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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Tracing is part of AI agent observability, not a substitute for it. A trace connects the steps in one run and shows where time or errors accumulate. Observability combines traces with logs, metrics, agent-specific context, and evaluations so a team can assess both system health and whether the agent behaved well.
What is the difference between tracing and observability?
| Signal | What it helps answer | Example for an AI agent |
|---|---|---|
| Traces | What happened during this run, in what order, and where did time or an error enter? | A request flows through orchestration, a model call, retrieval, and a tool invocation. |
| Logs | What event or error was recorded? | A tool returned an error, or an access decision was made. |
| Metrics | How often, how long, or how much? | Request volume, latency, error rates, tool-call volume, and token usage. |
| Evaluations | Was the agent’s output useful, correct, or aligned with the required behavior? | An evaluation result checks output quality or a policy decision against a defined criterion. |
Observability is the broader practice of using these signals together. A trace can reveal the execution path, but it does not by itself show aggregate trends or establish that a response was good. OpenTelemetry describes telemetry as useful for troubleshooting and, for non-deterministic agents, as an input to evaluation and improvement workflows.
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What should teams monitor in an agent run?
Identity and run context
Capture timestamps and the request or conversation context that already exists, including run identifiers where available. OpenTelemetry’s GenAI conventions caution against inventing a conversation ID by repurposing a UUID, trace ID, or content hash when the system has no conversation identifier.
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Execution path
Instrument the meaningful steps in the workflow: agent and workflow invocations, planning, model operations, tool executions, memory actions, and retrieval. A useful trace makes relationships between those steps visible; multi-agent examples can use parent and child spans, though the actual nesting depends on the framework and its instrumentation.
Performance, reliability, and usage
- Latency and duration: measure the whole run and relevant steps so slow dependencies can be distinguished from slow model calls.
- Reliability: track request volume, error rates, and error types, including failures from tools and other dependencies.
- Agent activity: monitor tool-call volume and model-call counts where trace data supports them.
- Model usage: record model identity when available and token consumption. Token usage is a useful usage signal; a monetary cost figure depends on how the selected backend calculates it and should not be assumed to be universal.
Quality, safety, and dependencies
Track evaluation results and relevant policy decisions alongside technical health. Where justified, preserve enough prompt and response context to investigate an outcome, and capture retrieval provenance, tool arguments and results, permissions, and inter-service dependencies only under an appropriate data policy. Uptime and low error rates cannot establish that an agent’s answer was correct, useful, or safe.
How to build a useful monitoring baseline
- Map one representative workflow. Identify the agent, model, retrieval, memory, and tool steps that make up a run. The trace should make the execution path and dependencies understandable, rather than merely show that a request completed.
- Correlate signals. Use consistent run context and span attributes so traces, logs, metrics, and evaluation results can be connected. This makes it possible to move from an aggregate symptom, such as rising latency, to the individual runs and steps associated with it.
- Set technical baselines. Establish the measures relevant to the service, such as run duration, error rates and types, request volume, tool activity, and token usage. Interpret them against the workflow and its dependencies rather than treating a single number as a complete health verdict.
- Define behavioral evaluations. Specify what acceptable output or behavior means for the agent’s task, then record evaluation outcomes alongside run context. Technical monitoring and quality evaluation answer different questions and should be read together.
- Review instrumentation and data controls. Confirm which operations are covered, which attributes are emitted, what is sampled or retained, who can access it, and how sensitive fields are handled. Revisit these choices as frameworks and telemetry conventions evolve.
How to compare agent observability options
Whether evaluating a platform or assembling components, compare capabilities against the same workflow and data requirements. Product documentation describes examples, not a comparative test or an endorsement.
Rank #2
- Trajectory coverage: Can it represent the full agent path, including tools, retrieval, and orchestration, rather than only model calls?
- Interoperability: Does it use OpenTelemetry GenAI conventions or support export to other backends? Consistent conventions can reduce dependence on a vendor- or framework-specific format.
- Signal correlation: Can teams connect traces with logs, metrics, and evaluation runs?
- Instrumentation maintenance: Is instrumentation built into the framework or added externally? Built-in options can be easier to adopt, while potentially increasing framework bloat or version coupling. External instrumentation is another approach.
- Governance controls: Are sampling, retention, access, redaction, and data-residency needs supported for the team’s use case?
Documented examples illustrate different approaches: Google Cloud describes dashboards, topology maps, trace-derived metrics, and prompt/response evaluation; AWS documents hierarchical traces across orchestration, model calls, tools, and retrieval in Amazon OpenSearch Service; Microsoft Foundry documents tracing in its portal and Azure Monitor Application Insights, including multi-agent span examples. These are feature descriptions from their respective providers, not evidence that one option is better for a particular team.
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Why OpenTelemetry conventions matter—and what to verify
OpenTelemetry’s GenAI work aims to make AI telemetry more consistent and less dependent on vendor- or framework-specific formats. Google Cloud describes the conventions as a basis for interoperable agent traces across observability backends. Microsoft notes that the GenAI semantic conventions have Development status and may change, so teams should verify which convention version their instrumentation library implements and account for schema changes over time.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Protect trace data as sensitive operational data
Agent traces may contain prompts, generated responses, tool arguments, and other details that expose personal information, secrets, or credentials. OpenTelemetry’s GenAI convention reference warns that input-message attributes are likely to contain sensitive information. Treat trace data with controls comparable to logs and metrics.
Quick Recap
- Redact personal data, secrets, and credentials from prompts, arguments, and span attributes where possible.
- Minimize collection to what is needed for troubleshooting, evaluation, and operational decisions.
- Set explicit rules for retention, access, sampling, and data residency, taking forensic needs and legal obligations into account.
- Review whether retained prompt, response, and tool data is necessary for the intended use, and ensure the policy applies across connected observability systems.
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