Monitor an AI agent at four levels at once: service health, task outcomes, execution behavior, and quality and safety. Latency, uptime, and model errors tell you whether the service is running; they do not tell you whether the agent chose the right tool, completed the user’s job, followed instructions, or created an unsafe result. Join infrastructure metrics to end-to-end traces, structured evaluations, and business outcomes so an operator can move from an alert to the exact model call, tool call, handoff, or policy decision that caused it.
The four measurement layers
A production agent is a nondeterministic workflow rather than a single request/response function. A useful monitoring plan therefore keeps four questions separate while connecting their data. The table below shows what each layer should answer and which signals belong in it.
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| Layer | Question | Core measures | Useful drill-down |
|---|---|---|---|
| Service health | Is the system available and operating within its service objectives? | End-to-end latency, per-step latency, request volume, failure and error rate, resource usage, model usage | Workflow, model or version, and tool |
| Task outcomes | Did the user’s intended job actually get done? | Task completion or success rate, user feedback, business-specific outcome measures | Task type, customer segment, and outcome category |
| Execution behavior | Did the agent take an appropriate path? | Tool-call failure rate, run count by tool, handoffs, delegated work, sequence and trajectory | Individual run, span, tool, handoff, or delegated worker |
| Quality and safety | Was the result correct, relevant, compliant, and safe? | Correctness, relevance, instruction following, policy adherence, sensitive-output risk | Trace, criterion, policy, evaluator, and application context |
Service health
Track complete-run latency as well as the duration of each step. An agent can meet an endpoint latency target while spending most of its time in a slow retrieval call or repeatedly retrying a tool. Record request volume, failures, error classes, resource consumption, and model usage. Segment these signals by workflow, model and version, and tool; otherwise a healthy high-volume workflow can hide a failing low-volume one.
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Usage data should support operational decisions, not just billing. Token or other model-usage measures, tool execution time, and retry counts can reveal a cost or capacity problem before users report an outage. The reviewed guidance does not establish universal acceptable latency, error, or quality values. Set thresholds from your workload, risk tolerance, and service objectives.
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Task outcomes
Define what “success” means before collecting scores. A response can be generated without an error and still fail the user’s task: a scheduling agent might create no appointment, or a support agent might answer a different question. Measure completion or success at the task level, then add user feedback and business-specific outcomes that reflect the application.
Keep technical completion distinct from outcome success. For example, “run returned a response” is a service event; “the customer’s issue was resolved without a second contact” is an outcome. Dashboards should show both so operators do not optimize for fast, well-formed failures.
Execution behavior
Record which tools were selected, how often each was called, whether calls failed, and in what order calls occurred. A rising tool-call failure rate can indicate an expired credential, a schema change, or an agent choosing an unsuitable tool. Run counts by tool expose unexpected routing and costly loops. Handoffs and delegated work deserve their own measures: a handoff that should have happened but did not is a quality defect even when the final response looks plausible.
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Grade correctness, relevance, instruction following, policy adherence, and sensitive-output risks against criteria written for your application. In a multi-step workflow, evaluate the trajectory as well as the final answer: the agent may reach a superficially acceptable result after an unauthorized data access or an unsafe tool action. Automated graders make broad review practical, but their scores are measurement aids. Calibrate them against human judgments and revisit them when traffic, prompts, tools, or policies change.
Instrument a trace that explains one complete run
Use one end-to-end run (or conversational turn, if that is your product’s unit) as the diagnostic object. Preserve its relationship to every component step. A trace should let an operator identify whether a defect originated in the prompt, retrieval, model response, tool path, handoff, guardrail, or evaluation coverage.
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Minimum trace contents
- Run identifier, workflow or agent name, model and version, timestamps, and overall status.
- Inputs and outputs where policy permits, including the user request and the agent’s final response.
- Each model generation, with duration, status, and available usage data.
- Every tool call, including selected tool, arguments, result, duration, retries, and error status.
- Handoffs and delegated work, with parent-child relationships and the reason or route when available.
- Retrieval or other intermediate steps that materially influence the decision.
- Guardrail decisions, blocked actions, policy labels, and evaluator scores.
