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Monitor an AI agent at four levels: service health, task outcomes, execution behavior, and quality and safety. Latency, uptime, token use, and error rates tell you whether the service is running, but they cannot tell you whether the agent chose the right tool, completed the user’s task, followed instructions, or avoided a policy violation. Combine infrastructure metrics with end-to-end traces, structured evaluations, user feedback, and business outcomes.
The practical unit of diagnosis is one complete run or conversation turn, connected to every model call, retrieval step, tool call, handoff, and delegated task. That structure lets an operator move from an alert to the exact decision that failed.
The four measurement layers
A production monitoring plan should answer two separate questions: is the service healthy, and is the agent doing the intended work safely and effectively? Organize metrics into four layers so a strong infrastructure signal cannot conceal a poor user outcome.
1. Service health
Service-health metrics describe availability, speed, capacity, and spend. Collect them for the complete run and, where possible, segment them by workflow, model or model version, and tool.
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| Measure | What it reveals | Useful segmentation |
|---|---|---|
| End-to-end latency | How long a user waits for the run to finish, including retries and tool work | Workflow, model version, region, request type |
| Step latency | Which model, retrieval, guardrail, or tool span is slowing a run | Span type, tool name, provider, success status |
| Request volume | Traffic level and changes that can affect capacity or evaluation coverage | Tenant, endpoint, workflow, hour or day |
| Failure and error rate | Transport failures, timeouts, provider errors, tool errors, and aborted runs | Error class, model, tool, retry count |
| Resource and model usage | Tokens, compute, concurrency, rate-limit pressure, and cost drivers | Model, tenant, workflow, input and output tokens |
OpenAI’s observability guidance describes inspecting live activity and recorded usage, while its tracing documentation exposes span duration and status. Use those signals to detect an unhealthy service, then use the trace to determine whether the cause was capacity, a provider response, a prompt change, or a downstream dependency.
Do not treat a low error rate as proof of success. An agent can return a syntactically valid answer after selecting the wrong tool or misunderstanding the request.
2. Task outcomes
Outcome metrics test whether the user’s intended job was completed. Define “success” for each workflow before collecting scores; a technically completed run may still have failed the task.
- Task-completion rate: the proportion of runs meeting the workflow’s explicit completion criteria.
- User feedback: thumbs-up or thumbs-down signals, ratings, corrections, re-prompts, cancellations, and requests for human help.
- Business outcomes: application-specific results such as a correctly updated record, a resolved support case, or a completed approval. Use only outcomes that can be verified.
- Abandonment and escalation: where users stop, restart, or request a person before completion.
LangChain’s production-monitoring guidance recommends dashboards for success measures such as task completion and user satisfaction. The OpenAI cookbook also demonstrates collecting user-feedback signals. These measures should be tied to a workflow definition rather than treated as a universal score for every agent.
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3. Execution behavior
An agent’s path matters as much as its final text. Record and measure model calls, retrieval operations, tool calls, handoffs, retries, and delegated work.
- Tool-call failure rate: failed, timed-out, rejected, or malformed calls divided by attempted calls.
- Tool selection: whether the chosen tool was allowed and appropriate for the user’s request.
- Tool sequence: whether calls occurred in an acceptable order, including required validation before an external side effect.
- Run count by tool: unexpected spikes can indicate a routing loop, prompt regression, or a tool that is being overused.
- Handoff correctness: whether escalation happened when the agent lacked authority, confidence, or required information, and whether it happened unnecessarily.
- Retry and loop behavior: repeated calls, maximum-step exits, and runs that consume budget without advancing the task.
The evaluation questions are concrete: Did the agent pick the right tool? Did a handoff happen when it should have? Did the workflow violate an instruction or safety policy? Trace-level data allows an operator to move from an aggregate failure to the run and step responsible.
4. Quality and safety
Quality metrics grade what the agent produced and how it behaved. Choose criteria that reflect the application, not a generic notion of “good AI.” Typical dimensions include:
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- Relevance and completeness for the user’s request.
- Instruction following, including developer and workflow constraints.
- Grounding in approved retrieval sources when retrieval is required.
