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To improve an AI agent in production, trace each run from start to finish, inspect the decisions and failures behind its outcome, and turn representative cases into repeatable evaluations. Compare changes on the same test cases, while tracking task quality alongside latency, errors, token use or cost, and user feedback. Logs alone rarely show why an agent failed; a useful operating loop connects what happened, what it should have done, and whether a change made it better.
What should you monitor in an AI agent run?
Instrument the workflow so an engineer can reconstruct the important parts of a run, not just see the final answer. A trace should capture the sequence of model generations, tool calls, handoffs, guardrail events, and relevant custom events, along with timing and status. OpenAI’s Agents SDK documentation describes these event types; OpenAI’s tracing guidance also describes recording turn timelines, inputs and outputs, duration, status, and token usage.
Capture enough context to answer operational questions without collecting data indiscriminately. For each event, useful fields may include the event type, timestamp, outcome, duration, and an appropriately protected representation of inputs and outputs. Connect related steps under a run or trace identifier so a tool error or delay can be traced back to the decision that led to it.
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A run can have no infrastructure error and still fail the task. Conversely, an agent may produce an acceptable answer after a slow tool call or an unnecessary retry. Pair task-specific quality checks with operational signals such as error rate, latency, token usage or cost, and user feedback. LangSmith describes dashboards for token usage, latency, error rate, cost, and feedback scores; Datadog describes correlating agent behavior with quality, security, and cost measures. These are vendor-described capabilities, not evidence that any one metric predicts success by itself.
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Define what success means for the particular workflow. For a support agent, it might mean resolving the request correctly and following escalation policy; for a research agent, it might include using appropriate sources and accurately conveying uncertainty. A generic score such as response length or user engagement cannot substitute for task-specific criteria.
How do you use traces to find failures?
Review traces against the intended workflow, not just the final text. OpenAI’s agent-evaluation guidance suggests questions that make useful review criteria:
- Did the agent pick the right tool for the task?
- Did it hand off work when it should have, or continue when a handoff was appropriate?
- Did the workflow violate an instruction or safety policy?
- Did a prompt or routing change improve the end-to-end behavior?
These questions help distinguish different failure modes that can look alike in a final response. A wrong answer might result from a poor model generation, an incorrect tool choice, incomplete tool output, a missed handoff, or an instruction-following failure. The trace gives reviewers evidence about where the workflow went wrong and what change might address it.
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Classify problems before changing the system
For each reviewed run, record the observed failure and the expected behavior in concrete terms. Useful categories might include incorrect tool selection, tool failure, unnecessary loop or retry, missed escalation, instruction or policy violation, incomplete task, and unacceptable latency. Treat these as working labels for your application rather than universal categories: the distinctions that matter depend on the agent’s job.
Separate a defect in agent behavior from a defect in the surrounding service where possible. For example, a tool returning an error is different from the agent choosing a tool that cannot answer the request. Both may need attention, but they point to different fixes and different evaluation checks.
How do you turn production failures into repeatable evaluations?
Build a small, representative evaluation set from real incidents and reviewed edge cases. Each example should include the relevant input or scenario, the expected behavior, and a way to judge whether the result met the requirement. Protect or transform sensitive production data before using it in datasets, and retain only what your privacy and security policies permit.
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Use the evaluation method that fits the criterion. Phoenix documentation describes evaluation with LLM evaluators, code checks, and human labels. A deterministic rule may be appropriate for a required field or tool-call condition; human review may be needed for nuanced task quality; an LLM grader can help assess structured qualitative criteria, but its judgments should themselves be checked on representative examples.
Run a controlled comparison
- Establish a baseline. Run the current agent against the chosen dataset and save the results, including task scores and relevant operational measures.
- Make one controlled change. Change a prompt, model, tool interface, routing rule, or guardrail so you can attribute observed differences more clearly.
- Rerun the same cases. Compare the new results with the baseline on identical inputs. Look for improvements, regressions, and cases where results are mixed.
- Review failures, not only averages. Inspect individual traces for changes in tool choice, handoffs, policy adherence, completion, errors, latency, and token use or cost.
