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Instrument an AI agent as a trace for one meaningful turn or workflow, then attach child spans for every model generation, tool execution, retrieval step, handoff, and important application action. Each span should carry the operation name, inputs and outputs where policy allows, start and end times, duration, status, and error details. Use your framework’s native tracing when it covers the complete run; use OpenTelemetry when you need to send a consistent span stream to an existing observability backend.
Keep API keys and telemetry credentials on the server, decide which prompt and tool data may be retained, and test both a successful and a failed run. A trace that shows only a provider request—or that lacks the final outcome—is not enough to debug an agent.
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What AI-agent instrumentation should capture
An agent run is a sequence, not a single request. The model may decide to call a search service, invoke a database function, hand work to another agent, apply a guardrail, and then generate a final answer. Instrumentation makes that sequence inspectable by representing it as a parent trace with related child spans.
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Trace, session, and span
- Session: a longer-lived conversation or business process that can contain several turns.
- Trace: one locateable turn or workflow, such as “refund request 8f2…” or “daily inventory reconciliation.”
- Span: a timed operation inside that trace, such as a model generation, a tool call, retrieval, handoff, or application-owned validation.
Give every trace a stable run or session identifier that your application can find in logs and support tickets. Keep separate turns distinguishable even when they belong to one session.
Minimum span inventory
| Span type | What it represents | Useful fields |
|---|---|---|
| Agent or workflow | Entry and exit of an orchestrated run | Workflow name, agent version, session/run ID, start/end time, final status, error |
| Generation | A model request and its response | Model identifier, input messages or prompt, generated output, parameters, usage when available, status and error |
| Tool | A function, API, database, search, or code execution requested by the agent | Tool name, arguments, available result, duration, status, error, timeout or retry information |
| Handoff | Work delegated from one agent to another | Source and destination agent, delegation input, returned result, status and timing |
| Retrieval | Document or data lookup used to ground a generation | Retriever name, query or redacted query, collection, result count, latency and failure |
| Application step | Business logic invisible to the model provider | Operation name, relevant identifiers, outcome, timing and exception |
Do not assume that a provider-level model trace records your application’s tool execution. If your framework does not create a tool span around the actual function boundary, add one there so the trace shows what the application requested and what it received.
Choose a trace boundary before writing instrumentation
Start by deciding what an engineer should be able to locate later. A customer-support turn, a document-processing job, and a multi-agent research task may each deserve a root trace. Avoid making the root so broad that unrelated turns become one unreadable tree, or so narrow that the model decision and its tools are split apart.
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Create a run ID at the boundary where work enters your service. Propagate it through queues, worker processes, tool wrappers, and delegated agents. Keep a session ID separately when several turns belong to the same conversation. Include tenant, user, request, or job identifiers only when your privacy policy permits them; prefer opaque IDs over email addresses or other direct identifiers.
Model nested and asynchronous work
A tool that launches a background job should create a child span for the submission and another linked operation for completion if the telemetry system supports links. A handoff should remain under the parent trace, with the delegated agent represented as its own span or nested trace according to your framework. Preserve timestamps and parent IDs across retries so a repeated call is visible instead of overwriting the first attempt.
Define the event data you will retain
Instrumentation is useful only when each event has enough context to explain an outcome. Establish a schema before collecting production traffic, and distinguish required metadata from optional content that may contain sensitive data.
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Core attributes
- Identity: trace ID, span ID, parent span ID, session or run ID, service name, environment, deployment version, and agent or workflow name.
- Timing: start time, end time, duration, queue wait where relevant, and attempt number.
- Operation: span type, model identifier, tool name, endpoint or operation name, and whether the step was a retry, handoff, or fallback.
- Outcome: success, failure, cancellation, or timeout; a bounded error type and message; and the final workflow result.
- Content: model input and output, tool arguments and result, retrieval query and documents, or a secure reference to those payloads.
- Usage: token or provider usage fields when the SDK supplies them, marked unknown when absent.
Use structured fields rather than one concatenated log line. Structured data lets you filter all failed tool calls, reconstruct ordering, and calculate latency without parsing prompt text.
Payload size and truncation
Large prompts, retrieved documents, screenshots, and tool results can make traces expensive or impossible to export. Set explicit byte or character limits, record that truncation occurred, and retain a secure object reference when full content is needed for an approved investigation. Never silently cut a JSON tool argument in a way that makes it appear valid.
Implement the instrumentation step by step
- Create the root trace. At the request, job, or message-consumer boundary, start a trace with a stable run ID, workflow name, service version, environment, and start timestamp.
- Wrap every model generation. Start a generation span immediately before the provider call. Record the model identifier and permitted input; on completion, record the output, usage fields if supplied, end time, and status. On exception, set an error status and capture a bounded, non-secret error description before re-raising or handling it.
- Wrap the real tool boundary. Start a child span when the agent requests a tool, including the tool name and validated arguments. End it only after the function or remote API returns, times out, or fails. Record the available result or a redacted summary, plus retry and timeout information.