- Policy adherence, authorization boundaries, and refusal behavior.
- Sensitive-output risks such as exposure of personal data, secrets, or prohibited content.
- Trajectory quality for multi-step work: the decisions, calls, and checks that led to the final result.
Automated graders make it possible to review more traces, but they are measurement aids rather than unquestionable truth. Calibrate them against application-specific examples and check them periodically with human reviewers. A grader can be confidently wrong if its rubric does not match the workflow.
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Design a trace that can explain a failure
Treat one end-to-end run or conversational turn as the top-level record, and preserve parent-child relationships for every step. OpenAI’s tracing documentation describes a dashboard showing what an agent did, including each step’s recorded inputs, outputs, duration, and status. That is the minimum visibility needed for diagnosis.
Fields to capture
| Trace area | Capture | Diagnostic question |
|---|---|---|
| Run identity | Run ID, timestamp, workflow, tenant or environment, model and version | Which exact execution is being inspected? |
| Inputs and instructions | User request, applicable system or developer instructions, retrieved context identifiers | Did the agent receive the right information and constraints? |
| Model spans | Prompt and response metadata, duration, status, token usage, finish reason, and retry details | Did the model produce a usable decision? |
| Tool spans | Tool name, validated arguments, authorization result, response status, duration, and error | Was the right tool called safely and did it work? |
| Handoffs and delegation | Source and destination agent, reason, payload, status, and resulting work | Was escalation or delegation justified and completed? |
| Outcome | Completion state, business verification, user feedback, and evaluator labels | Did the requested task actually succeed? |
Keep enough context to distinguish a prompt defect from a retrieval defect, tool-path defect, guardrail defect, or evaluation blind spot. If a trace cannot connect an incorrect final answer to the preceding decision and source data, it is an event log, not an effective diagnostic trace.
Privacy, retention, and access
Trace payloads may contain user data, instructions, tool arguments, and model outputs. Decide explicitly what may be stored, for how long, who can view it, and what must be redacted or tokenized. Apply access controls to both dashboards and exported traces. The appropriate policy depends on the data and jurisdiction; tracing documentation establishes the capability, not a universal retention rule.
If you export traces, preserve the run and span identifiers and document the export format. OpenAI documents OTLP JSON export when enabled, which can support integration with a broader observability system.
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Turn traces into evaluations
Define criteria before scoring
Write a structured rubric for each important workflow. Include pass and fail examples for a correct answer, wrong tool, improper handoff, instruction violation, and safety or authorization violation. Separate binary requirements, such as “did not send an unauthorized request,” from graded dimensions, such as relevance or completeness.
Build a representative dataset
Collect traces that cover ordinary requests, ambiguous requests, edge cases, failures, high-risk actions, and known regressions. Remove or protect sensitive data before using traces in a shared evaluation set. Keep the expected outcome or reference evidence beside each case so changes can be compared consistently.
Grade the whole trajectory
For a multi-step agent, score both the final answer and the path. A correct final answer reached through an unauthorized tool call is not a safe success. Conversely, a refusal may be correct when the requested action exceeds the agent’s authority. Trace grading assigns structured scores or labels to an end-to-end log of decisions, tool calls, and reasoning steps so correctness, quality, and adherence can be assessed together.
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Repeat evaluations after changes
Run the dataset whenever you change a prompt, model, router, retrieval configuration, tool schema, guardrail, or handoff policy. Compare completion, quality, safety, latency, and cost; an improvement in one dimension may create a regression in another. Keep results by version so a production incident can be matched to the change that introduced it.
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Sampling and evaluation overhead
Evaluating every production trace with an LLM may be too expensive or slow for some workloads. LangChain’s production article recommends sampling 10–20% of production traffic for LLM-based evaluation. That is a vendor recommendation, not an independently measured industry benchmark. Sampling should be risk-aware: always include high-impact actions, policy-sensitive workflows, new releases, and traces that trigger errors or unusual tool behavior.
The same guidance notes that evaluators may add a few seconds of latency per trace, may not match application-specific standards, and may need adjustment as traffic changes. Run expensive grading asynchronously when the user does not need an immediate result, and monitor the sampling rate itself so a configuration change does not silently remove coverage. Pair automated evaluation with periodic human review to detect grader drift and blind spots.