- Keep useful cases in the set. Add material failures and edge cases as they arise, so future changes are checked against known risks.
OpenAI’s evaluation guidance describes trace grading, datasets, and repeatable evaluation runs; Phoenix describes experiments on the same inputs. A score change is most useful when the underlying examples and grading criteria are stable enough to support comparison. If you revise the rubric, note that the new results may not be directly comparable with earlier scores.
How should you monitor changes after release?
Use the evaluation results to decide whether a change is ready for deployment, then apply rollout safeguards appropriate to the impact of the workflow. Continue sampling production traces after release: offline evaluations cannot guarantee that production traffic will match the cases in a dataset. Review new failures, monitor operational signals, and feed significant cases back into the evaluation set. This trace-to-dataset-to-evaluation loop is an operational practice, not a rollout method prescribed by any one vendor.
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Agree in advance on what would trigger investigation or rollback. Thresholds should reflect the agent’s task and risk: a small latency increase may be tolerable for a complex workflow, while a rise in policy violations or failed transactions may not be. Avoid treating a single blended score as the whole release decision; review the quality and operational trade-offs together.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which tools can support agent monitoring and evaluation?
The following are examples documented by their vendors, not rankings or endorsements. Capabilities, availability, terms, and deployment options can change; verify the current details for the product and plan you intend to use.
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| Option | Documented approach | Deployment or qualification |
|---|---|---|
| OpenAI agent tracing and evaluation | Agents SDK tracing includes generations, tool calls, handoffs, guardrails, and custom events. OpenAI evaluation guidance covers trace grading, datasets, and repeatable evaluation runs. | OpenAI states that Agents SDK tracing is unavailable to organizations using its APIs under Zero Data Retention (ZDR). |
| Arize Phoenix / Arize AX | Phoenix documentation describes OpenTelemetry-based traces, evaluations using LLM evaluators, code checks, or human labels, prompt iteration, and experiments on the same inputs. | Phoenix is described as open source, with Docker/Kubernetes or cloud self-hosting documented. Arize AX is described as the managed enterprise platform. |
| LangSmith | LangChain describes tracing and monitoring across frameworks, OpenTelemetry support, dashboards, and alerts. | The product page lists cloud, bring-your-own-cloud (BYOC), and self-hosted choices. Check current plan and contract terms. |
| Datadog LLM Observability | Datadog’s June 10, 2025 announcement describes a decision-path graph and investigation of latency spikes, incorrect tool calls, and loops, with quality, security, and cost measures. | The announcement described LLM Experiments as a preview on that date. Verify its current availability before relying on it. |
The cited product materials do not establish a neutral, current performance benchmark or a universal best platform. Compare candidates against your stack and operating requirements rather than choosing from feature lists alone.
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Questions to ask during selection
- Compatibility: Does the product support your agent framework, model providers, tools, and existing telemetry pipeline?
- Trace detail: Can it represent the events, handoffs, inputs, outputs, timing, and statuses needed to investigate your failure modes?
- Evaluation workflow: Can your team maintain datasets, apply the graders it needs, and compare runs on the same inputs?
- Portability: Can traces or evaluation results be exported, and does the product support OpenTelemetry where that matters to your architecture?
- Operations: Does it provide the dashboards, alerts, access controls, and expected-volume handling your team needs?
- Data handling: Where is data processed and stored? What retention, redaction, access-control, and residency terms apply to your deployment?
- Total cost: How do pricing and contract terms relate to expected trace volume, retention, evaluation use, and deployment model?
How should you handle sensitive trace data?
Traces may contain user prompts, model outputs, tool arguments, and other sensitive information. Decide what to collect, redact, restrict, and retain before enabling broad trace capture. Apply access controls and retention rules that fit your organization’s data policies, and confirm the product’s current terms for the actual region and deployment you plan to use.
Deployment choice can affect what controls are available. Phoenix documents self-hosting, while LangSmith lists cloud, BYOC, and self-hosted options. OpenAI’s Agents SDK documentation identifies the ZDR tracing limitation described above. These descriptions do not settle the retention or residency terms for every configuration; verify those terms directly with the provider and your internal security or privacy owners.
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