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Trace an AI agent as one distributed request, not as a collection of unrelated logs. Create a root span for the user-visible workflow, nest spans for each agent turn, model generation, tool execution, retrieval or memory operation, handoff, and downstream HTTP, RPC, or database call, then propagate W3C Trace Context through every boundary. That hierarchy lets you identify the exact model call or tool that failed, follow a request across microservices, and attribute latency, tokens, and errors to the right operation.
Use OpenTelemetry GenAI semantic conventions when you need portability across languages, model providers, and observability backends. If your agent SDK already emits useful spans, keep that instrumentation and add manual spans where orchestration or tools are not covered. Treat prompts, outputs, and tool payloads as sensitive data: capture them only with explicit controls, redaction, access restrictions, and a retention policy.
How should an AI-agent trace be structured?
The trace should mirror causality: a single workflow at the top, agent and turn operations beneath it, and model, tool, retrieval, memory, handoff, and service calls below those operations. A trace is useful when a reader can start with the user request and walk down to the exact operation that produced an error or delay.
OpenAI’s tracing documentation describes the purpose well: “A trace shows the steps within one turn: model responses, tool calls, and work delegated to other agents.” The dashboard records each step’s inputs, outputs, duration, and status when those fields are enabled.
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Use one root span for the user-visible run
Create a root span for the complete request or workflow. Give it a stable operation name such as customer_support.run, a correlation ID, and attributes that identify the tenant, channel, environment, and workflow version without putting personal data into the name. Keep this span open until the final response, including asynchronous work that is part of the same user-visible operation.
Nest agent and turn spans
Create an invoke_agent or invoke_workflow span for each orchestrator or agent. Under it, create a turn span for each reasoning-and-action cycle. In a multi-agent design, an in-process handoff can be an internal child span; a remote agent call should have a client span at the caller and a server span at the receiving service.
Put operations that change the outcome below the turn
Model generations, tool calls, retrieval, memory reads and writes, planning, and handoffs should be children of the turn that caused them. A downstream HTTP, RPC, queue, or database operation belongs beneath the tool or service operation that issued it. This arrangement distinguishes “the model chose the wrong tool” from “the tool called the right service and that service failed.”
The Tool Desk
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|---|---|---|---|
| Workflow | invoke_workflow |
One user-visible run | Workflow name and version, correlation ID, tenant, final status |
| Agent or turn | invoke_agent, agent.turn |
Which agent made a decision and when | Agent name, turn index, parent agent, handoff target |
| Model generation | chat or provider-specific inference operation |
Model latency, completion state, and usage | Provider, requested model, response ID, finish status, token counts |
| Tool execution | execute_tool |
Arguments, result, and tool failure | gen_ai.tool.name, tool type, call ID, agent name |
| Retrieval or memory | Retrieval or memory operation | Context lookup and persistence work | Store type, query metadata, hit count, latency |
| Remote service | HTTP, RPC, messaging, or database client/server span | Work performed outside the agent process | Protocol, route, status code, peer service, server address |
OpenTelemetry’s GenAI guidance says, “GenAI spans represent logical operations as observed by the caller.” The caller’s view matters: a model provider may have its own internal trace, but your application still needs a span that records when your code requested the operation and how that operation affected the workflow.
How do I propagate trace context through tools and microservices?
Inject W3C Trace Context at every outbound boundary and extract it at every inbound boundary. The receiving component must continue the caller’s trace rather than start an unrelated trace, while still creating its own server or consumer span for local timing and errors.
HTTP and RPC calls
Before a tool sends an HTTP or RPC request, create a client span and inject the current context into the request headers or metadata. The receiving service extracts that context, starts a server span as its child, and creates child spans for its database or downstream calls. Use the framework’s standard OpenTelemetry instrumentation where it is reliable, then verify that the trace ID remains unchanged across the call.
Queues and asynchronous work
Put the trace context into message headers or the queue’s supported propagation fields. A producer span covers publication; a consumer extracts the context and starts a consumer span. If a message can be processed much later or by several workers, preserve the original trace relationship and use span links when a strict parent-child relationship would misrepresent the asynchronous fan-out.
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Tool adapters and handoffs
Tool adapters are common context-loss points because they translate between an agent framework and an HTTP client, SDK, or command runner. Pass the active context explicitly through the adapter. For a remote agent handoff, record the destination agent and invocation ID, propagate the context to the remote process, and retain the local client span so the caller’s waiting time remains visible.
