OpenTelemetry helps you see where an AI application spends time and where requests fail by tracing work across your application, model provider, retrieval layer, and tools. It does not make a model faster or improve answer quality by itself; it gives you evidence to guide changes. Treat OpenTelemetry as the framework that instruments, collects, and exports telemetry, and use a separate observability backend to analyze it.
What OpenTelemetry shows in an AI application
OpenTelemetry is a vendor-neutral, open-source framework for instrumenting, generating, collecting, and exporting traces, metrics, and logs. Its documentation, last modified August 29, 2025, says it is supported by more than 90 observability vendors; that is the project’s dated statement, not a freshly measured count. The OpenTelemetry Collector can receive, process, and export telemetry without tying that pipeline to a single backend. OpenTelemetry documentation
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A trace represents the path of one request through a system. It contains spans: timed operations with parent-child relationships, attributes, and, when applicable, error information. In an AI workflow, a parent span might cover an agent run, with child spans for retrieval, a model request, and a tool call. If the response is slow, the trace helps show which operation consumed time; if it fails, it can help locate the failing step. A trace is diagnostic evidence, not proof that the generated answer was correct.
Use traces to investigate individual request paths, metrics to see aggregate behavior and trends, and logs for event details. A backend is where these signals can be queried and visualized; OpenTelemetry itself is not that analysis interface. Context propagation carries trace identity between supported components so their work can appear in the same request trace. OpenTelemetry’s observability primer
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Map one representative user action before instrumenting
Start with a single user-facing operation, then mark the boundaries that matter in your actual architecture. A possible trace looks like this:
- Incoming request: the HTTP or application-framework server receives the user action.
- Orchestration: the application routes the request, chooses a workflow, or runs an agent.
- Context gathering: retrieval, database, or cache work occurs, if the application uses it.
- Model/provider request: the application sends input to a model service and receives a result.
- Tools: an agent invokes an internal function, external service, or MCP tool, if applicable.
- Response: the application assembles and returns the result.
This is a proposed map, not a required architecture: omit boundaries that do not exist in your application. Give spans stable, meaningful names and use consistent attributes to distinguish operations. Avoid putting user-specific or secret values into span names; high-cardinality or sensitive data can create operational and privacy problems.
Choose zero-code, code-based, or combined instrumentation
Zero-code instrumentation can provide coverage for supported frameworks and libraries without changing business logic. Code-based instrumentation lets developers create application-specific spans and add context that an automatic agent cannot infer. The two approaches can be combined: use automatic coverage for framework and network edges, then add manual spans around meaningful workflow steps. Check language, library, and provider support for the exact stack rather than assuming every component emits the same data. OpenTelemetry instrumentation documentation
| Approach | Best suited to | Trade-off |
|---|---|---|
| Zero-code | Getting initial visibility into supported HTTP servers, clients, and libraries with little or no application-code change. | Setup can be quicker, but the emitted spans and context depend on available instrumentation and configuration; business intent may remain unclear. |
| Code-based | Marking application operations such as an agent run, a business decision, or a retrieval step, and attaching carefully selected context. | Requires code changes and maintenance, but gives the team control over operation boundaries and application-specific attributes. |
| Combined | Covering supported infrastructure automatically while adding manual spans where application meaning matters. | Requires checking that automatic and manual spans complement one another rather than duplicating work or creating confusing boundaries. |
Use resource attributes to identify the service and deployment, and propagate context so child operations remain connected to the originating request. Prefer established semantic conventions and consistent attribute names over one-off keys. OpenTelemetry’s current semantic-conventions documentation identifies version 1.44.0; check the conventions and instrumentation-library support applicable to your language and provider before depending on particular fields. OpenTelemetry semantic conventions
Instrument the request path in practical stages
- Establish a baseline: record end-to-end request duration and errors for a representative workload before changing instrumentation or application behavior. State what counts as a completed, failed, or abandoned request in your own service.
- Add available automatic coverage: enable supported instrumentation for the server framework and outbound HTTP or database clients. Confirm that it actually emits spans in the deployed environment.
- Add workflow spans: create code-based spans for operations whose duration or outcome you need to isolate, such as an orchestration run or retrieval phase. Keep names stable and attributes deliberate.
- Connect the context: configure context propagation across asynchronous work and service boundaries where supported. Inspect a real trace to verify that child work appears under the correct request.
- Export to a backend: configure the SDK or Collector pipeline to send the signals to a backend that accepts the chosen formats and conventions. Verify delivery and inspect actual payloads; successful app startup alone does not prove usable telemetry.
- Compare and iterate: use aggregate measurements to spot patterns, inspect representative traces to locate the responsible operation, make a targeted application change, and repeat the same workload comparison.
