Should I retain full model outputs for every agent run?
Not necessarily. Retain the minimum content needed for operations, security, and compliance, and use references, hashes, or redacted fields when full prompts and outputs create unnecessary exposure. Make the retention choice explicit because it determines what future reviewers can verify.
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