Check propagation instead of assuming it
Different SDKs emit different span trees, and some frameworks instrument model calls but not custom tools. Inspect a real trace and confirm that the following IDs form one chain: workflow, turn, model or tool span, client request, receiving server span, and downstream operation. Microsoft’s multi-agent guidance connects W3C Trace Context with GenAI conventions but notes that nesting depends on the instrumented framework.
Which fields belong on model-generation spans?
Record enough metadata to identify the request and measure it without automatically storing the prompt or completion. At minimum, capture the provider, requested model, logical operation, response ID when available, finish status, duration, and token usage.
- Provider and model: Store the provider name and the model requested by your application. If routing or fallback changes the model, record the effective model as well.
- Operation: Distinguish chat, completion, embedding, moderation, or another inference operation using the convention supported by your instrumentation.
- Response identity: Save a provider response ID when one is returned so an incident investigator can match application telemetry to provider-side records.
- Completion state: Record normal completion, refusal, cancellation, timeout, or provider error as a status or finish attribute.
- Timing: Capture start time, end-to-end duration, and, if your client exposes it, time to first token and streaming completion time.
- Usage: Record input, output, and total token counts when the provider supplies them. Use these values for attribution rather than estimating from character length.
Keep prompt and output content behind an explicit privacy setting. Content is valuable for debugging but can contain system instructions, credentials, customer records, or personal data. A metadata-first trace should remain useful when content capture is disabled.
How should tool executions be traced?
Create one execute_tool span for each logical invocation, even when the tool internally makes several service calls. Use the tool convention’s gen_ai.tool.name and add gen_ai.tool.call.id when the model or framework supplies one.
Record safe tool metadata by default
- Tool name and stable tool version.
- Tool type, such as function, search, database, browser, or remote agent.
- Calling agent name and parent turn ID.
- Provider or model call ID when the tool call originated in a model response.
- Start time, duration, status, retry count, and exception type.
- Remote service name, route, and status code for network-backed tools.
The conventions define opt-in fields for gen_ai.tool.call.arguments and gen_ai.tool.call.result. Enable those fields only after classifying their contents, applying redaction, and restricting access. Hashing or selective field capture can preserve correlation without exporting an entire payload.
Separate planning from execution
If an agent produces a plan before calling tools, create a plan span or equivalent operation and make each execution span a child of the turn or plan step that triggered it. This shows whether an error came from a flawed plan, an incorrect tool selection, or a tool implementation.
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How do I instrument an agent step by step?
The exact APIs vary by language and framework, but the sequence is consistent. The following procedure works whether you use SDK-native tracing, OpenTelemetry APIs, or a combination.
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- Choose a naming and convention version. Use OpenTelemetry GenAI semantic conventions for portable names, and pin the convention and instrumentation versions in your build. These conventions are marked Development, so names and attributes may change.
- Initialize one tracer provider. Configure an OTLP exporter directly or send telemetry to a collector. Set service name, service version, deployment environment, and resource attributes consistently across processes.
- Start the workflow span. At request entry, create the root span and attach a correlation ID that is safe to expose in logs and support tools.
- Wrap orchestration code. Start agent and turn spans around the code that selects models, interprets responses, and dispatches tools. Use the active span as the parent for every child operation.
- Instrument model and tool calls. Add model metadata and usage after the response arrives. Create a tool span before execution, record a sanitized result or error, and close it even when the tool raises an exception.
- Propagate context. Inject W3C context into HTTP, RPC, queue, and tool-adapter requests; extract it at receivers before creating server or consumer spans.
- Handle retries and export. Keep one logical operation span for an automatic retry sequence, annotate the retry count, create attempt child spans when individual attempts need timing, and export the completed trace to your collector or backend.
- Inspect a known failure. Trigger a controlled model timeout and a controlled tool error. Verify that the trace contains the expected parent-child relationships, error status, exception type, and remote service spans.
