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OpenTelemetry (OTel) can generate, collect, and export telemetry from an AI application, but it does not store or visualize that data. A useful pipeline also needs instrumentation for the full AI workflow, a destination that can query and present the signals, and explicit decisions about reliability and sensitive data. OTel is a foundation—not an end-to-end observability system.
Why OpenTelemetry alone is not enough
OpenTelemetry is an observability framework and toolkit. Its components include a specification, protocol, semantic conventions, APIs, language SDKs, instrumentation libraries, automatic instrumentation, and the Collector. The project’s official documentation draws an important boundary: OTel is not an observability backend. Storage and visualization are left to other tools.
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That means adopting OTel does not, by itself, give a team a place to retain telemetry, search it, build dashboards, alert on it, or investigate an incident. Those capabilities depend on the backend and how the team configures and operates it. Vendor neutrality can preserve a choice of destination; it does not remove the work of making that choice.
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For an AI application, “OTel alone fails” is therefore a system-boundary problem, not a claim that OTel cannot instrument AI. The practical task is to connect application and infrastructure instrumentation to a collection and export path, then to a backend suited to the team’s workflows.
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What an AI observability pipeline needs to show
A single model request rarely describes the whole path that produced an answer. An AI workflow may assemble a prompt, retrieve information, call a model, and invoke one or more external tools. If those steps are not represented and correlated in telemetry, an operator may see a slow or failed request without knowing which stage caused it.
In a May 26, 2026 CNCF article, Jaeger project maintainer Jonah Kowall describes agent paths involving prompt assembly, vector-database retrieval, and multiple tool calls. He identifies embedding-model latency, external tool calls, and token usage as signals relevant to RAG pipelines and agents. These examples make the key instrumentation question concrete: can the team follow one user-visible operation through its meaningful internal steps?
Traces for the sequence of work
Represent the request and its child operations so an investigation can distinguish prompt preparation, retrieval, model interaction, and tool execution where those stages exist. Preserve trace context as work crosses APIs, workers, model calls, retrieval components, and tools; otherwise the trace may fragment at the boundaries that matter most.
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Metrics can summarize behavior across requests, such as request volume, latency, and token counts. They help reveal aggregate changes, while traces help inspect an individual execution. The two answer different questions and are more useful together than either alone.
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Events for AI interaction details
Events can record details such as prompts and model responses. That can help explain why a particular interaction behaved as it did, but it also puts message content into telemetry and requires a deliberate data-handling decision. Capturing content should not be treated as an automatic default simply because it is technically available.
The January 20, 2025 CNCF article on OpenTelemetry for generative AI describes traces, metrics, and events for AI behavior, including request and response details, token counts, errors, latency, prompts, and responses. It describes prompt/response events using the Logs API and a Python instrumentation library for OpenAI interactions. At that publication date, the event-based prompt/response conventions were in development and marked unstable; treat that status as a dated account, not a current compatibility guarantee.
How to build the pipeline across application boundaries
A resilient design makes each handoff visible and gives telemetry a route to a backend. The exact implementation depends on the languages, services, and deployment, but the architectural responsibilities are distinct.
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1. Instrument application and infrastructure
Use OTel APIs, SDKs, instrumentation libraries, or automatic instrumentation where appropriate, and add AI-specific spans and attributes for the stages generic request instrumentation cannot explain. Decide which operations need their own spans and which details are essential to diagnosing failures or latency.
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2. Preserve context through the workflow
Carry trace context between the entry-point API, asynchronous workers, retrieval systems, model interactions, and tools. Validate that a trace remains connected when execution crosses process or service boundaries, including when work is retried or handed to a worker.
3. Collect and process telemetry
The OTel Collector is part of the project’s documented toolkit for collecting and exporting telemetry. Configure collection and processing to fit the deployment, and make the path from instrumented service to exporter observable enough that the team can detect delivery problems.
4. Export to a backend
Select a backend that receives the telemetry and provides the storage, query, visualization, and alerting capabilities the team needs. OTel provides an instrumentation and transport foundation; it does not decide how long data is stored or how operators will investigate it.
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When a request is slow or incorrect, inspect the linked stages: retrieval duration, model interaction, tool calls, errors, and relevant token usage. The goal is to identify which portion of the execution path needs attention rather than treating every symptom as a model issue.
How to choose an OpenTelemetry-compatible backend
There is no single backend choice established for every AI workload. Compare candidates against the actual telemetry and operational needs of the system rather than assuming that OTLP ingestion alone makes two destinations equivalent.
- Ingestion and correlation: Can it ingest the OTel data you emit and correlate AI spans with existing application and infrastructure telemetry?
- AI workflow visibility: Can it display the spans and GenAI attributes your instrumentation produces, including retrieval and tool activity where relevant?
- Investigation features: Does it provide the storage, query, visualization, and alerting functions your operators require?
- Data controls: Can the team control whether prompt and response content is captured, who can access it, and how it is retained?
- Operational fit: What work is required to deploy, configure, and operate it at the team’s scale?
- Portability: Which capabilities rely on standard OTel data, and which depend on backend-specific extensions or formats?
These are evaluation criteria, not comparative test results. Jaeger’s evolution illustrates one project’s direction: the 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. Its roadmap and convention support can change, so verify current project documentation before relying on a particular implementation detail.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What resilience means in practice
Resilience is not a universal retry count, queue size, or sampling percentage. Those settings depend on workload and deployment choices, and the cited project material does not establish values that apply across systems. Instead, define what telemetry must survive and test the failure modes that could break the path.
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- Identify essential signals and attributes. Decide which traces, metrics, events, and fields are necessary to investigate the failures your team cares about, and which can be omitted or reduced.
- Check context continuity. Follow a representative request through model, retrieval, worker, and tool boundaries, then verify that the resulting data remains associated with the same execution.
- Check delivery failure visibility. Define how operators will notice telemetry that is dropped, delayed, or duplicated, and exercise those conditions in a controlled test.
- Review behavior after recovery. Confirm that the pipeline’s observed behavior during and after an interruption matches the service’s operational requirements.
These checks are an engineering test plan, not a guarantee that any particular configuration will be reliable. Record expected behavior for the deployment and validate it under the conditions that matter to the team.
Should telemetry contain prompts and responses?
Message content can help explain a specific model interaction, but putting it into telemetry expands what the observability system stores and exposes. The 2025 CNCF article demonstrates that message-content capture can be enabled in its example; it does not establish a universal retention policy or comparative privacy assessment.
Decide whether the debugging value justifies capturing content for the application’s data and access requirements. If content is not needed for routine diagnosis, consider whether metadata and trace identifiers answer the operational question instead. Set access and retention expectations with the systems that receive the data, and verify that the chosen instrumentation and backend behave as intended.
What OTel’s graduation does—and does not—mean
The Cloud Native Computing Foundation announced OpenTelemetry’s graduation on May 21, 2026. The announcement reported more than 12,000 contributors from more than 2,800 companies. Those figures describe project participation; they are not a measure of AI observability quality or a guarantee about a particular deployment. Graduation is evidence of project maturity, not proof that every GenAI convention is settled or that one architecture fits every workload.
A June 2026 OpenTelemetry post still described GenAI semantic conventions as emerging. Teams should therefore distinguish the mature OTel foundation from the changing state of AI-specific conventions and validate the conventions and instrumentation they intend to use.
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