Alerts that lead to action
Alert on both service failures and application-specific outcomes. Establish thresholds from your workload’s risk tolerance and service objectives; the available guidance does not define universal acceptable values for latency, errors, or quality.
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|---|---|---|
| End-to-end latency exceeds the workflow objective | Latency by span, model, and tool | Find the slow dependency, retry storm, or queue buildup |
| Tool failures or timeouts rise | Tool error class and representative traces | Check credentials, schema validation, rate limits, and downstream availability |
| Task completion falls while service health is normal | Outcome dashboard and graded traces | Inspect routing, retrieval, prompt, and tool-selection changes |
| Policy or authorization violations appear | Safety labels and complete trajectories | Contain the affected action, review guardrails, and sample related traces |
| Cost per successful task increases | Token use, step count, retries, and success by workflow | Remove loops or unnecessary context without sacrificing quality |
Design alerts with a drill-down path: aggregate signal, affected workflow or version, representative run, failing span, and owner. A notification that cannot lead an operator to evidence will create noise rather than shorten recovery.
Choosing an observability and evaluation tool
Compare tools against the workflow you actually run, not against a feature checklist detached from implementation. LangSmith and Langfuse are examples of products in this category; neither should be treated as universally best. Evaluate the following axes:
- Framework and tool coverage: can it instrument your agent runtime, model providers, retrieval layer, and custom tools?
- Full workflow visibility: does it preserve handoffs, delegated work, retries, and parent-child relationships?
- Evaluation and grading: can you define structured criteria, attach labels to spans, run datasets repeatedly, and compare versions?
- Segmentation and drill-down: can dashboards filter by workflow, model, tool, tenant, release, and outcome, then open the underlying trace?
- Privacy controls: are redaction, retention, access permissions, regional storage, and audit functions adequate for your data?
- Sampling and overhead: can you control which traces are evaluated and keep evaluator latency or cost within budget?
- Integration and export: can telemetry reach existing monitoring systems in a format your team can operate?
- Total cost at required quality: include ingestion, storage, evaluation calls, retention, and engineering effort.
A practical rollout sequence
- Map the workflow. List models, retrieval steps, tools, handoffs, side effects, and completion criteria.
- Create the top-level trace. Assign a run ID and connect every model and tool span to it. Record duration, status, inputs and outputs where policy permits, and usage.
- Instrument outcomes. Add explicit completion states, business verification, user feedback, and escalation outcomes rather than inferring success from HTTP status.
- Start with failure visibility. Dashboard latency, request volume, error rate, tool failures, retries, and cost by workflow and version.
- Write the quality rubric. Define correctness, relevance, instruction following, policy adherence, and trajectory requirements with concrete examples.
- Assemble and label traces. Include normal, ambiguous, failed, high-risk, and recently changed cases; protect sensitive data.
- Automate sampled grading. Begin with risk-based coverage, measure evaluator cost and latency, and compare automated labels with human judgments.
- Set actionable alerts. Use workload-specific thresholds and ensure each alert links to representative traces and an owner.
- Review after every change. Re-run the dataset and compare quality, safety, latency, and cost before and after deployment.
Common monitoring mistakes
- Watching only infrastructure: uptime and latency can look healthy while task success collapses.
- Logging only the final answer: without tool arguments, handoffs, and statuses, the cause of failure is hidden.
- Using one global quality score: a score that ignores workflow-specific safety or authorization rules is misleading.
- Evaluating only easy traffic: averages hide rare but consequential failures; include edge and high-risk traces.
- Trusting an automated grader without calibration: graders require human checks and rubric updates as the application changes.
- Optimizing a single metric: lower cost or latency is not an improvement if correctness or safety declines.
- Storing sensitive traces indefinitely: define retention, redaction, and access rules before broad collection.
- Alerting without a drill-down: operators need the affected run and span, not just a red number.
No independent population statistic establishes how widely any particular monitoring practice is used in production. The defensible approach is to measure your own workload, document the criteria, and preserve enough trace evidence to explain every important failure.
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