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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Instrument an AI agent as a traceable workflow, then add structured logs and metrics for the operational questions a trace alone cannot answer. Start with a trace for each coherent run, preserve parent-child relationships across model calls and tools, correlate logs with that run, and define a small set of service metrics. You can use framework-native tracing such as the OpenAI Agents SDK or instrument with OpenTelemetry and send telemetry to a compatible destination. Decide what data may be captured and where it may be stored before enabling export.
What to instrument in an agent run
A useful agent trace shows how one request or job moved through the system, not merely that a model call occurred. Start a trace at the boundary of a coherent task and add child spans for work whose timing, outcome, or inputs help explain the run.
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- Model generations: record the generation as a span so latency, errors, and permitted usage details can be associated with the run.
- Tool calls: capture the start, end, outcome, and useful non-sensitive context for each tool execution.
- Handoffs and orchestration: represent transfers between agents or meaningful orchestration stages where they affect the execution path.
- Retrieval and external operations: add spans for application work that can explain delays, failures, or unexpected results.
- Custom decision boundaries: instrument important application-specific steps when they clarify why a run took a particular path.
Keep parent-child relationships intact. A flat series of events makes it harder to tell which tool call belonged to which generation or stage. The OpenAI Agents SDK documents built-in tracing for LLM generations, tool calls, handoffs, guardrails, and custom events, and nests spans under the current span. For OpenTelemetry-based setups, combine framework instrumentation with manual spans for important application work that automatic instrumentation does not cover. See the OpenAI Agents SDK tracing documentation, Google Cloud’s AI agent observability overview, and AWS guidance for sending AI agent telemetry.
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These signals answer different questions. A trace explains the sequence of work within a particular run. A structured log records a discrete event or state change. A metric summarizes behavior over time so you can detect a trend or trigger an alert. Adding tracing does not automatically give you a complete logging and metrics strategy.
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Use structured logs for events
Emit logs for events that operators need to find or investigate, such as a failed tool call, a retry, or a significant state transition. Include a trace or run identifier where appropriate so someone investigating an aggregate alert can locate the relevant execution. Keep log fields deliberate: avoid placing prompts, tool arguments, or other sensitive payloads in logs by default.
Choose metrics to answer operational questions
Define metrics for the service questions that matter to your deployment. Useful starting points include run volume, completion and failure rates, latency, retry rate, and resource or token usage where the chosen instrumentation exposes it. Select names and dimensions supported by your SDK and backend; the official guidance cited here does not define one universal agent-metrics schema.
Avoid labels that contain per-run identifiers or other high-cardinality values, and do not use sensitive data as metric dimensions. Metrics are for aggregate behavior; use correlated traces and logs to investigate an individual run.
Choose framework-native tracing or OpenTelemetry
The instrumentation route and the telemetry destination are separate decisions. A framework may provide useful spans while allowing you to configure where they are processed or exported. Alternatively, OpenTelemetry instrumentation can provide a portable path to a supported backend, with framework-specific coverage and manual instrumentation varying by stack.
| Route | What it provides | What to verify |
|---|---|---|
| OpenAI Agents SDK tracing | Built-in tracing of documented agent-run events, with configurable trace processors. | Current behavior for your SDK release, whether default processors remain active, and which payloads are captured. |
| OpenTelemetry instrumentation | A standard instrumentation and export approach documented for agent stacks by Google Cloud and AWS. | Supported framework and language coverage, automatic versus manual spans, deployment prerequisites, and destination configuration. |
The OpenAI Agents SDK documents built-in tracing and configurable processors; replacing the default processors changes whether the default OpenAI exporter remains active. Review the processor configuration rather than assuming that adding a custom processor leaves the existing export path unchanged. For OpenTelemetry, Google Cloud describes agent instrumentation guidance and samples, while AWS documents a CloudWatch route for supported framework and language combinations. Support in one vendor’s documentation does not establish support for every other destination. Confirm the exact framework, language, versions, compute environment, and policy requirements in the documentation for the route you choose.
Inspect or export OpenAI agent traces
Agents SDK traces
For SDK tracing, use the configured trace processors to determine how trace data is handled. If you add or replace processors, check explicitly whether data still goes to the default OpenAI exporter or instead follows your custom processing path. The SDK documentation also describes controls for excluding sensitive data; configure these before using traces with real user or business data.