Vendor-neutral instrumentation sketch
with tracer.start_as_current_span("invoke_workflow", attributes={"workflow.name": "support"}) as workflow:
with tracer.start_as_current_span("agent.turn", attributes={"agent.name": "router"}):
with tracer.start_as_current_span("chat") as model_span:
response = model_client.generate(request)
model_span.set_attribute("gen_ai.provider.name", response.provider)
model_span.set_attribute("gen_ai.request.model", response.requested_model)
model_span.set_attribute("gen_ai.response.id", response.id)
model_span.set_attribute("gen_ai.usage.input_tokens", response.input_tokens)
model_span.set_attribute("gen_ai.usage.output_tokens", response.output_tokens)
with tracer.start_as_current_span("execute_tool") as tool_span:
tool_span.set_attribute("gen_ai.tool.name", "order_lookup")
result = call_tool_with_current_context(response.tool_call)
if result.error:
tool_span.record_exception(result.error)
tool_span.set_status(StatusCode.ERROR)
inject_w3c_context(outbound_headers)
This is a structural example, not a drop-in package. Use the API and attribute names supported by your language SDK, then compare the emitted spans with the convention version you selected. Do not assume that a framework’s automatic instrumentation creates the same nesting shown here.
How should failures, timeouts, and retries appear?
Keep the logical operation visible even when the client retries. A model or tool span can represent the operation as a whole, with a retry-count attribute and final status, while child attempt spans record each request’s duration, response code, and exception.
Distinguish failure types
- Model failure: Record provider timeout, rate limit, authentication failure, refusal, or malformed response on the model span.
- Tool failure: Record validation errors, permission failures, remote status codes, and tool exceptions on the tool span.
- Propagation failure: Record a missing or invalid trace context when a downstream service starts a new root unexpectedly.
- Workflow failure: Mark the root span failed only when the user-visible operation ultimately fails. A recovered retry can leave the workflow successful while preserving the failed attempt as an event or child span.
Attach exceptions to the span that observed them, include a stable error type, and avoid copying secrets into exception messages. A status code alone is rarely enough to tell whether the model, adapter, or remote service caused the problem.
Which tracing approach is best for an AI-agent system?
SDK-native tracing is usually the fastest way to see framework-specific steps. OpenTelemetry and OTLP provide the portability needed when agents call multiple providers, languages, or backends. Many production systems use both: retain useful SDK spans, add OpenTelemetry spans around missing boundaries, and export the combined trace through a collector.
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|---|---|---|---|---|---|---|
| Framework or SDK-native tracing | Often strongest for that framework’s model calls, turns, and handoffs | Limited when components use different SDKs | Depends on the SDK; verify export and context behavior | Usually convenient at the SDK level; inspect defaults | Depends on the vendor dashboard and export options | Fastest start, but can create lock-in or blind spots |
| OpenTelemetry with a collector | Requires instrumentation for uncovered operations | Strongest portability across languages and providers | Designed for OTLP and W3C Trace Context | Centralized processors can redact, filter, and route data | Backend-dependent; supports separate retention policies | More setup, version management, and schema governance |
| Cloud-platform telemetry | Good for platform services and supported runtimes | Varies across clouds and external providers | Check native OTLP ingestion and propagation | Uses platform IAM, policies, and regional controls | Integrated dashboards, but export and retention may vary | Convenient operations with possible portability limits |
| OpenSearch-based observability | Depends on your instrumentation and ingestion pipeline | Works when telemetry is normalized before storage | Can ingest OTLP through the configured pipeline | Apply field-level access and retention in the storage design | Supports normalization, storage, service maps, and hierarchical dashboards | Flexible control, but your team owns more pipeline and capacity work |
Compare options against the same questions: Do model and tool calls appear automatically? Does context survive every boundary? Can you export OTLP? Can content be disabled per run? Can operators search by trace ID, tool call ID, model, tenant, and error type? Can raw content have shorter retention than metadata? What is the cost of storing high-cardinality attributes and token usage?
What privacy controls are required for agent traces?
Agent telemetry can contain secrets and personal data in prompts, system instructions, outputs, tool arguments, tool results, and skill or resource URIs. OpenTelemetry treats content fields as sensitive and recommends opt-in recording. OpenAI documents disabling sensitive-data capture globally or per run.
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Use metadata-first capture
Start with operation names, IDs, durations, statuses, model metadata, token counts, and sanitized error types. Add content only for a narrowly defined diagnostic use case. A trace should remain searchable and causally useful when payload fields are absent.
Redact before export
Remove credentials, authorization headers, payment data, health information, personal identifiers, and confidential business fields before telemetry leaves the process. Redaction at the collector is useful, but high-risk data should be filtered at the source as well because an exporter, buffer, or debug log may otherwise retain it.
