The Tool Desk
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First, contain the incident and define what you need to establish
If the action could cause continuing harm, use your established controls to pause the agent, restrict the relevant tool, or otherwise limit further activity. Preserve the available records while doing so. The right containment step depends on the system and the risk: avoid making an improvised change that destroys evidence or triggers another action.
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Write down the basic scope before searching logs. Record which agent or workflow was involved, the affected user or session, the approximate time window and time zone, the environment, and the action you observed. Be specific about the outcome you are investigating—for example, a record changed, a message sent, or a permission granted. “The agent behaved strangely” is not enough to identify the event you need to reconstruct.
- Distinguish what someone observed from what is confirmed in a system record.
- Capture relevant identifiers, such as a session, run, request, transaction, or affected record ID.
- Note when the incident was noticed as well as when the action may have occurred; those times can differ.
- Keep the investigation within your team’s incident and data-handling procedures, especially if logs contain personal or sensitive information.
The aim is to answer three separate questions: what the agent recorded or requested, what the application actually executed, and what changed in the connected system. Treat them as separate facts until the evidence connects them.
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Find the session, turn, and ordered trace
Begin with the session or run that matches the incident window and identifiers. In a system with session observability, open its session logs or event history, locate the relevant turn, and inspect the events in chronological order. A multi-step workflow may include several turns, nested spans, tool calls, or agent handoffs; looking only at the final response can hide the step where the behavior began.
OpenAI’s Agents SDK documentation describes traces as records that can include model generations, tool calls, handoffs, guardrails, and custom events. In that trace model, spans are grouped into an end-to-end workflow and have timestamps and parent relationships. OpenAI’s API tracing guide describes inspecting sessions, turns, and spans, including recorded generation inputs and outputs, tool calls, and results when those are available. These are descriptions of OpenAI’s products, not a guarantee that every agent framework records the same events or uses the same labels.
Follow the record forward and backward around the unexpected action. Start with the action or tool call, then inspect its parent span and the generation or event that preceded it. Continue through subsequent results, retries, handoffs, and later turns. Parent-child relationships and timestamps can help establish order and attribution, but check their meaning in the observability system you use rather than assuming that every displayed sequence is a complete causal explanation.
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Reconstruct the decision path step by step
- Inspect the generation immediately before the action. Capture the model input and output if the trace exposes them. Look at the instructions and conversation context that were present at that point, not just the agent’s final user-facing answer. Identify whether the output contains a tool request or another instruction that could have led to the behavior.
- Identify the selected tool and its arguments. Record the tool name, the exact arguments sent, and the time of the call. Compare those arguments with the intended operation. A request to update one field is different from a request to replace an entire record, even if both appear under the same tool name.
- Read the tool result, including errors and status. Determine whether the trace shows an invocation, a returned result, an error, a timeout, or a retry. Do not treat a model-emitted request as proof that a tool ran, or a successful-looking response as proof that an external system committed a change.
- Follow handoffs and nested work. If the first agent delegated to another agent or workflow, trace the parent and child spans and inspect the later agent’s inputs, outputs, and tool calls. The step that selected the action may not be the component that executed it.
- Check timing and sequence. Compare timestamps and status across generation, tool execution, handoff, retries, and results. A timeout followed by a retry, for example, can complicate the interpretation of a later success response. Use the application’s own records to determine what occurred when timing is consequential.
- Compare the path with the configuration in force. Check the system instructions, tool definitions, permissions, retrieved material, relevant context, configuration, and deployment version that applied at the time. Current settings may have changed since the incident.
For each step, separate the record from your interpretation. “The trace records a request with these arguments” is an observation. “The model chose this because the instruction was ambiguous” is a hypothesis that needs support from the recorded context and configuration.
Separate a request, an execution, and a real-world effect
These events are not interchangeable:
- Model request: The model emitted a proposed tool call or action.
- Tool invocation: The application or runtime attempted to execute that request.
- Tool result: The tool returned a result or error that may be recorded in the trace.
- External effect: The system that owns the relevant state committed a change, sent something, or otherwise performed the consequential action.
Verify consequential effects in the target system’s own audit, transaction, or event records. Correlate the record using whatever identifiers the application provides, such as a request or transaction ID, and compare its timestamp and outcome with the trace and application logs. If no shared identifier exists, use the narrowest reliable combination of time, actor, operation, and affected object, while noting that the match is less certain.
A trace that contains a tool call can establish that the call was recorded. It does not automatically establish that the target system accepted it. Conversely, an absent tool call in a trace does not establish that no action occurred: tracing may be disabled, unavailable, filtered, or incomplete. Use the system that owns the state to confirm the outcome.
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Test the likely causes against the evidence
Do not assume that every surprising result is a model reasoning failure. Compare the trace and system records against the plausible points where the behavior could have entered or changed:
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- Incomplete or misleading context: The relevant conversation, retrieved material, or state supplied to the model may have been missing, stale, ambiguous, or inconsistent with the operator’s expectations. Compare the actual recorded inputs with the information you expected the agent to receive.
- Tool definition or implementation: The tool’s description, schema, argument handling, validation, or side effects may not match what the agent or operator expected. Compare the request with the implementation and its application logs.
