If your agent returns a successful answer but its trace has no tool-call span, the call may never have been wired into the agent, instrumentation may not have recorded it, or the span may have failed to reach your tracing backend. Diagnose those as separate boundaries: first confirm the tool can run, then check instrumentation and span creation, and finally verify export and delivery.
Start by checking whether the tool could run
For LangChain and LangGraph, Microsoft’s troubleshooting guide names the symptom “LangChain spans appear but tool calls are missing.” Its first checks are whether tools are passed to bind_tools() and whether the graph includes the tool execution node. A model can produce a response without invoking a tool, and framework-level spans alone do not prove that a tool was available or executed.
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Use the framework’s own wiring as the first diagnostic boundary: confirm the model has the intended tools, then inspect the graph or agent path for the node that actually executes them. Microsoft’s steps are documented in Configure tracing for AI agent frameworks.
Check that instrumentation starts in the agent’s runtime
Instrumentation must be installed and initialized in the same runtime environment as the agent. AWS advises checking package installation, the startup command, and initialization order; its guidance specifically recommends initializing before importing the framework. If HTTP spans are present but agent, model, or tool spans are absent, verify that the relevant framework instrumentor is enabled rather than assuming that some telemetry means all instrumentation is active.
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- Confirm the instrumentation package is installed in the environment that launches the agent, not only in a development or build environment.
- Check the documented startup hook and make sure it runs before framework imports or agent construction.
- For package-manager environments, development reloaders, and pre-fork workers, verify that instrumentation runs in the process that handles the agent request.
- Check whether the framework and package versions are supported, and whether that instrumentation has been disabled.
AWS lists these setup and compatibility checks in Send AI agent telemetry. Configuration names and version prerequisites can vary between its AWS OpenTelemetry distribution route and OpenInference-based instrumentation; follow the setup for the route you chose.
Avoid competing providers and duplicate instrumentors
More instrumentation is not automatically better. Enabling equivalent instrumentors twice can produce duplicate spans. Creating a second tracer provider can also disconnect spans from the provider configured to export them. AWS says its distribution registers the global tracer provider and recommends attaching instrumentors, processors, and custom spans to that existing provider rather than creating a competing one.
Inspect the trace tree and tool-span details
Once the tool is wired and instrumentation is active, inspect the trace for a tool execution span beneath the agent or workflow invocation. Microsoft’s agent-tracing overview identifies execute_tool as a child span and describes attributes including gen_ai.tool.call.arguments and gen_ai.tool.call.result. The exact parent-child nesting can vary by instrumentation, so assess the actual trace structure rather than requiring one universal tree shape.
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These GenAI semantic conventions are marked Development status in Microsoft’s overview, which means names and representations may change. If a span exists but its attributes differ, check the convention version selected by your instrumentation and how your backend maps those fields. See Agent tracing overview.
Frameworks can represent tool activity differently
OpenAI Agents SDK documents a built-in trace model with runner task, model-turn, agent, generation, function, guardrail, and handoff spans. Langfuse supports native integrations as well as manually created observations; in its current-observation context, nested observations become children of the current observation. These are framework-specific approaches, not interchangeable guarantees about span names or nesting. Consult the relevant documentation: OpenAI Agents SDK tracing and Langfuse instrumentation.
Separate span creation from export and delivery
If a tool span appears locally or in an in-process exporter but not in the backend, focus on delivery rather than agent wiring. Check exporter configuration and logs, the OTLP protocol and endpoint, headers, network access, credentials and permissions, and any backend resource policy. A successful agent invocation only confirms the application path completed; it does not establish that telemetry was exported successfully.
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AWS documents a specific endpoint trap: if an OTLP endpoint already includes /v1/traces, endpoint logic that appends that path again can lead to a 404. Its guide recommends using the traces-specific endpoint in that case. Compare the configured endpoint with the exporter’s expected form and inspect delivery errors in AWS’s CloudWatch telemetry guidance.
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There is no universally best vendor or setup in the documented options. Compare routes on the dimensions that determine whether they will capture the trace you need:
- Framework and version support: Confirm that your agent framework and installed version are covered by the chosen instrumentation.
- Startup model: Determine whether the route uses zero-code startup instrumentation, explicit registration, or manual spans.
- Tool-span detail: Decide whether you need framework-native tool spans and attributes or whether generic OpenTelemetry spans are sufficient.
- Provider and exporter ownership: Identify which tracer provider receives spans and which exporter sends them to the backend.
- Captured content: Review whether prompts, tool arguments, results, or outputs are recorded and how to control that behavior.
Microsoft Foundry’s LangChain/LangGraph instructions describe enabling the Microsoft OpenTelemetry distro and LangChain instrumentation. The page also gives platform-specific settings for GenAI message capture and semantic conventions, and notes that custom operations require manual spans. Treat those settings as Foundry-specific, not as universal OpenTelemetry environment variables: Microsoft Foundry framework tracing setup.
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Microsoft Agent Framework documents zero-code setup with the OpenTelemetry CLI, custom spans through its tracer wrapper, and environment controls for instrumentation, message events, semantic convention selection, and sensitive-data capture. Its latest experimental convention mode differs from the v1.36 representation, so check the installed version and selected mode when comparing attributes: Microsoft Agent Framework observability.
Protect prompts, tool arguments, and results
Tracing can capture more than operation names. Prompts, responses, tool arguments, and results may appear in telemetry, so minimize or redact sensitive content before it reaches the exporter and apply suitable access controls and retention policies to stored traces. Microsoft’s guidance treats traces as production telemetry and advises redacting sensitive content before collection.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsThe OpenAI Agents SDK documentation says generation and function spans may store input and output, and that sensitive-data capture is enabled by default in the documentation cited here. Defaults can vary by installed SDK version; verify the version-specific setting and configure capture intentionally. See OpenAI Agents SDK tracing, Microsoft’s agent tracing overview, and Microsoft Agent Framework observability.
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