- Add spans for retrieval, handoffs, and guardrails. Any operation that can change the answer, consume significant time, or fail independently deserves a child span. Include source and destination agent names for handoffs and the collection or retriever for searches.
- Close the root with the outcome. Set the final status, end timestamp, duration, and a reference to the user-visible result or business outcome. Mark cancellations and partial completions distinctly from successful responses.
- Propagate context. Pass the trace context through asynchronous queues, HTTP headers, worker metadata, and agent-to-agent calls. Confirm that child spans appear under the intended parent rather than as unrelated traces.
- Export after application initialization is ready. For an OpenTelemetry path, initialize the telemetry SDK and register its span processor and exporter before the code you need to observe runs. Configure batching, shutdown flushing, and a bounded export timeout so a telemetry outage does not hang the agent.
The exact API names differ by language and framework, but the ordering is constant: open a span, attach permitted attributes, record success or failure, and close it in a finally-style cleanup path. Instrumentation should never change the tool’s arguments or the model’s response.
Use native framework tracing when it covers the run
Native tracing is usually the shortest route to useful coverage because the framework already knows when an agent starts, generates text, invokes a tool, hands off work, and applies guardrails. The OpenAI Agents SDK documents built-in tracing that records LLM generations, tool calls, handoffs, guardrails, and custom events. Its tracing reference is available at OpenAI Agents SDK tracing.
Check coverage instead of assuming it
Before relying on defaults, run a workflow that exercises every operation you care about. Verify that the trace includes model inputs and outputs, each tool’s arguments and result, handoff boundaries, exceptions, and the final answer. Provider requests alone do not prove that your application’s database call or business-rule check was captured.
Send native spans to another backend
The OpenAI Agents SDK documents a trace provider and processors, including adding a processor or replacing defaults to deliver spans to a different backend. Use that extension point when the native event model is complete but your team needs a shared telemetry destination. Keep one authoritative parent-child relationship; do not create a second unrelated trace for the same turn merely because you are exporting it.
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Use OpenTelemetry for a shared telemetry path
OpenTelemetry is appropriate when several services or frameworks must feed one collector and backend. Initialize the SDK before application logic, create or propagate context, and register a span processor and exporter. The receiving system may be a vendor backend or an OpenTelemetry-compatible collector.
Semantic consistency matters
Agree on names and attributes across services: use the same span kind for a generation, the same field for a model identifier, and the same status vocabulary for timeout and failure. Amazon Web Services documents GenAI semantic-convention attributes and hierarchical traces across orchestration steps, model calls, tools, and retrieval in its AI observability guidance for Amazon OpenSearch Service. Follow the conventions supported by your collector, while preserving provider-specific details in a namespaced attribute.
Connect an LLM-focused backend
Langfuse documents OpenTelemetry setup and integrations for OpenAI, the Vercel AI SDK, and LangChain in its LLM tracing setup guide. Such integrations can reduce manual generation instrumentation, but you still need application-owned spans around tools, queues, database work, and policy checks. Confirm that the integration records both the model activity and the orchestration events that matter to your team.
Capture prompts, tool arguments, and results safely
Prompt and tool payloads can contain personal information, credentials, customer records, proprietary instructions, or regulated data. Treat them as production data, not harmless debug strings.
Keep credentials server-side
Telemetry exporters and hosted tracing SDKs commonly authenticate with secret keys. Langfuse states that its tracing SDKs are intended for server-side code and that a secret key must never ship to a browser or mobile app; see the Langfuse SDK overview. Store secrets in the server’s secret manager, restrict their scope, rotate them, and prevent them from entering prompts, tool arguments, or error messages.
Choose a payload policy
- Full capture: retain complete inputs and outputs only for approved environments and data classes.
- Selective capture: record content for internal test tenants while storing hashes, classifications, or references in production.
- Redacted capture: remove secrets and direct identifiers before export, while retaining enough structure to debug.
- Metadata-only: keep names, sizes, timing, status, and hashes when content retention is prohibited.
Document who can view traces, how long each class is retained, where data is stored, and how deletion requests are honored. Apply the same policy to failed calls and retries; errors often echo the very payload you meant to protect.
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Redaction must be fail-closed when required
Processor order and failure behavior are security decisions. The OpenAI Agents SDK tracing documentation warns that a separate redaction processor does not necessarily stop another exporter from receiving data if redaction fails. If successful redaction is mandatory, perform redaction and delivery in the same application-owned exporter and discard the batch when redaction fails. Test the failure path deliberately rather than assuming a redaction flag guarantees it.
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Instrumentation is not finished when code compiles. Execute at least one normal workflow and one controlled failure, then inspect the receiving trace backend.
Successful-run checks
- The root trace has the expected run or session identifier and a closed end time.
- Generation spans show the intended model, permitted input and output, status, and duration.
- Every tool request appears with validated arguments, result or an explicit omission, and a parent span.
