To make an AI agent observable, trace the full run—not just its model calls—and connect operational signals such as latency and errors with evaluations of task quality and safety. Use OpenTelemetry GenAI semantic conventions as a portability baseline, and make prompt, response, retrieval, and tool-content capture an explicit data-governance choice.
What should agent observability explain?
When an agent succeeds, fails, or changes behavior, an operator needs to see how the run unfolded: the request, orchestration, model calls, retrieval, tool invocations, downstream services, and relevant policy checks. A trace of one model call cannot show whether a bad answer came from retrieval, a tool result, orchestration, or the model itself.
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Set a run boundary around a user request or workflow execution, then link its component operations so an investigator can follow the execution path. Preserve available request or conversation context where it helps connect the steps. Do not invent identifiers when the runtime does not provide them.
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Different telemetry signals answer different questions. Google Cloud’s official guidance describes logs as event and error detail, metrics as latency and token signals, and traces as execution paths. Traces can also provide the context for understanding model-call counts and token totals.
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| Signal | What it helps answer | Useful examples |
|---|---|---|
| Traces | Where did this run spend time, branch, or fail? | Orchestration, model, retrieval, tool, and downstream-service steps linked within a run |
| Metrics | Is the system’s operational behavior changing? | End-to-end and step-level latency, errors, request or tool volume, and token usage |
| Logs and events | What happened at a particular step? | Errors and selected event details, subject to the team’s content-capture policy |
| Evaluations | Did the agent do the task acceptably? | Task success, groundedness or factuality, safety, and appropriate tool use |
A trace describes execution, not whether the result was good. Keep quality evaluations alongside operational telemetry rather than treating a successful request or low error rate as proof of a successful task.
How can teams make agent telemetry portable?
Use OpenTelemetry GenAI semantic conventions where they fit the operation. The conventions describe shared attributes for providers and models, token usage, retrieval data sources, evaluations, tools, and operation names. Consistent semantics make telemetry easier to interpret across components and implementations.
Do not mistake this baseline for a complete, settled agent standard. OpenTelemetry’s overview describes agent and framework conventions as actively developing and calls for continued interoperability work. Framework-specific extensions may still be necessary, and coverage depends on the libraries and runtime components in use.
- Record which convention version and local extensions your instrumentation uses.
- Document any local attribute meanings so dashboards and queries remain interpretable.
- Verify coverage for the actual framework, provider, retrieval path, tools, and services in your workload.
- Keep the execution relationships clear even when some components require framework-specific instrumentation.
Which operational and quality signals belong together?
Monitor the run as a whole and its important steps. End-to-end latency can reveal a slow workflow; step-level latency helps locate the delay. Track errors, request and tool volume, and token usage to spot operational changes and understand resource behavior.
Pair those indicators with task-level evaluation. Depending on the agent’s job, assess success, groundedness or factuality, safety, and whether tools were selected and used appropriately. Microsoft Learn puts the limitation plainly: “Uptime and error rates are not good indicators of quality and reliability in AI systems.”
Build a set of representative regression cases and evaluate them when prompts, models, retrieval, tools, or policies change. Define normal behavior for both operational and quality signals, then investigate deviations in the context of those changes. Microsoft’s guidance recommends ongoing quality and safety evaluation and behavioral baselines; Google Cloud documents prompt and response data as inputs to evaluation.
What data should telemetry capture?
Prompts and execution content can contain personal or confidential information. That includes user inputs and model outputs, system instructions, retrieval query text, and tool arguments or results. OpenTelemetry’s GenAI guidance warns about these risks; its span guidance says full buffered content is often sensitive and large, and that instrumentation should not capture it by default while allowing an opt-in.
Before enabling content capture, establish a data contract that states 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 calls out data minimization, residency, legal obligations, access control, and encryption as considerations to balance against forensic needs.
- Start with the metadata needed to follow and measure a run; opt in to content only when there is a defined need.
- Choose which fields may be captured, and apply filtering or truncation where appropriate.
- Restrict access to sensitive telemetry and define retention and deletion periods.
- Review the collection policy as the workflow, data sources, and legal or operational requirements change.
How should a team implement observability?
- Map one representative execution. List the request boundary, orchestrator, model calls, retrieval operations, tools, downstream services, and policy checks. Decide which request or conversation context is already available to connect the steps.
- Instrument the path. Emit linked traces for the run and its component operations. Use applicable OpenTelemetry GenAI conventions, document local extensions, and check that instrumentation covers every component in the mapped workflow.
- Establish operational dashboards. Add end-to-end and step-level latency, errors, request and tool volume, and token usage. Include trace and event detail that helps explain anomalies without collecting sensitive content by default.
- Add quality evaluation. Choose task-appropriate success, groundedness or factuality, safety, and tool-use checks. Keep representative regression cases for changes to prompts, models, retrieval, tools, and policy.
- Set baselines and investigate changes. Define expected ranges for operational and quality behavior. When a signal departs from its baseline, follow the trace and correlate the change with model, prompt, retrieval, tool, or policy changes.
- Review collection and operations. Confirm the data contract, access, retention, and deletion rules. Assign ownership for dashboards, instrumentation, evaluation, reliability, and ongoing cost.
How should teams compare observability implementations?
Evaluate candidates against a representative workload rather than assuming one platform is best for every agent. Official documentation describes, for example, AWS OpenSearch AI observability with OpenTelemetry integration and framework instrumentation, and Google Cloud Application Monitoring using OpenTelemetry GenAI trace data. These are implementation examples, not an independent head-to-head ranking.
| Decision area | Questions to test |
|---|---|
| Coverage | Does it instrument the model and provider, agent framework, retrieval, tools, and downstream services actually used? |
| Trace usefulness | Can an operator follow parent-child execution relationships and investigate a complete run? |
| Portability | Does it use OpenTelemetry conventions and provide export options? Are vendor-specific extensions documented? |
| Evaluation | Can the team assess task quality and safety, run regressions, and act on evaluation changes? |
| Data controls | Can the team control content capture, filtering or truncation, access, residency, encryption, and retention? |
| Operations | Are search, aggregation, dashboards, scale, reliability, ownership, and total cost workable for the team? |
Run the same representative workflow through each candidate and check whether its traces include orchestration and tool steps—not only model calls. Then test whether the team can find a run, explain an anomaly, inspect quality signals, and enforce its data policy in the normal investigation workflow.
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