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To monitor an AI agent well, trace the whole task—not just its final answer. Record how model calls, tool use, retrieval, and application steps connect; measure cost, latency, and errors; and evaluate whether the result actually met the task. Traces help explain individual runs, while dashboards and alerts reveal patterns that need attention.
What to capture in an agent run
Represent each user task as a root trace, with meaningful actions recorded as linked operations or spans. This preserves the order and relationships among the agent’s decisions, model calls, tool executions, retrieval, and application logic. Without that structure, it is harder to tell what preceded a failure or which step drove cost or delay.
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Langfuse describes application tracing as structured logs that capture the prompt, model response, token usage, latency, and intervening tool or retrieval steps. See its observability and application tracing overview.
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- Inputs and outputs: prompts, model responses, and tool arguments or results where policy permits.
- Usage and timing: token use, cost when available, and latency for the run and its component steps.
- Failures: error details, retries, timeouts, and completion status.
- Correlation metadata: identifiers or labels that connect events to a session, agent or workflow version, model, tool, and environment.
Payloads and metadata can contain sensitive or personal information. Decide what to redact, retain, or exclude before collecting them, and check the selected monitoring service’s data-handling and deployment terms. There is no universal retention or redaction policy established by the sources cited here.
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How to monitor operational behavior
Use dashboards to track operational health over time, not just to inspect a single run. Where useful, segment results by agent or workflow version, model, tool, environment, and task type. Watch cost per completed task as well as total cost; an overall average can obscure a costly workflow or a change in task mix. Examine latency distributions and error rates alongside token usage and retries.
LangSmith lists token usage, latency percentiles, error rates, cost breakdowns, and feedback scores among its dashboard metrics in its observability documentation. Treat vendor feature descriptions as descriptions of stated capabilities, not as independent performance comparisons.
How to tell whether the agent did the job
Operational success is not the same as task success. A run can finish without an exception and still give the wrong answer, call the wrong tool, or miss the user’s goal. Define acceptance criteria for the task and evaluate traces against them using deterministic checks, human review, or appropriately calibrated model-based evaluators. Record user feedback when it is available.
An OpenAI Cookbook example on evaluating agents with Langfuse, published March 31, 2025, demonstrates connecting agent traces with evaluation and user feedback. The recipe is marked archived, so use it as an example of the approach rather than as current setup instructions.
How to investigate problems and respond
Set alerts for changes that warrant action, such as a sudden rise in errors, latency, or cost per task, or a decline in a quality score. An alert is a signal to investigate, not proof of a cause.
- Inspect representative traces. Follow the linked steps to find where behavior changed or where time and cost accumulated.
- Look for a relevant change. Compare the affected runs by workflow version, model, tool, environment, or task type.
- Validate a proposed fix. Check it against a replay or evaluation set before rolling it out broadly.
Langfuse and LangSmith document dashboards and alerting capabilities in their respective Langfuse overview and LangSmith observability page.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choosing an instrumentation and monitoring approach
There is no single best choice for every stack. Compare options against the systems you already use and the evidence you need to act on:
- Instrumentation fit: Check support for your frameworks, custom instrumentation, and interoperability with existing telemetry.
- Trace usefulness: Confirm that you can inspect nested model and tool steps, search traces, and correlate activity across sessions or agents.
- Operational monitoring: Look for token and cost attribution, latency distributions, error rates, dashboards, and alerts.
- Quality measurement: Check for custom evaluations, human feedback, online scores, and regression testing.
- Data controls: Verify hosted or self-managed deployment options, region and residency, redaction, access controls, retention, and export.
- Economics: Account for trace-volume limits, evaluation costs, hosting effort, and current plan pricing.
OpenTelemetry maintains Generative AI semantic conventions, and LangSmith documents OpenTelemetry integration. If portability matters, confirm which frameworks are instrumented, which attributes your setup emits, and the current status of the conventions you plan to use. Do not assume that adopting OpenTelemetry alone guarantees a complete agent trace.
Best Value
Langfuse documents trace capture, cost and usage tracking, quality scores, dashboards, and threshold alerts. LangSmith documents tracing, cost and latency monitoring, error rates, feedback scores, alerts, and OpenTelemetry integration, and says it works with multiple frameworks. Before choosing either, verify current integration coverage, deployment and data controls, retention, and plan limits against the terms for the deployment you need.
These examples illustrate feature categories, not a complete product ranking. The cited sources do not provide a neutral, comparable price table or independent benchmark, so pricing and performance should be checked against current primary sources rather than inferred from feature pages.
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