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An AI agent trace should show the run’s structure and causal sequence: which workflow ran, which model calls and tools it used, what retrieval or handoffs occurred, and where errors or delays happened. Record identifiers, timing, status, and usage metadata by default; capture prompt, tool-argument, and output content only when it is necessary and protected by explicit privacy controls.
What an AI agent trace should explain
A useful trace lets you follow a run from its workflow or agent invocation through individual model and tool operations. Correlated parent and child spans make orchestration visible, including handoffs and retrieval steps, rather than presenting a run as a disconnected list of log entries. OpenTelemetry’s agent span conventions describe this workflow-level view.
Build around a stable trace ID and meaningful workflow name, with environment, start and end times, outcome, and links to parent or child work. Use real identifiers, and keep operation names low-cardinality so that names remain useful for grouping and querying. If a conversation ID already exists, use it to correlate the conversation; do not invent a UUID, trace ID, or content hash and label it as the conversation identifier.
Recommended fields by trace event
| Event or span | Record | Capture only when needed |
|---|---|---|
| Trace or run | Stable trace ID; workflow name; environment; start and end time; outcome; parent and child links. | — |
| Agent or workflow | Agent or workflow identity, operation, parent-child relationship, and handoffs. | Implementation-specific details that do not help explain the user-facing workflow. |
| Model inference | Provider or system, model identifier when available, operation, timing, status or error, and usage data when available. | Ordered system instructions, input messages, and model output. |
| Tool execution | Tool name and type, call ID when available, timing, status, and error details. | Structured arguments and results, which may contain sensitive data. |
| Retrieval or context | Retrieval event, query and document identifiers or references, and relevance scores when available. | Full query or document contents, especially large or sensitive material. |
| Policy or evaluation | Concise guardrail or evaluation outcome when needed to debug policy behavior. | Detailed explanatory payloads that may reveal sensitive inputs or reasoning context. |
How to handle prompts, tool calls, and context
Prompts and model outputs
Model-call metadata can identify what happened without retaining message content. Include ordered system instructions and input/output messages only when content-level debugging or evaluation requires them. If content is captured, make that an explicit configuration choice rather than assuming it belongs in routine telemetry. OpenTelemetry’s GenAI span conventions say: “OpenTelemetry instrumentations SHOULD NOT capture them by default, but SHOULD provide an option for users to opt in.” Here, “them” refers to model instructions, user messages, and model outputs.
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Tool calls
Make each tool execution inspectable and link it to the relevant model or workflow span. Record the tool’s identity, call ID if supplied by the framework, status, timing, and useful error information. Do not create duplicate spans for a single tool call. Arguments and results are content, not innocuous metadata: enable their capture only when the diagnostic need justifies the exposure.
Retrieval and other context
Represent context in the order and form needed to explain the run. For retrieval, document IDs or controlled references and relevance scores can show what material was available without copying entire documents into telemetry. Include conversation correlation using an existing conversation identifier where available; it is distinct from the trace ID, which identifies the observed run.
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Guardrails and evaluations
When policy outcomes matter to debugging, capture concise results such as whether a guardrail or evaluation passed, failed, or triggered an intervention. Keep sensitive explanatory payloads separate or omit them unless they are necessary. The OpenAI Agents SDK tracing documentation describes trace events for generations, tool calls, handoffs, guardrails, and custom events; those are SDK capabilities, not a universal event set for every agent framework.
Choose a content-retention pattern
OpenTelemetry describes three approaches: omit instructions, inputs, and outputs by default; opt in to structured content on spans; or store content externally and put controlled references in telemetry. Its guidance describes external storage as a production option when payload size or sensitive-data handling is a concern, because access to content can be governed separately from access to trace metadata.
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Google Cloud similarly recommends Cloud Storage for prompts and responses rather than putting them in a log entry. Its documentation describes tracing as useful for troubleshooting failed API requests, loops, and latency, validating communication flows, and evaluating output quality and cost; these are documented product use cases, not independent performance findings. See Google Cloud’s observability guidance for AI agent developers.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Set privacy and operational controls
- Keep secrets out: Do not put credentials, tokens, or other secrets in prompts or tool arguments. Microsoft Foundry gives this guidance in its tracing configuration documentation.
- Restrict access: Decide who can inspect trace metadata and who can retrieve separately stored content. Access requirements depend on the service and deployment.
- Set capture deliberately: Use metadata-only tracing by default where content is not needed; enable content capture for a defined debugging or evaluation purpose.
- Match sampling and retention to the environment: Choose settings based on diagnostic need, cost, and data-handling requirements. Retention and pricing vary with the configured service.
Provider defaults are not interchangeable. For example, the OpenAI Agents SDK documentation says sensitive-data capture is enabled by default and can be disabled. Microsoft Foundry advises enabling content capture for development and debugging and disabling it in production. Check the current settings for the framework and service you actually deploy.
Quick Recap
Choose a trace design by the questions it can answer
- Diagnostic detail: Will metadata alone show where a run failed, or do you need controlled access to prompts, tool results, or retrieved context?
- Privacy exposure: What content enters telemetry, who can see it, and can content be stored separately with stricter access controls?
- Correlation: Can you connect the workflow, model call, tool execution, retrieval, and conversation using their actual identifiers and parent-child links?
- Cost and scale: How do payload size, sampling, retention, and queryability affect the design?
- Portability: Does the schema align with OpenTelemetry GenAI conventions, or does it depend on provider-specific fields? OpenTelemetry’s GenAI conventions are actively maintained, and the project notes that they have moved to a separate repository; confirm current attribute names and stability before implementing them.
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