Monitor an AI agent on two tracks: watch whether the service is operating reliably, and check whether it is still completing the intended task safely and well. Use traces to diagnose individual runs, then aggregate operational signals and task evaluations to spot changes across runs. No dashboard or alert can guarantee that every failure will be caught.
What to monitor: service health and task success
An agent can return a successful service response and still fail the user. It might call the wrong tool, stop before finishing, or produce an answer that misses the task requirements. Conversely, a task-quality problem may appear alongside healthy infrastructure. Monitor both dimensions rather than treating uptime or a low error count as proof that the agent works as intended.
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NIST’s 2026 overview separates functionality monitoring—whether a system continues to work as intended—from operational monitoring—whether it maintains consistent service across its infrastructure. These are distinct questions, and an agent’s monitoring plan should answer both.
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|---|---|---|
| Operational | Is the service responding consistently, and where is it slowing down or erroring? | Run status, error status, duration, and failures in model or tool steps |
| Functional and quality | Did the agent complete the requested work, use tools appropriately, and meet the task’s requirements? | Task completion, valid tool actions, and scores against an application-specific rubric |
Choose task checks for your application and risk level. The sources do not establish universal quality-score, latency, or alert cutoffs that apply to every agent.
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Trace a whole run, not just the final answer
An agent run is a sequence of steps. A useful trace has a parent record for the user task and child spans for the work that happened inside it, such as model responses, tool calls, retrieval, and delegated-agent work. Include timing and status so an operator can see which step failed, stalled, or returned an unexpected result.
Attach identifiers that let the team connect the run to its session and the relevant deployment or prompt version. Capture only the input and output content needed for debugging and evaluation; detailed payloads may contain sensitive information.
A trace is a diagnostic record, not a quality guarantee. It can show what the agent did, but the team still needs checks that determine whether those actions and the final result were acceptable.
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Build a monitoring loop that turns failures into checks
1. Instrument a representative end-to-end task
Use OpenTelemetry or an integration supported by your agent framework to emit traces, metrics, and logs for a complete task. OpenTelemetry is a vendor-neutral instrumentation and telemetry framework; its Collector can receive, process, and export telemetry. Confirm that the instrumentation actually covers the model, tools, retrieval, and delegation paths your agent uses.
2. Define task outcomes alongside operational signals
For each important workflow, decide what counts as completion and what a valid tool action looks like. Where a task has measurable requirements, evaluate the output against a rubric or representative expected results. Record those outcomes alongside operational signals so an error-free run is not automatically counted as a successful task.
3. Alert on sustained, meaningful changes
Set thresholds from your own baseline, tolerance for false alarms, and the consequences of a failure. A sustained increase in errors or duration may warrant investigation; so may a decline in task completion or rubric results. There is no source-backed universal cutoff for these signals.
Make alerts actionable: include the affected workflow or deployment, the signal that changed, and links within your observability system to representative traces and evaluation examples. An alert should help an operator find what to inspect, not merely announce that a number crossed a line.
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4. Review the run, add a test, and compare changes
- Inspect a failed or degraded run and identify the step or behavior involved.
- Turn the example into a representative evaluation case, removing or protecting sensitive data as needed.
- Run the same cases against the current and candidate prompt, model, or agent change.
- Review task outcomes and operational behavior before expanding the rollout.
AWS’s CloudWatch agent-monitoring documentation describes this kind of loop: instrument agents, analyze traces and sessions, score outputs, build datasets and experiments, and monitor production health. OpenTelemetry’s agent guidance also explains how telemetry can support evaluation, which matters because agent behavior is non-deterministic.
Choose tools by role and fit
OpenTelemetry, managed cloud observability products, and provider tracing features serve different roles. OpenTelemetry provides an instrumentation and telemetry foundation; product-specific tools provide their own analysis and evaluation experiences. Fit depends on your framework, runtime, permissions, export needs, data controls, and existing stack—not on a universal ranking.
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| Option | What the documentation describes | Check before adopting |
|---|---|---|
| OpenTelemetry | Vendor-neutral instrumentation and collection/export for traces, metrics, and logs | Whether your frameworks and languages are instrumented, and whether your chosen backend can receive the data you need |
| AWS CloudWatch agent monitoring | Agent instrumentation with OpenTelemetry, trace/session and topology analysis, output evaluation, production health, and experiments; documentation lists AgentCore agents and agents using other frameworks and compute environments | Whether your agent runtime, AWS environment, permissions, and evaluation workflow fit the documented integration |
| OpenAI Agents tracing | Sessions, turns, and spans for model responses and tools, with inputs/outputs, duration, and status where recorded; trace export uses OTLP JSON | Trace export must be enabled, and the project needs appropriate access for export |
| Google Cloud Observability | OpenTelemetry instrumentation, quality and cost review, and telemetry for communication flows | Where payloads are stored, who can access them, and how retention and deletion work for your use case |
These descriptions reflect the cited providers’ documented capabilities, not a comparative performance test. Evaluate products using your own representative tasks and governance requirements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Set payload, access, and retention rules before collecting rich traces
Prompts, responses, and tool arguments can contain personal, confidential, or security-sensitive information. Decide what to capture, what to redact, who may inspect it, how long it is retained, and how it can be deleted before enabling full-content tracing.
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Google’s agent observability guide recommends Cloud Storage rather than log entries for prompt and response content when fine-grained deletion or larger objects matter. It identifies a 256 KiB maximum log-entry size and warns that oversized data may be rejected or truncated. That limit applies to Cloud Logging log entries; it is not a general limit for every telemetry backend.
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Use a runbook that starts with the affected task
When an alert fires, first determine whether the issue is operational, task-level, or both. Then use the trace and evaluation record to narrow the investigation instead of assuming the final answer alone explains the failure.
- Service errors or slow runs: inspect the parent trace and identify which model, retrieval, or tool span has the error or unusual duration.
- Task completion or quality drops: compare failed examples with prior evaluation cases and inspect the steps that differed.
- Unexpected tool behavior: check the tool call and its status against the task’s rules for valid actions.
- Possible sensitive-data exposure: follow the established access, redaction, retention, and deletion process before sharing trace content.
NIST describes post-deployment AI monitoring as important because AI systems can be variable and behave unpredictably, while characterizing the field as fragmented. Its AI Risk Management Framework is voluntary and is being revised; treat it as a risk-management resource, not immutable regulatory text.
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