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In Observe your agent, you found a failure by reading one trace. Now catch that failure across ongoing production traffic with an online eval: an evaluation that scores every trace as it arrives.
The judge calls a model, so add a provider first under Settings → Account Settings → Integrations. See AI provider integrations.
Arize AX Evaluators page: Evaluator Hub and Running Eval Tasks tabs, New Evaluator and New Task buttons, quickstart cards for Hallucination, User frustration and Session frustration, and a Span evals panel showing label distribution per evaluator

The Evaluator Hub, with quickstart templates and score distributions for evaluators already running.

1

Pick a template

Go to Evaluators → New Evaluator and pick the template that matches your failure. This guide uses Hallucination. For objective checks, like an empty response, use a code evaluator.
New Evaluator template picker: Response Quality, Code Quality and Trajectory groups with Hallucination, Q&A, User Frustration, Toxicity, Summarization, SQL Generation, Tool Calling and Task Completion templates, plus options to create a custom eval via skills or code

Templates grouped by what they check, with the variables each one needs.

2

Configure the judge

Name it, set its Scope, pick the model, and adjust the prompt and Choices to your standards. Leave Explanations on so each score says why it passed or failed.
3

Point it at your traces

Set Pick a Data Source to your project, use Filter Spans to choose what’s scored, and use Map Variable to Data to connect {input}, {context}, and {output} to your span attributes. Test Evaluator On Spans tries it on a sample.
New Evaluator configuration: name, scope, model and judge prompt on the left with hallucinated and factual choices; on the right, pick a data source, filter spans, matching spans and map variable to data, with Test Evaluator On Spans, Create Evaluator and Create and Run Evaluator buttons

Configuring a hallucination evaluator and pointing it at a project.

4

Turn it on

Click Create and Run Evaluator. This creates a task that scores new traffic from now on. Manage it under Running Eval Tasks.

Find the failures

After a few minutes, filter your project’s traces by the evaluation score and open a failing trace to read the judge’s explanation.
Traces list with evaluation score columns showing evaluator labels

A traces list with evaluation score columns.


Trace detail showing an evaluator label with judge explanation

A trace detail with the evaluator's label, score, and explanation.

Look for a shared cause, often the prompt. Next, run evals on experiments to test a fix. See the evaluators reference for how scoring works.

Other ways to run evals

With your coding agent, using Arize Skills (arize-evaluator and arize-trace). See coding agent setup for authentication.
With Alyx:
In code, with the Python SDK, TypeScript SDK, or ax CLI:

Previous: Observe your agent

Next: Run evals on experiments