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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallTo monitor an AI agent effectively, trace the full task from start to finish—not just its final answer. Record linked model, tool, retrieval, and application steps; measure usage, latency, errors, and cost; then evaluate whether the result actually met the task. Traces explain individual runs, while dashboards, alerts, and quality checks help reveal recurring problems.
What to capture in an agent run
Treat each user task as a root trace, with meaningful actions represented as related operations or spans. A useful trace shows what happened and in what order, so you can connect a bad result or delay to the step that caused it.
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- Model activity: the model or version, prompt and response where policy permits, token usage, latency, and errors.
- Tool and retrieval activity: calls made, relevant inputs and outputs, timing, failures, and retries.
- Application steps: decisions or other workflow operations needed to understand how the run proceeded.
- Correlation metadata: identifiers or labels that connect events to a session, deployment, agent or workflow version, environment, and task type.
- Outcome: whether the run completed, timed out, or failed, plus any available quality score or user feedback.
Langfuse describes application tracing as structured records of requests that can capture the prompt, model response, token use, latency, and intervening tool or retrieval steps in its observability and application tracing documentation.
Minimize sensitive data
Prompts, outputs, tool arguments, and metadata can contain personal or confidential information. Decide what to redact, retain, or exclude before capturing payloads, and check the monitoring service’s data-handling and deployment terms. There is no universal retention or redaction policy established by the sources cited here; these controls depend on your implementation and provider.
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Measure operational health and cost
Track operational signals per run where available, then group them by dimensions that help explain variation—such as workflow version, model, tool, environment, and task type. Useful measures include:
- Token usage and cost per run, alongside total cost and cost per completed task.
- Latency distributions, not only averages.
- Tool and model errors, retries, timeouts, and completion status.
- Quality scores or feedback, analyzed separately from technical success.
LangSmith lists token usage, latency percentiles, error rates, cost breakdowns, and feedback scores among the metrics available in its observability documentation. Which signals you can collect and how they are calculated depends on your instrumentation and configuration.
Evaluate whether the agent did the right thing
A run that completes without an exception may still answer incorrectly, choose an unsuitable tool, or miss the user’s intended task. Define acceptance criteria for each workflow and assess traces against them. Depending on the task, evaluation can use deterministic checks, human review, calibrated model-based evaluators, or user feedback.
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An OpenAI Developers Cookbook recipe published March 31, 2025 demonstrates connecting agent traces with evaluation and user feedback using Langfuse. The recipe is marked archived, so use it as an example of the evaluation approach, not as current setup instructions: Evaluating Agents with Langfuse.
Use traces to investigate and alerts to find patterns
A trace helps explain an individual run; aggregate views help identify whether a problem is recurring or growing. Set alerts for changes that warrant investigation, such as rising errors, latency, or cost per task, or declining quality scores. When an alert fires, inspect representative traces, locate the affected step, and check what changed. Validate a proposed fix against a replay or evaluation set before rolling it out broadly.
Langfuse and LangSmith document dashboards and alerting among their observability capabilities: Langfuse documentation and LangSmith observability. Choose thresholds based on the workflow’s normal behavior and the cost of missing or over-alerting on a problem.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose an instrumentation and monitoring approach
OpenTelemetry can be relevant if you want instrumentation that connects with an existing telemetry pipeline. Its documentation covers Generative AI semantic conventions, and LangSmith documents OpenTelemetry integration. Before relying on a convention or integration, confirm its current status, framework support, and which attributes your instrumentation actually emits.
Langfuse and LangSmith are examples to assess, not a complete product ranking. Their vendor documentation describes their own capabilities; it does not establish independent superiority or benchmark performance.
Best Value
| Approach or example | What to assess | Checks before choosing |
|---|---|---|
| OpenTelemetry-based instrumentation | Potential portability and connection to existing telemetry pipelines; OpenTelemetry maintains GenAI semantic conventions, and LangSmith documents OTel integration. | Verify framework instrumentation, emitted attributes, and current convention status. The cited OpenTelemetry page does not establish specific field coverage or maturity for your setup. |
| Langfuse | Its documentation describes trace capture, cost and usage tracking, quality scores, dashboards, and threshold alerts. | Confirm integration coverage, hosting and data controls, retention, and plan limits for the deployment you intend to use. |
| LangSmith | Its documentation describes tracing, cost and latency monitoring, error rates, feedback scores, alerts, and OpenTelemetry integration; the vendor says it works with multiple frameworks. | Check current data residency, deployment, pricing, and data-handling terms directly with the vendor. |
Compare options against your requirements
Use the same criteria for each candidate so a polished trace viewer does not distract from missing instrumentation, evaluation, or data controls.
- Instrumentation fit: supported frameworks, custom instrumentation, and interoperability with OpenTelemetry.
- Trace usefulness: visibility into nested model and tool steps, searchable payloads, and correlation across sessions or agents.
- Operational monitoring: token and cost attribution, latency distributions, error rates, dashboards, and alerts.
- Quality measurement: custom evaluations, human feedback, online scores, and regression testing.
- Data controls: hosted or self-managed options, region and residency, redaction, access controls, retention, and export.
- Economics: trace-volume limits, evaluation costs, hosting effort, and current plan pricing.
The cited sources do not provide a neutral, comparable price table or a complete independent benchmark. Check current primary vendor terms before comparing cost or performance.
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