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OpenTelemetry helps you find where an AI request spends time and where it fails by connecting telemetry across your application, model calls, retrieval, and tools. It does not make a model more accurate or an application faster by itself; it gives engineers evidence to decide what to change. The practical approach is to trace representative requests, measure aggregate latency and errors, and collect only the data your team needs—especially when prompts or responses may contain sensitive information.
What OpenTelemetry can show you in an AI application
OpenTelemetry is a vendor-neutral, open-source framework for instrumenting software, generating telemetry, collecting it, and exporting it for analysis. Its signals include traces, metrics, and logs. A separate observability backend receives and analyzes that telemetry; OpenTelemetry is not itself the analysis interface. The project’s documentation, last modified August 29, 2025, says OpenTelemetry was supported by more than 90 observability vendors at that time. That is a dated statement from the project, not a current vendor census. OpenTelemetry documentation
A trace represents the path of one request through a system. It is made up of spans, which record individual operations and can be nested to show parent-child relationships. In an AI application, those operations might include an incoming request, orchestration, a model-provider call, retrieval, or a tool call. Examining the spans can help answer whether time was spent in your own application, an external call, or another step—and where an error occurred. OpenTelemetry’s observability primer
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Metrics help describe behavior across many requests, while traces let you inspect an individual request path. Logs can provide event details, but they are most useful alongside traces when the configured pipeline correlates them. A trace does not tell you whether an AI answer was correct: pair operational signals with an application-specific measure of the outcome users care about.
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Choose instrumentation that fits the context you need
Zero-code instrumentation can quickly cover supported frameworks and libraries without adding instrumentation calls throughout application code. Code-based instrumentation can add stable, meaningful spans and application-specific context. Both approaches can be used together; available coverage depends on the language, library, and deployment environment. OpenTelemetry instrumentation documentation
| Approach | Useful for | Trade-off |
|---|---|---|
| Zero-code instrumentation | Quick initial coverage of supported frameworks, libraries, or runtime activity. | Coverage and detail depend on the available instrumentation; it may not expose the business context needed to interpret an AI workflow. |
| Code-based instrumentation | Adding application-specific spans, stable operation names, and context that automated instrumentation cannot infer. | Requires application changes and ongoing care as the workflow evolves. |
| Combined instrumentation | Using automated coverage for common edges and code-based spans for important workflow boundaries. | Requires checking that overlapping instrumentation does not create confusing or redundant spans. |
Use resource attributes to identify the service and deployment, and propagate trace context so child operations remain connected to the originating request. Prefer established semantic conventions and consistent attribute names over ad hoc keys. OpenTelemetry’s current semantic-conventions documentation identifies version 1.44.0; check the applicable convention and instrumentation support for your actual stack rather than assuming every field is available. OpenTelemetry semantic conventions
Map a representative AI request into a trace
Start with one user action and identify the operations that matter in your application. A useful candidate shape is shown below. These are possible boundaries, not mandatory components: an application may have no retrieval step, no external tool, or a different orchestration pattern.
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- Orchestration or agent workflow: Add an enclosing span for the operation that coordinates the request.
- Model/provider request: Capture the model interaction as a child operation where supported, with timing and outcome information appropriate to your privacy policy.
- Retrieval or database work: Instrument these operations when they are part of the request path, so their time and failures can be distinguished from model time.
- Tool or MCP calls: Add spans for tools that contribute materially to the workflow and propagate context through the call.
- Response boundary: Keep the end-to-end request visible so operation-level timings can be interpreted against total user-perceived latency.
Do not add spans merely to maximize span count. The goal is a trace that answers operational questions: which operation was slow, which failed, and whether the affected request followed an unusual path.
Use metrics and traces to identify what to change
Track aggregate behavior with metrics, then use traces to investigate the requests behind a latency spike or error pattern. Compare latency distributions and errors across dimensions that are meaningful for your application, such as operation, provider, or tool. Avoid labels with unbounded values, such as raw prompts or unique user identifiers, which can create privacy and cardinality problems.
- End-to-end latency: Measure the full request path to understand the time experienced at the application boundary.
- Operation timing: Compare model, retrieval, orchestration, and tool spans to find where time accumulates.
- Error behavior: Observe which operation failed and whether failures cluster around a particular provider, tool, or workflow path.
- User-facing reliability: Pair speed and availability indicators with a measure of whether the answer or action met the application’s requirements.