- Permissions: The agent may have had broader access than the task required. Check the permissions that applied at the time, including those of any delegated agent or service account.
- Retry or timeout behavior: A timeout can leave uncertainty about whether the first attempt completed. Review retries and downstream records for duplicate or delayed effects instead of assuming a timeout means nothing happened.
- Handoff or orchestration: A later agent, workflow, or orchestration step may have transformed, repeated, or acted on the initial request. Follow the full parent-child chain and inspect the inputs at each boundary.
- External state change: Another process or person may have altered the relevant data before or after the agent acted. Compare target-system records and application events to determine whether the agent was the actor responsible for the change.
These are diagnostic possibilities, not claims that a given framework exposes a record for every cause. When a link in the chain is missing, state what can be established and what remains unresolved rather than filling the gap with an inference.
Preserve the evidence and reproduce only when it is safe
Retain the relevant session identifiers, trace or export, available tool arguments and results, timestamps, application logs, and downstream-system records under your team’s incident process. Record the environment and software or configuration version so that a later reviewer can distinguish the incident setup from the current one. Restrict access to sensitive logs and follow applicable retention and privacy controls.
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If reproducing the event could clarify the cause, use controlled inputs in a safe environment with permissions and external side effects constrained. Do not replay a production action merely to see whether it happens again. A safe reproduction can test a hypothesis about instructions, context, tool behavior, or orchestration, but it is not a substitute for confirming what happened in the original target system.
Keep a concise incident timeline that connects each observation to its source: trace event, application log, or target-system record. Mark uncertain correlations as uncertain. This makes it easier for another engineer or responder to review the same evidence without mistaking a plausible explanation for a verified fact.
Understand what tracing can—and cannot—show
Trace coverage depends on the framework, runtime, and configuration. OpenAI’s Agents SDK documentation describes controls for enabling or disabling tracing. Its Python documentation also notes sensitive-data settings and says tracing is unavailable for organizations operating under OpenAI APIs with a Zero Data Retention policy. A missing trace or field can therefore reflect configuration or policy rather than the absence of an action. Check the active runtime, tracing and export settings, retention policy, and data-capture settings before drawing conclusions from a gap.
Recorded inputs and outputs may be limited by redaction or sensitive-data settings. That can be an appropriate privacy choice, but it affects what an investigator can later reconstruct. Decide in advance which information is needed for incident response, what must be redacted, who can access it, and how long records should be retained. Do not assume that a trace includes content that the system was configured not to capture.
For teams using cross-framework telemetry, AWS CloudWatch documents OpenTelemetry tracing for multiple agent frameworks and describes traces that can expose model calls, tool calls, and orchestration steps. That is one documented telemetry route, not a requirement to use AWS and not proof that every downstream side effect is captured. Whatever route you use, validate it with a representative workflow and compare its records with the logs of the systems that execute consequential actions.
Turn the incident into better observability and safer behavior
Once the immediate cause is understood—or the evidence gap is clearly identified—improve the path that made the event hard to explain. The aim is not to log everything indiscriminately. It is to preserve the information needed to link a decision to an attempted operation and an outcome, while respecting privacy and retention requirements.
- Log tool execution explicitly. Capture the tool name, relevant arguments, execution status, error or result, timestamps, and a correlation identifier where appropriate. Avoid storing secrets or sensitive payloads unnecessarily.
- Record external effects at the boundary that owns them. Application and downstream-system logs should make it possible to confirm whether a consequential change was committed, rejected, or duplicated.
- Use least privilege. Limit agent and tool permissions to the actions and data required for the task. Where appropriate, require a human confirmation or an additional check before high-impact actions.
- Keep event relationships interpretable. Preserve session, turn, span, parent, and request identifiers when available so responders can follow nested work rather than seeing disconnected events.
- Validate the logging configuration. Confirm which events are recorded, what sensitive-data controls remove, how exports work, and what retention applies. Test the behavior rather than assuming a setting produces a complete record.
- Create a regression case. Turn the incident into a controlled test that checks whether the intended behavior continues after changes to prompts, tools, permissions, or orchestration.
OpenAI’s evaluation guidance recommends inspecting representative workflow traces before formalizing graders. Use that inspection to identify meaningful expected behavior, then evaluate whether the fix continues to meet it. A passing evaluation is useful evidence about tested cases; it does not prove that every future run will behave correctly.
A practical evidence checklist
Before closing an investigation, make sure the record distinguishes the following, where available:
- The affected agent, session or run, turn, environment, time window, and deployment or configuration version.
- The model generation immediately before the event, including recorded inputs and outputs.
- The selected tool, arguments, invocation status, result or error, and any retries.
- Handoffs, nested agents, parent-child relationships, timestamps, and relevant orchestration events.
- Application logs and the target system’s own record of whether the consequential effect occurred.
- The tracing, export, redaction, and retention settings that affect what evidence is available.
- The confirmed cause, remaining uncertainty, containment taken, and changes made to reduce recurrence.
When some evidence is unavailable, say precisely which link could not be verified. For example, the trace may establish that a tool request was recorded while the target-system log establishes whether the requested change was committed. That is more useful than treating a partial trace as a complete account of the incident.
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