AI workflows cross application services, data pipelines, retrieval, model providers, tools, queues, and GPU infrastructure. Monitoring each component separately makes incidents difficult to reconstruct.
OpenTelemetry provides vendor-neutral APIs, SDKs, and collection for traces, metrics, and logs. The design work is choosing signals that explain user outcomes without exposing sensitive content or creating unbounded cardinality.
Trace the user-visible task
Start one trace at the workflow boundary and propagate context through retrieval, model, tool, database, queue, and review operations. Use spans for material latency and decision points.
Define service indicators from the user
Measure completion, latency, correct outcome, escalation, and availability of the full journey. Component health supports diagnosis but does not replace workflow reliability.
Control sensitive and high-cardinality data
Avoid raw prompts and documents by default. Use approved content capture with redaction, retention, and access. Limit unique attributes such as user IDs, document IDs, and full model outputs.
Connect telemetry to evaluation and cost
Sample failed, expensive, corrected, and long-running traces into review. Record behavior versions and estimate cost per task. Convert confirmed incidents into regressions.
Design a shared semantic model
Use traces to connect application requests, retrieval, model calls, tools, queues, databases, and human review. Define stable attributes for workflow, tenant class, model and prompt version, retrieval version, tool, policy result, token use, and outcome. Avoid uncontrolled high-cardinality or sensitive content in standard telemetry.
Metrics should derive from the same event model where possible: completion, escalation, error, latency, token, cost, and accepted outcome by workflow and version. Logs add diagnostic detail but should preserve correlation identifiers.
Sampling must protect rare failures. Keep complete traces for incidents, policy denials, high-consequence actions, and selected evaluation traffic while sampling routine success according to cost and privacy.
Connect telemetry to operating decisions
Create service objectives around user-visible completion and latency, then supporting indicators for model and dependency behavior. Alert when an owner can take action. Token spikes or model errors without workflow context generate noise.
Control access, retention, and redaction for prompts, retrieved data, and tool results. Observability should not become an uncontrolled copy of sensitive business information. Test the telemetry path as part of security and deletion requirements.
Executive decision record
The decision is which shared telemetry semantics let teams explain and operate workflows across models, retrieval, tools, queues, and human review. Write that decision before selecting a model, vendor, framework, or implementation pattern. A written boundary keeps technical exploration connected to the operating outcome and makes it possible to explain why the organization advanced, revised, or stopped the work.
Approval should depend on correlated traces, stable attributes, privacy and sampling controls, workflow objectives, actionable alerts, and incident reconstruction. The evidence does not need to remove every uncertainty, but it should address the uncertainty capable of changing value, architecture, risk, or ownership. Record the baseline, assumptions, unresolved questions, and the person accepting the next stage.
Failure boundary and operating ownership
The central failure to guard against is collecting provider-specific metrics and sensitive raw content without the outcome and version context needed for action. Treat that condition as a testable scenario. Define how the system detects it, what users experience, which action is prevented or reversed, and what evidence reaches the person responsible for recovery.
Long-term accountability sits with the observability platform team for standards and the application team for instrumentation quality, objectives, and response. Supporting specialists can provide platforms, research, review, or delivery capacity, but they cannot substitute for an owner who controls policy and operating change. Name that owner before production and include the ownership path in release evidence and incident procedure.
A practical 90-day application plan
During the first 30 days, convert correlated traces, stable attributes, privacy and sampling controls, workflow objectives, actionable alerts, and incident reconstruction into a bounded evidence plan. Assign each artifact to a named contributor, identify the representative inputs required, and agree on the comparison baseline before implementation expands. The objective of this period is to expose the assumption most likely to invalidate the work while the cost of changing direction is still low.
During days 31 through 60, build or instrument the smallest complete workflow that can support the decision about which shared telemetry semantics let teams explain and operate workflows across models, retrieval, tools, queues, and human review. Include the real data and authorization path where feasible, record exceptions, and review difficult cases with the people who own the underlying process. Resist adding breadth until the team can explain the measured behavior of this narrow slice.
During days 61 through 90, test the boundary represented by collecting provider-specific metrics and sensitive raw content without the outcome and version context needed for action. Exercise degraded dependencies, ambiguous inputs, recovery, and handoff rather than demonstrating only successful cases. End the period with a written advance, revise, or stop decision that cites evidence, residual exposure, expected operating cost, and the next authority boundary.
The review should be accepted by the observability platform team for standards and the application team for instrumentation quality, objectives, and response. That group should confirm not only that the system can work, but that ownership, support capacity, monitoring, and change control are credible. If those conditions are absent, the responsible outcome is another bounded learning stage rather than an unsupported production commitment.
Practical checklist
- Workflow-level trace context
- Application, data, model, tool, and GPU spans
- User-centered service indicators
- Sensitive-content policy
- Metric cardinality controls
- Cost and behavior version attributes
- Incident-to-evaluation feedback
Continue reading
- [AI agent observability](/ai-agent-observability-production)
- [MLOps versus LLMOps](/mlops-vs-llmops-production-systems)
Sources and further reading
- [What is OpenTelemetry?](https://opentelemetry.io/docs/what-is-opentelemetry/)
- [OpenTelemetry observability primer](https://opentelemetry.io/docs/concepts/observability-primer/)
- [OpenTelemetry metrics](https://opentelemetry.io/docs/concepts/signals/metrics/)