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Start with the affected run, locate the turn in which the unexpected action occurred, and follow the recorded steps in order: model generation, tool request, tool result, handoff, and later activity. Then check the application and the system the agent interacted with to establish whether the action actually took effect. A trace can help explain what was recorded; by itself, it does not prove that every event was captured or that an external change occurred.
First, distinguish what the agent requested from what actually happened
“The agent did it” can describe several different events. A model may produce a tool request; the application may invoke the tool; the tool may return a result; and a separate system may or may not apply the requested change. Treat these as separate facts until the records establish how far the action went.
| Event | What it establishes | What it does not establish by itself |
|---|---|---|
| Model output | What the model generated in the recorded turn, if the system captured that output. | That the application executed a request contained in the output. |
| Tool invocation | That the application recorded or attempted a call to a named tool, with arguments if those were captured. | That the tool completed successfully or changed external state. |
| Tool result | What the tool returned to the application, including a recorded error when available. | That the result accurately reflects the final state of another system. |
| Downstream audit or transaction record | What the system that owns the affected state recorded, such as a completed transaction or change. | Why the agent chose the action; correlate it with the run and application records for that. |
This distinction is central to a reliable investigation. A trace is evidence about activity recorded by an instrumented system, not a universal audit guarantee. Confirm consequential effects in the target system’s own records.
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If the action could continue causing harm, use the controls your team already relies on to pause or restrict the agent or its access. Preserve the relevant records as you do so. Avoid starting with a replay or another live run: it can repeat the action or alter the state you are trying to understand.
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Write down the incident’s known boundaries before searching. Record the affected agent or workflow, user or session if known, approximate time window and time zone, environment, and the action or state that prompted the investigation. If the report is vague, describe the observable outcome rather than assuming which component caused it—for example, “a record was changed” rather than “the model changed the record.”
- Identify which environment was involved, such as development or production, and whether the agent was operating under a particular deployment or configuration.
- Record the first known time of the outcome and any related request, transaction, or object identifier available from the affected system.
- Note what is confirmed, what is reported but unverified, and what remains unknown. Keep those categories separate as evidence comes in.
Find the session, turn, and event sequence
Search the system’s session logs, event stream, or trace interface using the best identifiers you have. If the product supports session, turn, and span inspection, move from the broader session to the specific turn and then to the individual recorded operations. OpenAI’s API tracing guide describes this kind of inspection, while the Agents SDK documentation describes traces that group spans into an end-to-end workflow with timestamps and parent relationships. These are capabilities documented for those products; other systems may expose different records or use different labels.
- Locate the run. Search by session or run identifier when available. Otherwise narrow by the approximate time window, user, agent, environment, or related request identifier. Confirm that the record belongs to the affected environment and not a nearby test run.
- Open the relevant turn. Find the turn closest to the observed action. Inspect adjacent turns too: context or a tool result from an earlier turn may explain a later decision.
- Read the recorded steps in chronological order. Note each generation, tool call, tool result, handoff, guardrail event, retry, and custom event that appears. Do not infer a missing step merely because one would normally be expected.
- Follow parent-child relationships. Where spans have parent identifiers or nesting, use them to distinguish the top-level workflow from a nested operation or delegated agent. Check timestamps and status as well; similar-looking events may belong to different branches or attempts.
- Record the trace’s scope. Note which turns and operations are present, and whether inputs, outputs, arguments, or results are absent or redacted. A visible partial sequence should not be treated as a complete history without checking capture settings.
OpenAI describes its tracing dashboard as showing an agent’s recorded steps, including inputs, outputs, duration, and status. The qualification “recorded” matters: what a viewer can show depends on the product, runtime, and configuration.
Reconstruct the decision path around the action
Once you have the relevant sequence, work backward from the attempted or completed action to the generation and context immediately before it. Build a timeline rather than relying on a single event or a summary view.
Inspect the generation before the tool call
Read the model input and output if they are available. Check what instructions, conversation history, retrieved material, or other context the model received at that point. Then compare the generated response with the tool request that followed. A model may propose an action in its output, while application logic determines whether and how that proposal becomes a tool invocation.
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Look for a mismatch between the instruction and the generated action, an ambiguous request, relevant context that was absent or stale, or retrieved content that may have influenced the response. These are hypotheses to verify against the records and configuration that applied at the time, not conclusions that can be drawn from an unexpected outcome alone.
Examine the tool request and its result
For each relevant call, capture the tool name, arguments, timestamp, status, returned result, and error details if recorded. Ask whether the arguments match the action described in the report, whether the tool returned success or failure, and whether the application retried the operation. If the system represents a retry as a separate span or event, keep each attempt distinct; a later success can follow an earlier failure, and repeated calls may matter when the action is not idempotent.
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Check the tool implementation and the permissions available to it at the incident time. A tool can behave differently from the model’s apparent intent because of argument handling, validation, defaults, retries, timeouts, or external state. The records may narrow these possibilities, but a trace alone may not establish what the tool did internally.