OpenAI’s tracing documentation describes this visibility as follows: “The tracing dashboard shows what your agent did, including each step’s recorded inputs, outputs, duration, and status.” That level of span detail is what allows a dashboard aggregate to lead to the exact failing step. OpenAI documents agent, generation, and tool spans, and traces can be exported as OTLP JSON when that option is enabled.
Keep parent and child timing consistent
Store a start and end time for the run and for each child span, using a common clock and stable identifiers. Report both wall-clock duration and step duration; parallel tool calls can make the sum of child durations larger than elapsed run time. Preserve retries rather than overwriting them, because repeated attempts often explain latency and cost spikes.
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Inputs, instructions, tool arguments, and outputs can contain personal, confidential, or regulated information. Decide what to capture, redact, encrypt, retain, and expose before enabling broad access. Apply role-based access to traces and separate operational metadata from sensitive payloads when feasible. Retention and redaction requirements depend on your data and jurisdiction; tracing documentation demonstrates capability but does not choose those policies for your organization.
Turn traces into repeatable evaluations
Write criteria for the decisions you care about
Start with representative traces and define structured labels or scores for the questions operators and product owners actually ask:
- Was the selected tool appropriate for the user’s request and available permissions?
- Did the agent hand off when another specialist or workflow was required?
- Were tool arguments valid, complete, and within authorization?
- Did the trajectory follow required instructions and safety policies?
- Was the final answer correct, relevant, and complete for the task?
Make labels observable. “Good answer” is too vague for a reliable evaluator; “contains the requested shipping date and cites the order record” is testable. Include an explicit failure category for “technically completed but task unsuccessful.”
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Build datasets and rerun them after changes
Once criteria are stable, create a dataset of representative inputs and expected properties. Run it whenever you change a prompt, model, router, retrieval index, tool schema, guardrail, or handoff policy. Compare both final-answer grades and trajectory grades. A release that improves wording while increasing unauthorized tool attempts is not an improvement.
OpenAI defines trace grading as “the process of assigning structured scores or labels to an agent’s trace—the end-to-end log of decisions, tool calls, and reasoning steps—to assess correctness, quality, or adherence to expectations.” Trace-level grading exposes workflow failures that a final-response score can miss.
Combine automated grading with human calibration
Automated evaluators can score a large sample consistently and cheaply, but they may misread domain-specific standards or drift as the application changes. Have people review a recurring set of traces, compare their judgments with evaluator results, and update prompts, rubrics, or labels when disagreement is systematic. Keep a holdout set that graders do not influence, so apparent improvement is not merely evaluator overfitting.
Sample production traffic consciously
LangChain’s production-monitoring guidance recommends sampling 10–20% of production traffic for LLM-based evaluation to manage cost. This is a vendor recommendation, not an independently measured industry benchmark. Evaluators can add a few seconds of latency per trace, may not reflect application-specific standards, and may need adjustment as traffic shifts. Use lower or higher rates according to risk, traffic volume, and evaluator cost, and retain periodic human review even when automated coverage is high.
Design alerts that lead to action
Alert on both service failures and application outcomes. A practical alert set includes:
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- Run or step error-rate increases, separated by error class.
- Latency degradation at the run and critical-tool level.
- Tool-call failure or retry spikes, segmented by tool and version.
- Task-success or completion-rate drops for important workflows.
- Safety-policy violations, sensitive-output detections, or unauthorized-action attempts.
- Unexpected changes in routing, handoff frequency, or model usage and cost.
Set baselines from normal behavior and service objectives rather than adopting a universal number. A payment-authorizing agent warrants tighter safety thresholds than a low-risk brainstorming assistant. Route urgent alerts to the owner who can act, and attach links or identifiers that open the relevant run and spans.
Use a dashboard-to-trace workflow
- Start with the aggregate alert and identify the affected workflow, model or version, tool, and time window.
- Compare task outcomes with service signals to determine whether the incident is availability, quality, or both.
- Open representative traces, including successful and failed runs from the same segment.
- Locate the first divergent span: prompt or retrieval, model decision, tool call, handoff, guardrail, or evaluator.
- Reproduce the pattern in a labeled dataset, apply the fix, and rerun evaluations before rollout.
