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What each signal tells you
| Signal | Answers | Best used for |
|---|---|---|
| Traces | What steps made up this run, and how were they connected? | Reconstructing one slow, failed, or unexpected workflow |
| Logs | What happened at this particular event? | Searching event details, tool results, errors, or application decisions |
| Metrics | How often, how much, and how is behavior changing? | Monitoring trends, service health, and alert conditions |
How traces show an agent’s execution path
A trace represents the execution structure for one workflow or turn. It groups spans—individual operations with start and end times and parent-child relationships—so you can see how work progressed rather than treating the agent as one opaque model request.
Useful agent spans can represent model generations, tool execution, guardrail checks, handoffs, and custom events. Inspect a trace when a run took too long, called an unexpected tool, failed after delegation, or followed a surprising sequence of actions. OpenAI’s Agents SDK tracing guide describes these trace events; its API guide explains how to view a turn’s steps, inputs, outputs, duration, and status.
How logs explain individual events
Logs preserve details about a particular event, such as a tool outcome, error, or application decision. Structured, searchable fields make them useful when you need to find a specific failure or inspect what a step returned.
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Where possible, correlate log entries with trace and span identifiers. That lets an engineer move from the execution path to the event detail without losing context. Microsoft’s Agent Framework observability documentation describes logs as one of the telemetry signals emitted through its OpenTelemetry instrumentation; it does not establish a universal log schema for all agents.
How metrics reveal patterns across runs
Metrics aggregate measurements over requests and time. Latency, error rates, token or usage counts, and cost can help surface regressions or changes worth alerting on. LangSmith describes monitoring model-performance measures such as cost and latency in its monitoring documentation; Microsoft also documents metrics alongside traces and logs.
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A metric is not a quality guarantee: low latency or a low error rate does not prove that an agent’s answer or action was correct. Metrics identify patterns; traces and logs help investigate what happened in particular runs.
How to investigate an agent problem with all three
- Start with the metric or alert. Identify what changed, such as tool failures or latency, and narrow the affected time period or workflow if your monitoring allows it.
- Open an affected trace. Follow the model calls, tool invocations, guardrail checks, and handoffs to locate the relevant step.
- Inspect the span and its logs. Check timing and status alongside event-level details such as the tool outcome or error.
- Compare other affected runs. Use the aggregate metric to see whether the issue appears isolated or is recurring.
This workflow is an implementation approach, not a guarantee that every product links signals automatically. Check whether the tools you choose correlate metrics, traces, and logs through shared request, trace, or span identifiers.
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What to evaluate in an observability implementation
- Agent-step coverage: Does it capture model calls, tool invocations, handoffs, guardrails, and custom application events?
- Interoperability: Can telemetry use OpenTelemetry conventions and reach your existing storage and dashboards?
- Diagnostic depth: Can engineers inspect relevant inputs, outputs, timing, status, and parent-child context?
- Operational monitoring: Are traces complemented by useful metrics, such as latency, errors, and cost?
- Data governance: What content is recorded, who can access it, how long is it retained, and can collection be disabled or data exported?
- Integration effort: Does the implementation support your framework and providers, and what instrumentation or backend work will your team need?
Vendor documentation describes different implementation approaches, not a tested ranking. The OpenAI Agents SDK has built-in agent tracing. Microsoft documents an OpenTelemetry-based path that emits traces, logs, and metrics. LangSmith presents monitoring and framework integrations, while AWS describes OpenTelemetry-integrated AI observability in OpenSearch. Evaluate these against your workflow, operating requirements, and data policies rather than treating the documentation as a comparative performance test.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check privacy and retention before enabling capture
Depending on instrumentation and configuration, agent traces may include prompts, model outputs, tool inputs, and other sensitive workflow context. OpenAI’s Agents SDK tracing documentation describes a sensitive-data capture setting and says tracing is unavailable for organizations using OpenAI APIs under a Zero Data Retention policy.
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Before turning on collection, verify the selected SDK and backend’s current capture defaults, retention period, access controls, export options, and disabling controls. Apply the same review to logs, since event details can also contain sensitive information.
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