Follow handoffs and nested agents
If the workflow handed control to another agent or component, continue along that branch. Identify what was passed to the next step, which agent or tool acted, and how control returned, if it did. A top-level trace can conceal the decision that occurred in a nested workflow unless the child operations are also recorded and linked.
Keep the attribution precise: identify which recorded component made the request, which component executed it, and which system reports the resulting state. A handoff or parent-child relationship can help with attribution, but it does not prove that all relevant components were instrumented.
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Verify the outcome in the system that owns the state
Use the affected service’s audit log, transaction history, object history, or equivalent authoritative record to determine whether the external change occurred. Correlate records using timestamps and any shared request, transaction, user, or object identifiers. If the trace says a tool returned success but the target system shows no change, investigate the gap rather than treating either record as a complete answer.
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- Establish whether the intended target object or account was affected.
- Determine when the target system recorded the change and whether it was later reversed or modified.
- Compare the target’s record with the tool arguments and response, including any identifiers returned by the tool.
- Mark the effect as confirmed, not confirmed, or unresolved based on the records—not on the model’s wording.
Compare the evidence with the behavior that was intended
After establishing the sequence and outcome, compare the run with the instructions, tool definitions, permissions, relevant context, and deployment configuration that were active then. If possible, retrieve the versioned artifacts or deployment record for the incident time rather than inspecting only the current setup.
Use the comparison to test plausible points of introduction:
- Model choice: Did the recorded generation select an action inconsistent with the instructions or available context?
- Context: Was information missing, stale, ambiguous, or presented in a way that could reasonably change the decision?
- Tool contract: Did the tool definition accurately describe its effect, required arguments, defaults, and limits?
- Execution: Did application code, validation, permissions, retries, timeout handling, or error recovery change what was executed?
- Handoff: Did a later agent or component receive the intended context and constraints?
- External state: Did a concurrent update or other outside change alter what the tool acted on?
These are diagnostic branches, not a claim that every framework records enough detail to resolve each one. When the records cannot distinguish between explanations, report the uncertainty and identify what additional evidence would be needed.
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Preserve the evidence and reproduce safely
Retain the relevant session and turn identifiers, trace or export, captured tool arguments and results, timestamps, application logs, and downstream records under your team’s incident process. Keep the time zone and source of each timestamp clear, and preserve the versions of instructions, tool definitions, permissions, and deployment configuration that applied during the run where those are available.
OpenAI’s tracing documentation describes inspection and export, but it does not establish a universal legal evidence-preservation standard. Follow your organization’s applicable security, privacy, retention, and incident procedures when collecting or sharing records. Recorded prompts, outputs, arguments, or results may contain sensitive information; limit access and use approved handling practices.
Reproduce the behavior only if doing so can clarify the cause without risking another consequential action. Prefer a controlled environment, a test account or safe substitute, and inputs that isolate the suspected condition. Preserve the original evidence first; a reproduction is a new run, not a replacement for the incident record. If the behavior does not recur, that does not by itself disprove what happened in the original environment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Understand gaps in traces before drawing conclusions
Trace fields and availability depend on the framework and configuration. The OpenAI Agents SDK documentation describes controls for enabling or disabling tracing; its Python documentation also describes sensitive-data settings and states that tracing is unavailable for organizations operating under an OpenAI API Zero Data Retention policy. Therefore, a missing trace or field is not proof that an action did not happen.
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When expected evidence is absent, check the active runtime, tracing and export settings, retention policy, and data-capture or redaction settings. Establish whether the trace covers the affected run and whether the relevant fields were eligible to be recorded. Do not treat a missing prompt, argument, or result as evidence that the value was empty or that no operation occurred.
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For teams using cross-framework telemetry, AWS CloudWatch documents OpenTelemetry tracing for multiple agent frameworks, with traces that can expose model calls, tool calls, and orchestration steps. That is one documented telemetry route, not a requirement to adopt a particular service and not proof that every downstream side effect will appear in a trace.
Turn the incident into a prevention and evaluation change
Use the cause—or the remaining uncertainty—to improve the next investigation and reduce avoidable risk. Add explicit logging around tool execution and external effects so that a request, an invocation, a response, and a confirmed state change can be correlated. Scope permissions to what the workflow needs, and require human confirmation for consequential actions when appropriate.
Create a regression case from the incident using controlled inputs and a clearly defined expected outcome. After changing instructions, tools, permissions, or orchestration, evaluate representative traces to check whether the intended behavior persists. OpenAI’s evaluation guidance recommends inspecting representative workflow traces before formalizing graders; use that inspection to decide what behavior to measure, rather than assuming a grader can explain an event it was never designed to observe.
For future runs, decide which evidence is essential before an incident occurs: identifiers that connect sessions to requests, timestamps, tool arguments and results, handoff relationships, downstream transaction IDs, and the retention and redaction rules for those records. Instrumentation is useful only if it records the evidence the team needs and can be accessed under the organization’s data controls.
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