OpenAI Agents API traces
For the OpenAI Agents API, the documented dashboard workflow is Logs → Agents, where a completed session can be inspected through its turns and steps. The API guide describes exporting a session’s traces as OTLP JSON using /v1/agents/sessions/{session_id}/traces. Export must be enabled for the organization, and the caller needs appropriate project API-key permissions. The documented export is paginated; a one-time export is not evidence of automatic future delivery. Details are in the OpenAI Agents API tracing guide.
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The API guide also describes usage information that may arrive after a turn, may be null when unknown, and may change. Treat it as recorded usage information rather than assuming it is a final bill. The page’s example session reports 252,468 tokens; that is an illustrative recorded session, not a typical-run figure or benchmark.
Compare destinations before routing telemetry
Choose a destination based on the whole operating path, not just whether it can receive spans. Compare the following before committing:
- Stack coverage: confirm that the exact framework, language, and versions used by your agent are supported.
- Instrumentation effort: establish what is collected automatically and which tools, retrieval steps, or application-specific boundaries need manual spans.
- Export behavior: distinguish continuous delivery from a manual or one-time export workflow, and confirm the format and permissions involved.
- Correlation: check whether operators can move from aggregate metrics or logs to the trace for a particular run.
- Data governance: review access controls, retention and deletion behavior, payload handling, and size constraints.
- Operational responsibility: consider hosting, runtime overhead, configuration complexity, and which responsibilities are handled by the managed platform.
Google Cloud recommends OpenTelemetry for agent observability and provides guidance that includes LangGraph and ADK samples. AWS documents a CloudWatch path for Python and Node.js with LangGraph, LangChain, Strands Agents, CrewAI, OpenAI Agents, LlamaIndex, and Vercel AI SDK. These are vendor-documented paths, not a guarantee that every listed stack behaves identically or is supported in every compute environment. Check the Google Cloud overview and AWS instructions against your deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Control payload capture and protect sensitive data
Before rollout, decide separately whether prompts, completions, tool inputs, tool outputs, and audio payloads should enter telemetry. Traces can be valuable for debugging, but their contents may be more sensitive than ordinary operational metadata.
The OpenAI Agents Python SDK documentation says generation and function spans can store inputs and outputs, and that trace_include_sensitive_data defaults to true. Review that setting and the available controls for disabling capture before production use. If redaction must happen before delivery, the documentation warns that adding a redaction processor alongside the default exporter does not guarantee that the exporter receives only redacted data. Its described approach is to replace processors and put redaction and delivery in an application-owned exporter. See the SDK tracing documentation.
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Use allowlists for metadata, minimize identifiers, restrict access to telemetry, and ensure export failures do not print payloads into application logs. If debugging requires raw content, keep that choice explicit and governed rather than allowing incidental capture.
Keep large prompts and responses out of log entries
Google Cloud recommends storing prompts and responses in Cloud Storage instead of log entries. Its documentation states that Cloud Logging log entries have a maximum size of 256 KiB; an oversized entry can be rejected, and some over-limit fields may be truncated. It also notes that individual log entries cannot be deleted. The guidance favors Cloud Storage for finer control, including deletion of an individual stored conversation. These storage and deletion characteristics make it important to decide where payloads belong before emitting them. See Google Cloud’s agent observability guidance.
Roll out instrumentation safely
- Define the run boundary: choose the request, job, or task that begins a trace, and identify the non-sensitive identifiers and workflow metadata needed to correlate its signals.
- Instrument the execution path: enable supported framework tracing, then add spans for important tools, retrieval, external calls, and custom steps not represented automatically.
- Decide signal ownership: specify which events go to traces, which state changes and errors become structured logs, and which aggregate questions become metrics.
- Set capture and access rules: decide which payload fields are excluded, how redaction works before export, who may view telemetry, and how long it is retained.
- Verify export and failure behavior: check processors, permissions, destination configuration, pagination or ongoing delivery, and what happens when an export or oversized record fails.
- Test an end-to-end investigation: confirm that an operator can start from an aggregate signal, find a correlated run, and understand its spans without relying on unavailable or unnecessarily sensitive payloads.
Instrumentation details and platform behavior can change with SDK releases, destination configuration, and deployment environment. Verify them for the versions, regions, and policies in use.
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