Segment metrics so averages do not hide failure
At minimum, segment by workflow, model and version, tool, and outcome type. Add customer or tenant, geography, language, risk tier, and deployment when those dimensions affect behavior and your privacy policy allows their use. Preserve enough context to distinguish a new prompt from a new model, a provider outage from a schema error, and a rare high-impact failure from ordinary variance.
Watch distributions, not only averages. Tail latency, concentration of tool failures, and the worst safety grades often matter more than a mean. Pair a quality score with its sample count and confidence information so a small batch of traces does not create a false trend.
Choose observability and evaluation capabilities
When comparing tracing or evaluation software, use a capability checklist rather than a generic vendor ranking. Products such as LangSmith and Langfuse are examples of tools discussed in production-monitoring material; suitability depends on your stack, data controls, and workload.
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| Decision axis | Questions to answer |
|---|---|
| Framework and tool coverage | Can it instrument the agent framework, model provider, retrieval layer, and every production tool? |
| Workflow and handoff visibility | Does one trace contain the complete run, parallel work, retries, handoffs, and delegated agents? |
| Evaluation and grading | Can you define application-specific criteria, grade traces, manage datasets, and compare versions? |
| Segmentation and drill-down | Can dashboards filter by workflow, model, tool, outcome, and risk, then open the underlying spans? |
| Privacy and access | Are redaction, retention, encryption, role-based access, and regional controls adequate for your data? |
| Sampling and overhead | Can you control evaluation sampling and measure added latency and cost? |
| Integration and export | Can traces and scores flow into existing incident, data, and analytics systems? |
| Total cost | What is the combined cost of ingestion, storage, evaluation-model calls, human review, and operational latency at the required coverage? |
A staged implementation plan
- Map the workflow. List model calls, tools, retrieval, guardrails, handoffs, and delegated workers. Choose the run or turn as the root trace.
- Define success and failure. Write task-level outcomes and safety criteria with observable labels before building dashboards.
- Add tracing. Capture timing, status, inputs and outputs allowed by policy, usage, tool arguments and results, retries, and parent-child relationships.
- Ship service dashboards. Display volume, latency, errors, usage, and tool failures with workflow and version filters.
- Add outcome signals. Instrument completion, user feedback, and business measures, keeping them distinct from technical response success.
- Create an evaluation set. Label representative traces, include edge and safety cases, and add a holdout set.
- Run sampled production grading. Begin with a risk-appropriate rate; LangChain’s 10–20% suggestion is one cost-management reference, not a required target.
- Calibrate and alert. Compare automated grades with human reviews, establish workload-specific thresholds, and connect alerts to trace drill-down.
- Review every change. Rerun the dataset after model, prompt, routing, tool, retrieval, or policy changes and watch production slices after release.
Common monitoring mistakes and their fixes
Only watching infrastructure
CPU, uptime, latency, and HTTP errors can all look normal while the agent selects the wrong tool. Add task outcomes, tool-path measures, and trace grading.
Scoring only the final answer
A plausible final response can conceal an unsafe or unauthorized intermediate action. Grade the trajectory, tool arguments, handoffs, and policy decisions as well as the response.
Using one global quality score
Different workflows have different definitions of correctness and risk. Use criteria tied to each application and segment results by workflow and risk tier.
Trusting an automated evaluator without calibration
Evaluators can encode the wrong standard or drift with traffic. Compare them with periodic human judgments and revise the rubric when disagreement appears.
Collecting traces without a data policy
Unrestricted payload capture can expose sensitive information. Decide redaction, access, retention, and jurisdictional controls before expanding trace collection.
Optimizing one metric
Lower latency or cost is not a win if task success or safety falls. Review cost, latency, quality, and outcome measures together and optimize for the workload’s requirements.
What a mature operating rhythm looks like
On each deployment, compare service health and evaluation results against the previous version. During daily operations, inspect outcome and safety alerts, then sample traces from both good and bad segments. At regular review intervals, refresh representative datasets, check evaluator agreement with human reviewers, and verify that retention and access rules still match the data being captured. This closes the loop from live behavior to diagnosis, controlled change, and evidence that the change helped.
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