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OpenTelemetry (OTel) is a strong, vendor-neutral way to generate, collect, and export telemetry, but it is not a storage or visualization backend. For an AI application, useful observability also depends on instrumenting the full workflow—not just the model call—then choosing a backend and making deliberate decisions about reliability and sensitive data.
What OpenTelemetry does—and where its job ends
OpenTelemetry is a framework and toolkit for generating, collecting, and exporting telemetry. Its components include a specification, protocol, semantic conventions, APIs, language SDKs, instrumentation libraries, automatic instrumentation, and the Collector. The project’s official overview explicitly distinguishes OTel from an observability backend: OTel does not provide the storage and visualization layer.
That boundary matters. Instrumentation can produce data and the Collector can process and export it, but another system must store it and provide the queries, dashboards, alerts, and investigation tools your team needs. OTel can preserve flexibility in how telemetry is exported; it does not eliminate the work of selecting, configuring, and operating a destination.
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An AI request often crosses several boundaries before it returns an answer. A trace that records only the API request to a model may miss where the delay, failure, or unexpected behavior occurred. For a retrieval-augmented generation (RAG) service or agent, useful stages can include prompt assembly, retrieval, and external tool calls.
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In a May 26, 2026 CNCF article, Jaeger project maintainer Jonah Kowall describes agent execution paths involving prompt assembly, vector-database retrieval, and multiple external tools. The article calls out embedding-model latency, tool calls, and token usage as signals relevant to RAG and agent developers. These examples show why a single model-request measurement is not a complete account of an AI workflow.
What AI telemetry can capture
OpenTelemetry’s GenAI material describes three signal types—traces, metrics, and events—with different roles in understanding model behavior. The CNCF article published January 20, 2025 outlines examples:
- Traces: model-interaction lifecycle details such as parameters, response details, token counts, and errors, connected to the wider request path.
- Metrics: aggregates such as request counts, latency, and token counts, useful for observing patterns across requests.
- Events: records such as prompts and model responses. The article describes prompt and response events using the OpenTelemetry Logs API.
These are descriptions of the signal model, not a guarantee that every language, library, model provider, or backend supports every field in the same way. Convention maturity is also time-sensitive: the January 2025 article described prompt/response conventions as in development and unstable. A June 2026 OpenTelemetry post still described GenAI semantic conventions as emerging. Check the current convention and instrumentation documentation for the specific stack you plan to deploy.
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Build the pipeline from application to investigation
Think of an AI observability setup as a chain of responsibilities rather than a single installation. Each stage needs to pass enough context to make the next stage useful.
- Instrument the workflow. Add telemetry around the application request and the meaningful AI stages: prompt construction, retrieval or embedding, model interaction, and tool calls where applicable. Carry trace context across service, worker, and tool boundaries so related operations can be correlated.
- Choose what to emit. Decide which traces, metrics, and events answer operational questions. For example, a team may need latency and error data even when it chooses not to record prompt or response content.
- Collect and process telemetry. Use the OpenTelemetry Collector or another supported collection path to receive and process emitted data. Keep the configuration aligned with the fields and signals your chosen instrumentation actually produces.
- Export to a backend. Select a destination that can ingest your telemetry and provide the storage and investigation capabilities you require. Verify how it handles trace correlation and the AI workflow details you emit.
- Investigate in the backend. Use the backend’s query, visualization, and alerting functions to follow a request across its stages and find where errors or delays occurred. These capabilities are not supplied by OTel itself.
Jaeger’s implementation illustrates how these layers can relate: Kowall’s May 2026 CNCF article says Jaeger v2 replaced its original collection mechanisms with the OpenTelemetry Collector framework and describes work to visualize evolving GenAI conventions. That is a project-specific account, not a universal deployment recipe or guarantee of support for every convention.
Choose a backend against your workload
There is no evidence here for a universal winning backend or a comparative performance ranking. Evaluate candidate destinations against the needs of your own application and operations:
- Ingestion and correlation: Can it accept the telemetry you export and connect AI spans to existing service and infrastructure traces?
- AI workflow visibility: Can you query the spans and GenAI attributes your instrumentation emits, including retrieval and tool stages where relevant?
- Investigation features: Does it provide the storage, queries, visualizations, and alerting your team needs?
- Data controls: Can you control capture and retention of sensitive prompt, response, or tool payload data?
- Operational fit: What does your team need to operate and configure at its expected scale?
- Portability: Which capabilities use standard OTel data, and which depend on backend-specific extensions?
OpenTelemetry’s documentation notes that backends may be commercial or open source. Treat compatibility, retention, and operational fit as items to verify for the actual product and deployment rather than assuming that OTel makes every destination interchangeable.
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Test resilience instead of guessing at settings
There is no single retry count, queue size, sampling rate, or failover configuration established for all AI telemetry pipelines. Those choices depend on the workload and deployment. Define what reliable delivery means for your system, then test the failure modes that could undermine it.
- Exporter or backend interruption: Simulate an unavailable destination and observe what happens to telemetry delivery, application behavior, and recovery after service returns.
- Signal prioritization: Identify which data is essential during an incident and what can be delayed or omitted under constrained conditions.
- Broken context: Check whether a trace remains connected across API handlers, workers, retrieval systems, model calls, and tools.
- Missing or repeated data: Decide how operators will recognize dropped, delayed, or duplicated telemetry and distinguish it from application behavior.
- End-to-end investigation: Follow a representative request through the system and verify that the emitted fields support the questions responders actually need to answer.
These are validation questions, not prescribed settings. The right outcome is a pipeline whose behavior under interruption is known and acceptable for the service’s requirements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Decide whether to capture prompts and responses
Prompt and response content can provide debugging context, but it can also contain sensitive information. The 2025 CNCF article demonstrates that message-content capture can be enabled; it does not establish a universal privacy, security, or retention rule. Make a deployment-specific decision about whether to capture content, who can access it, how long it is retained, and whether metadata is sufficient for routine diagnosis. Avoid treating full payload capture as a prerequisite for tracing: request correlation and carefully selected attributes can be useful even when content is excluded.
What OTel’s graduation means for AI teams
The Cloud Native Computing Foundation announced OpenTelemetry’s graduation on May 21, 2026, reporting more than 12,000 contributors from over 2,800 companies. Graduation is evidence of the project’s maturity and ecosystem standing; it does not resolve the separate choices involved in AI instrumentation, backend selection, privacy, or pipeline operations. A June 2026 OpenTelemetry post continued to describe GenAI semantic conventions as emerging, so assess convention maturity at the time you implement rather than inferring it from the project’s graduation status.
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