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To build useful observability for agentic AI, trace a complete run—not just individual model calls—and connect operational signals such as latency and errors with evaluations of task quality and safety. Use OpenTelemetry GenAI semantic conventions where they fit, document any framework-specific extensions, and decide explicitly which sensitive content may be collected.
What should an agent trace show?
A useful trace lets an engineer follow one user request through the agent or workflow, model calls, retrieval, tool invocations, policy checks, and relevant downstream services. That end-to-end path helps locate where behavior changed or failed; a model-call trace by itself may miss the orchestration or tool step that explains the result.
Start by identifying the run boundary and the execution steps your system actually performs. Link the spans across those steps so an operator can move from the run to its constituent operations. Preserve available request or conversation context, but do not invent identifiers that the runtime does not provide.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems| Execution stage | What to observe | Question it helps answer |
|---|---|---|
| Agent or workflow orchestration | Run-level trace and the sequence of operations | Where did the execution branch, stop, or change direction? |
| Model call | Provider, model, operation, latency, and available token-usage data | Which model operation was slow, failed, or used more tokens? |
| Retrieval | Retrieval operation and data-source context; capture query text only under an explicit content policy | Was relevant source data retrieved for this run? |
| Tool and downstream service | Tool invocation and service boundary, with timing and error context | Did a tool or dependency fail, return unexpected data, or dominate latency? |
| Policy and evaluation | Applicable policy checks and task-level evaluation results | Did the run meet quality and safety expectations? |
Google Cloud’s observability guidance distinguishes logs, metrics, and traces: logs provide event and error detail, metrics can show latency and token signals, and traces show execution paths. Trace data can also support deriving model-call counts and token totals. Treat these signals as complementary rather than expecting one to answer every debugging question.
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How can OpenTelemetry make instrumentation more portable?
Use OpenTelemetry GenAI semantic conventions as a shared vocabulary when they match the operation being instrumented. The conventions registry covers categories including provider and model, token usage, retrieval data sources, evaluation labels, tools, and operation names. Consistent semantics make telemetry easier to interpret across components and can reduce dependence on one backend’s naming scheme.
Do not assume the conventions describe every agent framework or are a finished standard. OpenTelemetry’s overview describes the agent and framework conventions as actively developing and calls for further interoperability work. For each deployed component, check the current convention and library coverage, record the convention version you use, and document local extensions where the shared fields do not fit. A locally invented field can be useful, but its meaning and scope should be clear to every team that queries it.
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Which signals reveal whether the agent is working well?
Pair operational health with task outcomes. Latency, errors, request and tool volume, and token usage can show whether the system is slow, failing, or changing its resource profile. They cannot establish whether the agent completed the task correctly or safely. Microsoft Learn puts the distinction plainly: “Uptime and error rates are not good indicators of quality and reliability in AI systems.”
Define evaluation signals that match the work the agent is expected to do. Depending on the task, these can include task success, groundedness or factuality, safety, and whether tool use was appropriate. Keep representative regression cases and evaluate changes to prompts, models, retrieval, tools, and policy against them. Google Cloud documents prompt and response data as inputs to evaluation; whether to capture that content should be decided through the data-governance policy, not assumed as a default.
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- Measure end-to-end and step-level latency so a slow run can be localized.
- Track errors and request or tool volume to identify failures and changes in usage.
- Monitor available token-usage signals as an operational indicator, not a proxy for answer quality.
- Evaluate quality and safety against defined criteria and regression cases.
How should teams protect prompts and execution content?
Telemetry can contain more sensitive information than its field names suggest. Inputs, outputs, system instructions, retrieval queries, and tool arguments or results may include personal or confidential data. OpenTelemetry’s GenAI guidance warns about this exposure; its span guidance says full buffered content is often both sensitive and large, and recommends that instrumentation not capture it by default while allowing opt-in capture.
Before enabling content collection, set a data contract that explains what is collected, why, who can access it, where it is stored, how long it is retained, and when it is deleted. Microsoft’s guidance also highlights data minimization, residency, legal obligations, access control, and encryption. Apply filtering or truncation where it meets the debugging need, and restrict access to content-bearing traces more tightly than to aggregate operational metrics when appropriate.
- Inventory each field that could carry user, business, or credential-like data.
- Choose whether to omit, redact, filter, or truncate content; avoid collecting full prompts and tool payloads by default.
- Limit access by role and document the purpose for any opt-in content capture.
- Set storage location, retention, deletion, and encryption requirements before turning collection on.
How should dashboards and alerts be set up?
Establish a baseline for both operational behavior and task quality, then alert on meaningful deviations. A useful investigation should let the on-call engineer connect an alert to the affected run and inspect relevant changes in model, prompt, retrieval, tool, or policy behavior. Microsoft’s guidance recommends ongoing quality and safety evaluation alongside behavioral baselines.
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Keep run-level views and aggregate views connected: a dashboard can reveal that a metric moved, while a trace helps explain what happened during a particular execution. Avoid treating a rise in token use or latency as proof of a quality regression; use the evaluation signal and relevant execution context to assess the effect.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you choose an observability implementation?
Compare implementations against a representative workload from your own agent, not a universal vendor ranking. Official documentation describes different service capabilities, but the available evidence does not establish an independent head-to-head benchmark. For example, AWS documents OpenSearch AI observability with OpenTelemetry integration and framework instrumentation, while Google Cloud documents Application Monitoring using OpenTelemetry GenAI trace data. These are implementation examples, not evidence that one platform is best for every workload.
Run the same representative execution through each candidate and check whether it captures the steps your team needs, supports the frameworks and model providers in use, enables practical investigation, and meets your data requirements. Include the operational work and cost of maintaining the system in the decision.
Quick Recap
| Decision area | What to verify |
|---|---|
| Coverage | Instrumentation for model providers, agent frameworks, tools, retrieval, and downstream services used by the workload |
| Trace usefulness | Clear parent-child execution relationships and a practical way to investigate a run end to end |
| Portability | Support for OpenTelemetry conventions and export options, plus documented vendor-specific extensions |
| Evaluation | Task-quality and safety scoring, regression workflows, and a way to act on evaluation results |
| Data controls | Opt-in content capture, filtering or truncation, access control, residency, encryption, and retention |
| Operations | Query and dashboard workflow, scale and reliability needs, ownership, maintenance burden, and total cost |
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