OpenTelemetry’s metric conventions emphasize consistent names and attributes, meaningful aggregation, unambiguous metrics, and seconds as the duration unit. OpenTelemetry metrics semantic conventions There is no universal GenAI quality metric or threshold established by these conventions; define quality evaluation for the use case rather than treating an OpenTelemetry field as a model-quality score.
What the official AI-agent example demonstrates
The OpenTelemetry Demo Agent Service is a Python FastAPI agent. Its documentation describes Traceloop initializing the OpenTelemetry SDK and bundled GenAI instrumentation, with HTTPX instrumentation explicitly enabled and FastAPI instrumentation added to the application. It reports server spans for /prompt, client spans for outbound HTTP calls, and spans for LangChain or LangGraph steps, LLM invocations, and MCP tool calls when MCP is enabled. A workflow decorator creates an enclosing span for an agent run. OpenTelemetry Demo Agent Service
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The example is useful for seeing how a parent workflow can contain model and tool work, but it is not proof of a particular performance gain or a universal setup recommendation. It also illustrates why you must inspect the actual signal pipeline: the demo says its standard-library logs go to standard output and are collected by the container runtime, but are not correlated with traces in that configuration. It configures metrics unless TRACELOOP_METRICS_ENABLED=false is set, while defining no custom metrics.
The demo records prompt and response content in the gen_ai.input.messages and gen_ai.output.messages span attributes unless its TRACELOOP_TRACE_CONTENT=false option is set. That setting is specific to the demo; it is not a universal OpenTelemetry environment variable. GenAI conventions are maintained in a separate OpenTelemetry repository covering GenAI clients, MCP, and provider-specific conventions. Check that repository and the support of your language and provider instrumentation before depending on particular fields. OpenTelemetry GenAI semantic conventions
Protect prompts, responses, and other sensitive telemetry
Instrumentation can emit more data than engineers expect. OpenTelemetry’s sensitive-data guidance makes implementers responsible for understanding and reviewing the telemetry emitted by their instrumentation libraries. Examples of potentially sensitive data include personally identifiable information, credentials, session tokens, financial or health data, and user behavior. Applicable consent, policy, and regulatory obligations depend on the implementation. OpenTelemetry guidance on handling sensitive data
- Decide whether content is necessary. If prompt or completion bodies are not needed for troubleshooting, do not capture them. Content can expose user data and can make telemetry harder to govern.
- Inspect emitted payloads. Review real telemetry from each instrumented path, not just the attributes you intended to add.
- Minimize and control access. Limit collection, retention, and access to what the troubleshooting use case justifies.
- Remove or transform attributes in the pipeline. The Collector documents attribute, filter, redaction, and transform processors as options for changing or removing telemetry data. Verify the deployed configuration actually applies the intended processing.
- Do not treat hashing as automatic anonymization. Predictable source identifiers can remain guessable after hashing.
Evaluate the telemetry pipeline before relying on it
OpenTelemetry includes the Collector, a vendor-agnostic component that can receive, process, and export telemetry. A backend is still needed to analyze the exported signals. Before relying on the setup during an incident, check the backend and pipeline against practical requirements:
- Which signals and semantic conventions can it receive and interpret?
- Can the team correlate traces, logs, and metrics in the way its instrumentation emits them?
- Do retention periods and access controls fit the sensitivity of the collected data?
- Can telemetry be exported or moved in a way that meets portability requirements?
- Will the workload’s data volume and retention create acceptable operating costs?
These are selection criteria, not a claim that one backend is best. OpenTelemetry standardizes instrumentation and data movement; it does not remove the need to validate backend behavior, retention, access, or cost for your own workload.
Quick Recap
A practical optimization loop
- Choose a representative user action. Map its actual request path and mark only the operations needed to explain latency or failure.
- Establish baseline signals. Instrument available HTTP and framework edges, then add application spans where the workflow needs clearer boundaries or context.
- Validate trace continuity. Confirm that context propagates through child work and that a trace shows the expected request path.
- Review data exposure. Inspect exported attributes, suppress prompt or completion capture when it is unnecessary, and verify pipeline processors and access controls.
- Compare aggregate and individual behavior. Use metrics to find patterns in latency and errors; inspect traces to understand representative requests behind those patterns.
- Make one change and evaluate it. Compare the relevant latency, error, and user-facing reliability measures before and after under comparable workload conditions. Do not infer a general improvement percentage from instrumentation alone.
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