For AI agents, traces show the connected sequence of work in one run, logs preserve searchable details about individual events, and metrics summarize behavior across many runs. To debug a particular failure, follow a trace into the relevant span and its logs; to spot a broader regression or set an alert, use metrics. Linking the signals to the same request or workflow makes that investigation more useful.
What each signal tells you
| Signal | Best question to ask | What it shows for an agent |
|---|---|---|
| Trace | What happened during this run, and in what order? | The connected execution path, represented by spans for operations such as model generations, tool calls, handoffs, guardrail checks, and custom events. Spans can carry timing and parent-child context. OpenAI Agents SDK tracing documentation describes these kinds of trace events. |
| Log | What happened at this particular event? | Searchable event-level details, such as a tool result, error, or application decision. Logs are useful when you need diagnostic detail that is not apparent from the execution path alone. Microsoft documents logs as one telemetry signal emitted through its Agent Framework OpenTelemetry instrumentation; it does not define a universal agent log schema. |
| Metric | How often, how much, or how is behavior changing? | Aggregated measurements over requests and time, such as latency, error rates, token or usage counts, and cost. They help identify trends and alert-worthy changes, but do not by themselves establish that an answer or action was correct. LangSmith monitoring documentation describes monitoring measures including cost and latency. |
How traces make an agent run understandable
A trace is the execution map for a workflow or turn. It groups spans: operations with start and end times and relationships to parent operations. For an agent, a useful trace exposes the sequence around the model, tools, guardrails, and any handoffs rather than reducing the whole run to one opaque model request.
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Use a trace when a run was unexpectedly slow, selected the wrong tool, failed after delegation, or followed an unexpected sequence of actions. The Agents SDK tracing guide describes tracing for LLM generations, tool calls, handoffs, guardrails, and custom events. Its API guide to viewing traces describes inspecting a turn’s steps, inputs, outputs, duration, and status.
How logs and metrics complement traces
Logs preserve event details
A trace may show that a tool call happened and how it fits into the run; a corresponding log can make the tool outcome, error, or application decision searchable. Where possible, correlate log records with trace and span identifiers. That lets an engineer move from a step in the execution path to the detailed event record without guessing which event belongs to which run.
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Metrics show patterns across runs
Aggregated latency, errors, usage, or cost can reveal a change across many requests that would be difficult to notice by inspecting individual traces. Metrics are useful for trend analysis and alerting, but a favorable operational metric is not a quality guarantee: low latency or a low error rate does not prove the agent chose an appropriate action or gave a correct answer.
A practical way to investigate an agent issue
- Start with a metric or alert. Identify what changed, such as a rise in tool failures or latency. Use the metric to narrow the time window or affected service, not to infer a cause.
- Find an affected trace. Open a representative workflow or request from the affected period. Confirm that the trace covers the relevant agent steps.
- Follow the spans. Inspect order, duration, status, and parent-child relationships to locate the model generation, tool execution, guardrail, or handoff associated with the problem.
- Open associated logs. Use correlated trace or span identifiers to inspect the event-level detail, such as a tool outcome or error.
- Compare affected runs. Check whether the sequence or event details differ from runs that did not show the issue. Treat this as an investigation path, not proof that any particular backend links signals automatically.
This approach connects a broad symptom—such as increasing tool failures—to a particular workflow, tool call, error detail, and surrounding model or handoff context. Microsoft documents traces, logs, and metrics as telemetry signals in its Agent Framework OpenTelemetry integration; the investigation sequence above is a practical way to use those signal types together.
What to compare when choosing observability tooling
- Agent-step coverage: Check whether instrumentation captures model calls, tool invocations, handoffs, guardrails, and custom application events.
- Interoperability: Determine whether telemetry follows OpenTelemetry conventions and can reach your existing storage and dashboards.
- Diagnostic depth: Confirm that engineers can inspect relevant inputs, outputs, timing, status, and parent-child context.
- Operational monitoring: Make sure traces are complemented by metrics useful to your service, such as latency, errors, and cost.
- Data governance: Establish what content is collected, who can access it, how long it is retained, and how collection can be disabled or data exported.
- Integration effort: Check framework and provider support, along with the instrumentation and backend work your team must operate.
Vendor documentation illustrates different approaches, not an independent product ranking: the OpenAI Agents SDK documents built-in agent tracing; Microsoft documents an OpenTelemetry-based framework path that emits traces, logs, and metrics; LangSmith presents a platform with framework integrations and monitoring; and AWS describes OpenTelemetry-integrated AI observability in OpenSearch. These descriptions do not establish comparative performance.
Check sensitive-data handling before collecting traces
Depending on instrumentation and configuration, agent traces can include prompts, model outputs, tool inputs, and other sensitive workflow context. Before enabling collection, verify the selected SDK and backend’s capture defaults, retention period, access controls, export behavior, and options for disabling capture.
The Agents SDK documentation describes a sensitive-data capture setting. OpenAI also states that tracing is unavailable for organizations using its APIs under a Zero Data Retention policy; see its Zero Data Retention documentation. Check the current documentation for the configuration you use, since these controls and availability are product-specific.
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