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An agent’s tool call can leave more than a request and a result. Depending on the platform and its configuration, a trace may also record the model response that triggered the call, handoffs between agents, guardrail checks, timing, status, and workflow context. It is an implementation-specific record—not a guarantee that every input or output was captured.
What can appear in a tool-call trace?
A trace is a record of activity in an agent workflow. In OpenAI’s Agents API, a session groups related conversation or work, while a trace shows the steps in a turn. The tracing dashboard can show recorded inputs and outputs, duration, status, and tool requests and results when those details are available. OpenAI’s tracing guide describes the dashboard and the distinction between sessions and traces.
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A tool call may therefore sit among several connected events rather than appear as an isolated log entry. The OpenAI Agents SDK’s structured records can include model calls, tool calls, handoffs, guardrails, and custom spans; a default trace can include workflow activity, model calls, tool calls, and outputs. What is captured depends on the implementation. The SDK observability guide describes these record types.
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- Initiating model response: the response that issued or selected the tool call, if recorded.
- Tool request and result: the arguments sent to the tool and the returned data, when available.
- Workflow events: handoffs to another agent, guardrail activity, and custom spans, where configured.
- Operational details: status and duration, if exposed by the trace interface.
These are possible trace contents, not a universal checklist of fields every platform captures. A missing field in a dashboard does not establish that no record exists elsewhere; the application, tool server, or service may keep separate records.
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Where can you inspect or export the records?
Inspection and export depend on the product surface. OpenAI’s Agents API observability guide describes session logs, saved history, usage review, and options to inspect or export recorded traces, including model responses, tool calls, and subagent activity. See the Agents API observability guide for the documented controls.
When reviewing a trace, follow the event sequence around the tool call: what preceded it, what request was recorded, whether a result appears, and what happened next. Compare the trace view with the application’s own logging and the tool’s records; a trace is only one view of the workflow.
Does a trace tell you how long data is retained?
No. “Retention” can refer to different records with different controls: abuse-monitoring logs, session state or saved history, traces, and logs kept by a tool server are not one data category. Check the policy and deletion controls for each relevant surface rather than assuming one retention period applies to all of them.
OpenAI’s data-controls documentation says abuse-monitoring logs are retained for up to 30 days by default, subject to legal requirements. That figure describes that log category and policy, not a universal lifetime for traces, session state, or tool-server records. The Agents API FAQ separately describes retained session state and deletion controls.
Retention terms are product- and deployment-specific. For another example of why the distinction matters, Anthropic’s API retention documentation distinguishes API arrangements and identifies cases where a cloud provider acts as processor. Do not transfer one provider’s terms or one product surface’s controls to another.
What changes when a tool uses a remote server?
A remote MCP server is a separate service boundary: data sent to it is subject to that server’s own retention and data-residency policies. OpenAI’s MCP guidance recommends reviewing what is shared with remote servers and notes that third-party servers have their own policies.
Before enabling a remote tool, identify which parts of the conversation or task are passed to it, what the server returns, and which party retains each record. Log the data shared where appropriate, and review the server’s terms rather than treating the agent platform’s trace settings as a complete account of data handling.
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Zero Data Retention (ZDR) availability and tracing behavior are specific to the product surface. The OpenAI Agents SDK tracing documentation says tracing is unavailable for organizations using OpenAI APIs under ZDR. Separately, the Agents API FAQ says the Agents API does not support ZDR. These statements concern distinct surfaces; neither should be generalized to every OpenAI product or to other providers. Check the current documentation and your organization’s applicable configuration before relying on tracing or a ZDR setting.
Quick Recap
A practical review checklist
- Identify the surface. Establish whether the workflow uses an API, SDK, dashboard, or remote tool, since available records and controls differ.
- Inspect the event sequence. Look for the initiating model response, request, result, handoffs, guardrails, status, and duration where the interface records them.
- Locate each record owner. Separate platform traces and session state from application logs and remote tool-server records.
- Check retention and deletion per category. Read the relevant policy for each service and record type; do not infer one from another.
- Confirm data sharing and ZDR behavior. Review what reaches third parties and whether tracing is available under the particular product’s data controls.
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