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What to capture for each tool call
A tool-call record should let you answer four questions: what ran, what data it received, what happened, and how it ended. Use a structured span or event rather than a single free-form log line.
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- Identity and context: tool name and server or provider, workflow or agent name, and the parent span. Include a genuine session or conversation ID if the application already has one.
- Timing and outcome: start time and end time or duration, status, and a concise error type or detail when something fails.
- Call and result: structured arguments and returned output when your content policy allows them. Keep them distinct from the model’s prompt and response: tool arguments and results explain the action, while model content explains the reasoning context around it.
- Execution state: retries, approval state, or other state changes that help explain whether and how an action was carried out.
- Trace correlation: trace ID, span ID, and parent span ID so events can be placed in workflow order and related to model-generation spans.
This is a practical schema, not a universal standard. OpenAI’s tracing guide describes tool spans with tool details, arguments, results when available, timing, status, and errors; MCP call details can include the server label, tool name, arguments, output, and error. OpenAI’s tracing guide shows how those spans fit alongside agent and generation spans.
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A trace represents a workflow or agent turn; spans represent its individual operations. Put model generations and tool executions under the agent or subagent that performed them. This structure preserves causality without pretending that every step runs sequentially: parallel child work may overlap, so a timeline and parent-child relationships help reveal both ordering and elapsed time.
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Use the conversation or session identifier your application already assigns when one exists. Do not manufacture a conversation ID from a trace ID, a new UUID, or a hash of request content just to fill a field; OpenTelemetry’s agent conventions caution against that. Trace identifiers describe the telemetry trace, not necessarily the user’s continuing conversation. See the OpenTelemetry GenAI agent span conventions.
Choose what happens to arguments and outputs
Raw tool arguments and results can contain personal information, credentials, private business data, or other sensitive content. Model prompts and completions can be sensitive too, but they are different records with different debugging purposes. Set a capture policy for each before turning on production logging.
Omit content
Keep operational fields such as tool identity, timing, status, and error class while leaving prompts, arguments, and results out of telemetry. OpenTelemetry’s GenAI span conventions state: “Default: Don’t record instructions, inputs, or outputs.” This is a useful starting point when full content is not needed for diagnosis.
Record content as span attributes
Where volume and privacy conditions permit, structured message or tool content can be stored on spans. This makes inspection convenient, but content attributes can grow large, include media, and exceed telemetry backend limits. Apply minimization, redaction, truncation, and backend-size controls as appropriate; truncation can make a record easier to export but may remove details needed to reproduce a failure. Consult the OpenTelemetry GenAI span conventions for the documented patterns.
Store content separately and link to it
For high-volume telemetry or sensitive payloads, keep the content in storage with its own access controls and retention policy, then put a reference on the span. This separates broad observability access from access to raw user data. OpenTelemetry documents this option and recommends external storage for production when volume or sensitive-data handling is a concern.
OpenAI tracing options and their trade-offs
Inspect or export Agent API traces
If you use the OpenAI Agents API, its tracing interface presents agent, generation, and tool spans, including available call details, status, and error information. Trace export returns OTLP JSON. Export requires organization-level trace export to be enabled and an API key with trace-read or broader agent-read permission. See OpenAI API tracing for the current interface and export requirements.
Configure tracing in the Python Agents SDK
The OpenAI Agents SDK for Python documents trace_include_sensitive_data as true by default. Setting it to false omits Responses model request and response content; the documented official-endpoint case still retains the response ID as correlation metadata. Confirm the SDK version and configuration you deploy rather than assuming these behaviors apply to every version or endpoint.
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Processor configuration also affects where data goes. Adding a custom processor leaves the default exporter registered; replacing processors does not send data to OpenAI unless an appropriate exporter is included. A redaction processor is not a guaranteed gate: processors are independent observers, and an exception in one callback does not stop other registered processors. If the default exporter remains installed, it may still receive data even when redaction fails. Validate the configured processors and exporters, including the behavior when redaction errors, using the Python SDK tracing documentation.
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Use OpenTelemetry conventions across frameworks
OpenTelemetry provides vendor-neutral GenAI span conventions. When evaluating an implementation, check framework compatibility, control over payload capture, attribute-size limits, external-content storage options, and whether your team can operate the collector and export pipeline. Conventions can evolve, so check the current specification when implementing them. If sending large content, account for filtering or truncation and tune batch and export settings accordingly.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make logs useful without calling them a complete audit trail
Trace spans are useful for debugging latency, failed calls, and the relationship between model and tool steps. They are not automatically a complete audit log. If you need an audit trail, instrument the relevant actions, inputs and outputs allowed by policy, internal state changes, errors, timestamps or durations, and contextual identifiers; retain those records under controls appropriate to the security use case. Singapore’s Securing Agentic AI addendum recommends monitoring tool activity and considering privacy requirements for logged inputs.
These field and storage choices are implementation guidance, not a universal legal-compliance recipe. Set access controls and retention based on the data’s sensitivity, applicable rules, and operational need; the cited guidance does not establish one retention period for every agent.
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