For GenAI-specific attributes, the OpenTelemetry GenAI semantic conventions are maintained in a separate project repository. The repository covers GenAI clients, MCP, and provider-specific conventions, but support can vary by stack; do not assume a field is present merely because a convention exists. OpenTelemetry GenAI semantic conventions repository
Measure latency, failures, and user-facing reliability
Use metrics for aggregate behavior and traces to examine the path of an individual request. Useful views depend on the application, but commonly include end-to-end latency distributions, operation-level durations, request volume, and errors. Compare relevant dimensions—such as operation, model/provider, and tool—without collecting unnecessary user identifiers. OpenTelemetry’s metric conventions emphasize consistent names and attributes, meaningful aggregation, unambiguous definitions, and seconds as the duration unit. OpenTelemetry metrics semantic conventions
System telemetry cannot establish whether an answer is useful or an action is correct. Pair latency and error measures with an application-specific reliability or quality measure, such as whether the user’s requested task completed successfully, if your product can assess that safely and validly. There is no universal GenAI quality metric or threshold established by OpenTelemetry, so define and validate one for the product rather than presenting it as an OpenTelemetry standard.
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Use the measurements to form a testable hypothesis: for example, a slow retrieval span may justify investigating the retrieval path, while repeated model-call errors may warrant examining provider failures and retry behavior. Then evaluate the change against the same workload and user-facing outcome. Instrumentation can reveal where to investigate; it cannot promise a particular speedup or reliability gain.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the official Agent Service example demonstrates
The OpenTelemetry Demo Agent Service is a Python FastAPI agent. Its documentation says Traceloop initializes the OpenTelemetry SDK and bundled GenAI instrumentation, while HTTPX instrumentation is enabled explicitly and FastAPI instrumentation is added to the app. The documented coverage includes server spans for /prompt, client spans for outbound HTTP calls, and spans for LangChain/LangGraph steps, LLM invocations, and MCP tool calls when MCP is enabled. A workflow decorator encloses an agent run in a parent span. OpenTelemetry Demo Agent Service
The example is useful for seeing how automatic and application-level spans can fit together, but it is a demo configuration, not a universal setup recipe or evidence of measured optimization results. It documents prompt and completion message attributes unless content capture is disabled with the demo-specific option TRACELOOP_TRACE_CONTENT=false. It also says metrics are configured unless TRACELOOP_METRICS_ENABLED=false, but defines no custom metrics. Its standard-library logs go to standard output and are collected by the container runtime; in the documented setup, those logs are not correlated with traces by default.
Protect prompts, completions, and other sensitive telemetry
Instrumentation may emit prompt or completion content as span attributes. Before exporting telemetry, decide whether those bodies are needed at all. OpenTelemetry’s sensitive-data guidance identifies personal information, credentials, session tokens, financial or health data, and user behavior as examples requiring attention; it places responsibility on implementers to understand and review what instrumentation emits. Applicable consent, policy, and regulatory duties depend on the implementation. OpenTelemetry guidance on handling sensitive data
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- Disable unneeded content capture: use the setting supported by your instrumentation. The demo’s
TRACELOOP_TRACE_CONTENT=falseoption is specific to that demo and should not be treated as a universal OpenTelemetry variable. - Minimize and restrict: prefer allowlists or deletion of unnecessary attributes, restrict who can access telemetry, and set retention to match the operational need.
- Transform in the pipeline where appropriate: the Collector documents attribute, filter, redaction, and transform processors for controlling telemetry data. Verify the effective configuration and exported payload after deployment.
- Do not assume hashing makes data anonymous: predictable source identifiers may be recoverable from hashes, so hashing alone is not a privacy guarantee.
Select a backend by operational fit, not by the OpenTelemetry label alone
OpenTelemetry can export telemetry for analysis elsewhere. When assessing a backend, verify the behavior that matters for your workload rather than assuming that accepting OpenTelemetry guarantees equivalent support across products. The source material does not establish a vendor ranking or pricing comparison.
- Signal and convention support: confirm ingestion of the traces, metrics, and logs you produce, including the GenAI attributes your instrumentation actually emits.
- Correlation: check whether trace, metric, and log views preserve useful request context and whether any correlation must be configured separately.
- Access and retention: verify permissions, data location and retention controls against the sensitivity of your telemetry and your operational requirements.
- Export and portability: determine whether the Collector or another pipeline can route data to the destinations you need without changing application instrumentation.
- Workload cost: estimate the effect of request volume, span and attribute volume, retention, and any sampling choices under your expected use. No universal cost follows from using OpenTelemetry.
Common gaps to check when a trace is not useful
- Only the outer request is visible: add supported library instrumentation and manual spans around important workflow operations, then inspect a new trace.
- Child work appears as a separate trace: check context propagation through the service, asynchronous boundary, or tool integration where the link is lost.
- Spans exist but lack meaning: use stable operation names and carefully chosen attributes; do not expect automatic instrumentation to infer business context.
- Logs cannot be tied to a trace: verify log correlation in the application and backend. The Agent Service demo’s standard-output logs are not correlated with traces by default.
- Telemetry exposes content you do not need: disable content capture where supported, filter or transform attributes, and re-check the exported data rather than relying on an assumed configuration.
- Metrics are ambiguous: define what each measure counts and its unit, and use consistent names and attributes in line with the metrics conventions.
OpenTelemetry’s observability primer describes proper instrumentation as providing enough information that developers do not need to add more instrumentation to troubleshoot an issue. In practice, treat that as an aim for useful coverage, not a guarantee: the right level depends on the application and the incidents the team needs to diagnose. OpenTelemetry observability primer
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