How to Monitor AI Agents in Production: Traces, Evaluations and Review

Updated October 29, 2026. This guide now reflects selected compatible evaluators, explicit instrumentation and engineer-reviewed recommendations. Unsupported setup-time, overhead and ETL-speed promises have been removed.

Monitoring an AI agent requires both operational evidence and checks of the task outcome. A fast 200 OK response can still contain an unsupported answer, an irrelevant retrieval result or an action outside the agent’s assigned scope.

A useful monitoring workflow connects a signal to the captured request, the evaluation condition and the decision about what to change. I’m Shashank Agarwal, founder and CEO of Noveum AI and creator of API.market. This product-oriented guide was refreshed with AI assistance.

What to monitor

Separate measures that answer different questions:

An aggregate quality score is not a replacement for those distinctions. Decide which failures matter for your application and how a reviewer will inspect them.

Step 1: Capture the operations you need

Use context managers or supported framework integrations to capture application behavior with noveum-trace. Initialize the SDK once, then instrument the model, retrieval and tool boundaries that matter for your task.

import os

import noveum_trace

noveum_trace.init(
    api_key=os.environ["NOVEUM_API_KEY"],
    project="support-agent",
    environment="development",
)


def normalize_question(question: str) -> str:
    with noveum_trace.trace_operation("normalize_question") as span:
        cleaned = question.strip()
        span.set_attribute("input.character_count", len(cleaned))
        return cleaned


if __name__ == "__main__":
    print(normalize_question("  Where is my invoice?  "))
    noveum_trace.flush()

This example traces a local function and does not call an LLM or evaluate its output. Check the integration guide for the configuration and application versioning appropriate to your integration.

Verify one representative trace before expanding capture. Instrumentation, framework support, sampling and configuration determine what is available. Avoid claiming complete visibility into operations the application has not instrumented.

Step 2: Define evidence for each evaluation

A system prompt defines instructions and constraints. It is not an independent source of factual truth.

For role adherence, the prompt and task can help specify expected behavior. For a grounded answer, the relevant source context is needed. For exact or reference-based accuracy, prepare suitable expected outputs or reviewed references. Some checks do not require a reference answer, but still require correctly mapped inputs.

Keep a reviewed set of representative cases. Include missing context, failed tool calls, ambiguous requests and previously observed failures. Inspect a sample of passing cases as well as failures so that evaluation mistakes are visible.

Step 3: Prepare data and select compatible checks

Use dataset and ETL workflows to turn relevant trace data into structured evaluation items. Inspect the transformed output before using the results as a release gate.

On October 29, 2026, Noveum’s public library lists 112 evaluators across 15 categories. Configure checks that fit your task and available fields; this is not a promise that every check runs on every trace.

The library contains model-backed, deterministic, audio-model and signal-based checks with different result types. Use the scorer’s native result and saved pass decision. Do not assume every check is a 0–10 score or that one threshold suits every application.

Step 4: Investigate repeated failures

NovaPilot organizes evaluated failures into ranked findings and recommendations with supporting evidence. A recommendation is a candidate for investigation, not a guaranteed diagnosis or an automatic production fix.

For example, an unsupported answer may follow from an irrelevant retrieval result, missing source content, ambiguous instructions or the model’s use of the context. A score alone cannot determine which explanation is correct. Inspect the relevant trace and dataset item before choosing a change.

Keep the application version, dataset selection, evaluation configuration and model settings where applicable with the investigation. That makes later comparisons easier to interpret.

Step 5: Test and review the proposed change

Exercise a candidate change with relevant cases and, where appropriate, configured NovaSynth personas and scenarios. Compare the behavior on previously failing examples and inspect regressions in other important cases.

Decide what happens when a required check fails or cannot run. Release approval remains an engineering decision based on the evidence and the application’s requirements.

The eval-driven development guide covers this development loop. For a narrow worked example, see why a correct HTTP response can still produce a bad agent outcome.

Practical monitoring questions

Do I need labeled examples?

It depends on the check. Reference-free checks can work without an expected answer when their required context and task fields are available. Other checks need a reference. Human-reviewed examples also help determine whether a scorer’s judgments match the application’s requirements.

Does tracing evaluate every response inline?

Tracing and evaluation are separate workflows. Capture alone does not establish that a response has been scored, that scoring runs before delivery or that a configured check can block an action. Verify the execution path and latency requirements for your application.

What overhead should I expect?

Measure it in your integration. Workload, payload size, instrumentation, sampling and transport settings affect overhead. This guide does not promise a universal per-trace latency or setup time.

Are model-reported confidence and predicted satisfaction outcomes?

They are signals with their own limitations. Preserve measured outcomes, human feedback and predictions as different fields. A predicted satisfaction score is not a customer survey result.

Start with the current documentation and one representative request. Follow my founder and engineering work on LinkedIn.