- Retrieval, handoffs, guardrails, and application checks appear in the order they ran.
- The final span identifies the user-visible or business outcome.
Failure-run checks
- A tool timeout, provider error, validation rejection, and cancellation are distinguishable.
- The failed span contains a bounded error type and message without secrets.
- Retries have separate attempt information and do not overwrite the original failure.
- The root trace closes with failure or partial completion, even when an exception short-circuits normal control flow.
- Export failure is visible through local health metrics or logs and does not silently turn a missing trace into a successful run.
OpenAI’s observability documentation notes that exported traces use OTLP JSON and that recorded usage may be unknown or change after the turn. Inspect the backend rather than assuming export succeeded, and treat an absent usage value as unknown—not as zero. See OpenAI’s observability and usage documentation.
Compare implementation paths
| Decision axis | Native framework tracing | OpenTelemetry instrumentation |
|---|---|---|
| Coverage | Strong for events the framework understands, such as generations, tools, handoffs, and guardrails; application-only work may need manual spans. | Can cover orchestration, model calls, tools, retrieval, queues, and business logic when you create or propagate the spans. |
| Interoperability | Depends on the framework’s processors and exporters. | Designed for a shared collector and multiple compatible backends. |
| Setup effort | Usually quickest; enable tracing and verify defaults. | Requires early SDK initialization, context propagation, processor, exporter, and shutdown handling. |
| Data controls | Use the framework’s processors and your own filtering; verify redaction failure behavior. | Centralize filtering in an exporter or collector, while checking that no other exporter receives unredacted data. |
| Operational fit | Best when one agent framework dominates and its trace view answers your debugging questions. | Best when many runtimes and services must be correlated in an existing observability system. |
| Cost visibility | May expose provider usage when available, but fields can be null or updated later. | Can normalize usage across services, but cannot invent values a provider did not return. |
These paths are not mutually exclusive. A native SDK can produce the detailed agent events while an OpenTelemetry processor exports them alongside database and HTTP spans. Avoid double-counting by assigning one trace identity and documenting which component owns each span.
Make outcomes useful for operations
Record business outcomes, not only technical success
An HTTP 200 response from a tool does not prove that the agent completed its task. Add an application-owned outcome such as “invoice created,” “answer returned with citations,” “human review required,” or “policy blocked.” Keep that field separate from transport status so operators can distinguish a successful API call from an unsuccessful business operation.
Measure latency by segment
Because each span has start and end times, you can identify time spent waiting for a model, a tool, a queue, retrieval, or a handoff. Record retries and queue delay explicitly; otherwise a single duration hides the cause of a slow run. Do not publish performance benchmarks unless you have measured them under stated conditions.
Use usage fields cautiously
Token or cost-related usage is useful only when it covers the relevant model calls and subagents. Provider documentation may return null, omit a field, or revise usage after a turn. Keep the raw value and a separate “known” indicator, and never convert missing usage to zero in dashboards or billing logic.
Troubleshoot common gaps
The trace contains the model call but no tool result
Instrument the function or HTTP client that actually executes the tool. Check that the tool wrapper receives the current trace context and that exceptions close the span. A provider’s declaration of available tools is not evidence that the execution was recorded.
Spans appear as separate traces
Inspect context propagation across threads, async tasks, queues, and service boundaries. Ensure workers extract the parent context and start child spans instead of creating a new root for every message. For intentionally asynchronous work, use a documented span link when a strict parent cannot remain open.
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Confirm that the telemetry SDK initialized before the first agent operation, the exporter endpoint and credentials are valid, and the process flushes on shutdown. Check exporter health logs and the backend directly. Batch processors can delay visibility; use a short flush or a synchronous exporter temporarily while diagnosing.
Redaction removed too much—or too little
Test representative prompts, nested JSON arguments, error messages, and streamed output. Keep a count or reason for each redaction so an engineer can tell whether a missing value was intentionally removed. If redaction fails and policy requires fail-closed behavior, drop the batch rather than sending an unredacted copy through another processor.
Usage is missing
Check the provider response and SDK version before changing your schema. Represent unavailable usage as unknown, and do not infer token counts from character length. If usage can change after completion, update the trace through the supported mechanism and retain the update timestamp.
Quick Recap
Production checklist
- One root trace represents a locateable turn or workflow.
- Session and run identifiers are propagated without exposing direct personal identifiers unnecessarily.
- Generation, tool, retrieval, handoff, guardrail, and important application spans have parent-child relationships.
- Each span records timing, status, and bounded error information.
- Prompts, outputs, arguments, and results follow a documented retention and redaction policy.
- Exporter credentials remain on trusted server-side components.
- Redaction and exporter failure behavior has been tested, including fail-closed requirements.
- Successful, failed, timed-out, retried, cancelled, and partial workflows have been inspected in the backend.
- Usage fields are labeled unknown when the provider does not supply a value.
- Telemetry outages cannot silently change the agent’s business outcome or expose secrets.
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