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Separate access and retention
Keep raw payloads in a restricted store only when inspection is essential, and link them to the trace with an expiring identifier. Give most operators access to metadata-only traces, retain raw content for a shorter period, and audit who can retrieve it. Make sensitive-data capture reversible through configuration rather than requiring a code deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can traces attribute token use and cost?
Attach usage to each model-generation span, then aggregate by workflow, tenant, agent, model, and time period. Keep input and output token counts separate because providers and pricing rules can treat them differently. If a request uses fallback models, preserve a span for each actual generation so attribution reflects what ran rather than what the router originally requested.
No industry-wide performance or cost figure establishes a universal “best” tracing design. Measure overhead, export volume, storage, latency, and sampling behavior in your deployment, especially when streaming responses or recording high-cardinality attributes.
How do I troubleshoot a broken or incomplete trace?
Start at the user-visible workflow and follow the trace ID. The first missing edge usually identifies the boundary that lost context or the component that was never instrumented.
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- The model span exists but no tool span does: Add a manual span around the dispatcher or tool adapter; automatic model instrumentation rarely knows about custom tools.
- Tool timing includes remote work but no remote service span exists: Instrument the HTTP, RPC, queue, or database client and confirm that the receiving service extracts context.
- Retries look like separate user requests: Keep one parent operation for the retry sequence and attach attempt spans or retry events beneath it.
- Trace search is slow or expensive: Remove unbounded user text and IDs from indexed attributes, keep high-cardinality values as non-indexed fields where supported, and sample routine successful traffic.
- Payloads expose secrets: Disable content capture, rotate affected credentials, review exporter and backend retention, and add source-side redaction before re-enabling diagnostics.
- Span names or attributes differ after an upgrade: Check the pinned GenAI convention and SDK versions, review release notes, and update dashboards and queries together.
What is a practical OpenTelemetry architecture for agents?
A portable architecture has instrumented application processes, an OTLP exporter, an optional collector, and a trace backend. The application creates the causal hierarchy and propagates W3C context; the collector applies redaction, sampling, routing, and batching; the backend supplies search, service maps, retention, and access control.
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Keep resource attributes consistent across services so a trace can be filtered by service name, version, environment, and deployment. Configure the same propagation format for every language involved. If one component cannot use OpenTelemetry, wrap its boundary with manual spans and carry the trace ID through its native correlation mechanism.
What should you verify before relying on agent traces?
Run a small acceptance suite whenever you change an SDK, model client, framework, collector, or semantic-convention version.
- One successful request produces a single root trace with the expected workflow, turn, model, tool, and downstream spans.
- A remote call shows a client span and a child server span with the same trace ID.
- A queue handoff preserves context across producer and consumer processes.
- A model timeout and a tool exception carry status, exception type, duration, and retry information.
- Disabled content capture removes prompts, outputs, arguments, and results from exported telemetry.
- Token counts and effective model names remain correct when routing or fallback occurs.
- Dashboards and alerts still work after semantic-convention or SDK upgrades.
Once these checks pass, operators can answer the questions that ordinary application logs cannot: which turn selected the failing tool, which model response caused the selection, where time accumulated across services, and whether a retry recovered the user-visible operation.
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What should you implement first?
Implement the root workflow span, W3C propagation, model-generation spans, and tool spans before adding prompt or result capture. Then instrument retrieval, memory, handoffs, and downstream services, export through OTLP, and enforce metadata-first privacy controls. This order produces a causal trace quickly while leaving sensitive payloads disabled until your organization has approved how they will be redacted, accessed, and retained.
Frequently Asked Questions
Should I sample AI-agent traces?
Yes, but keep enough complete traces to investigate failures and unusual latency. Head sampling can discard the very requests that later fail, so many teams combine baseline sampling with rules that retain errors, long-running traces, selected tenants, or explicit debug sessions.
Can I correlate agent traces with ordinary application logs?
Add the current trace ID and span ID to structured log records at emission time. Avoid putting full prompts or tool payloads in logs just because tracing is enabled; apply the same redaction and retention policy to both signals.
How do streaming model responses affect tracing?
Keep the model span open until the stream closes or fails, and record total duration plus usage once the provider reports it. If time-to-first-token matters operationally, record it as a span event or separate timing attribute rather than ending the span when the first chunk arrives.
What if a provider does not return token usage?
Mark usage as unavailable instead of estimating silently. You can add a separately labeled estimate for internal capacity planning, but keep it distinct from provider-reported counts so cost reports do not mix measured and inferred values.
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