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Instrument an AI agent by treating one user-visible run as a trace, adding spans for orchestration, model calls, tools, retrieval, and handoffs, then exporting the telemetry to a backend you have verified. Use logs for individual diagnostic events and metrics for aggregate behavior. OpenTelemetry provides a portable foundation; the most important production decisions are what to capture, how to redact it, and whether the exported data is complete and safe.
What logs, traces, and metrics show
These signals answer different operational questions. OpenTelemetry describes their roles in its Signals documentation.
- Traces connect the work done for one request. A trace can show that orchestration called a model, which then led to a tool call or retrieval step, and where an error or delay occurred.
- Logs record discrete events with context, such as a tool timeout or a validation failure. They help explain a specific occurrence.
- Metrics aggregate behavior over time, such as request counts, error rates, or latency distributions. They help reveal trends and alert on operational conditions.
For agents, a useful trace is more than a record of model requests: it connects the application-level workflow to its model, tool, and retrieval work.
Design the trace around an agent run
Start with the boundary of one user-visible operation or workflow. Give significant stages their own spans and preserve their parent-child relationships, start and end times, duration, status, and relevant error context. OpenAI’s Agents API describes traces as grouping model responses, tool calls, and delegated-agent work, with each recorded step represented as a span. Its tracing guide also describes inspecting recorded inputs, outputs, duration, and status in the dashboard.
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Common span boundaries include:
- Agent orchestration and application-specific decisions
- Model generation
- Tool execution
- Retrieval
- Handoffs to another agent or component
For useful filtering and diagnosis, capture stable operational attributes where available: operation name, provider or system, requested model, and input/output token usage. The AWS OpenSearch example documents these alongside trace and span IDs, parent span ID, timing, duration, status, and GenAI attributes in its AI observability guide. Exact field names depend on the instrumentation and semantic-convention version you use.
Choose instrumentation and route it to a backend
Framework instrumentation can create spans for common operations, but it does not automatically guarantee that telemetry reaches your chosen destination. You still need an SDK or provider configuration and an exporter or equivalent delivery path. OpenTelemetry’s GenAI semantic conventions establish shared AI-related attribute conventions; the registry is versioned, so check the current version and the support provided by your SDK.
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- Map the workflow. Identify the start and end of an agent run and the orchestration, model, tool, retrieval, and handoff stages whose behavior you need to see.
- Use framework instrumentation where it fits. Prefer built-in spans for operations the framework can observe, then add manual spans around custom orchestration and business logic that it cannot see.
- Configure the telemetry pipeline. Set up the SDK/provider and exporter, select a backend, and verify that the instrumentation source names match the sources configured in the provider.
- Inspect a real trace. Check hierarchy and parent-child links, durations, status, error placement, and token usage when available. Confirm that the recorded operations match the workflow you intended to observe.
- Verify delivery behavior. Review batching, flushing, shutdown, permissions, retention, and sampling in the documentation for the specific SDK, exporter, and backend before relying on the data during an incident.
Microsoft’s Agent Framework documentation demonstrates framework observability with OpenTelemetry traces, logs, and metrics, including exporter examples. AWS documents a Python pattern that configures an OTLP exporter and adds a manual agent span. These are implementation examples, not evidence of a neutral performance or cost comparison.
Choose a practical instrumentation path
| Approach | When it fits | What to verify |
|---|---|---|
| Framework-native tracing | Your framework already instruments the model, tool, or handoff operations you need. | Coverage of custom steps, privacy defaults, exporter configuration, and whether the spans duplicate lower-level client instrumentation. |
| OpenTelemetry with manual spans | Your agent is custom, or you need control over application-level spans and routing. | Provider and exporter setup, source-name configuration, consistent attributes, and delivery lifecycle. |
| Backend-managed trace exploration | You want to inspect agent traces in a hosted or cloud backend your team already uses or prefers. | Trace structure, access controls, retention, redaction, export options, metric aggregation, and operational cost. |
OpenAI’s Agents SDK documents built-in tracing for generations, tool calls, handoffs, guardrails, and custom events. Microsoft documents Azure Monitor export, while AWS documents OpenSearch’s Agent Traces interface, including hierarchical trace views, span details, flow visualizations, and aggregate metrics. For OpenAI Agents API traces, the dashboard supports inspection and a session trace endpoint returns paginated OTLP JSON. Export requires organization-level trace export and a project API key with trace-read or broader agent-read permission. The endpoint includes traces available when each page is requested; it does not set up ongoing delivery. The SDK documentation explains how trace processors can be added or replaced to send data elsewhere.
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Protect prompts, tool data, and other sensitive content
Agent telemetry can contain prompts, completions, tool arguments and results, or audio. In the documented Python OpenAI Agents SDK, sensitive-data capture is enabled by default, and controls are available to omit generation inputs and outputs or function-call inputs and outputs. Microsoft likewise cautions that prompts, responses, function arguments, and results can be sensitive. Review the relevant Agents SDK tracing documentation and Microsoft observability guidance before enabling capture.
- Decide which content is necessary for diagnosis; do not treat full payload capture as a default requirement.
- Redact sensitive values before export and validate the full delivery path, not just one processor in isolation.
- Restrict access and set retention deliberately in the backend.
- Test failure behavior: a redaction error must not allow an unredacted copy to reach another exporter.
The OpenAI SDK documentation notes that processors are independent observers. If redaction fails in one processor, a separately registered exporter may still receive the original data. When delivery must depend on successful redaction, the documentation recommends combining redaction and delivery in an application-owned exporter and discarding the batch if redaction fails.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Avoid misleading traces and duplicate instrumentation
Instrumenting both an agent and its chat or model client can create duplicate spans; Microsoft notes that captured context can appear in both layers. Choose intentional span boundaries and inspect a representative trace before using it for debugging or reporting.
Do not read missing token usage as zero. OpenAI notes that usage may arrive after a turn ends, so a blank or null count means unknown. Also account for concurrency: a parent span’s duration includes its child work, and parallel child durations should not be added together as if they were elapsed wall-clock time.
Validate observability before relying on it
Run a representative workflow that includes the operations your application actually uses. In the backend, verify the trace’s hierarchy, operation outcomes, durations, available usage data, and error context. Then exercise the configured export and shutdown path and confirm that permissions, retention, redaction, and sampling behave as intended. A trace that exists in a framework but never reaches the destination—or reaches it with sensitive payloads you did not intend to capture—is not a production-ready observability setup.
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