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AutoJudge

AutoJudge evaluates AI behavior automatically so teams can catch regressions before they become customer incidents. It can run in CI, in production, or as part of scheduled pipelines.

What AutoJudge Evaluates

AutoJudge can score:

  • Quality and correctness.
  • Safety and harmful-content risk.
  • Relevance to user intent.
  • Policy compliance.
  • Tool-use quality.
  • Business outcome alignment.
  • Regression against previous releases, prompts, models, or workflows.

When To Use It

Use AutoJudge when you need a repeatable evaluation layer for production AI systems, especially where manual review is too slow or inconsistent.

Prerequisites

  1. Capture traces from an agent, chat, model, or workflow.
  2. Define what “good” means for the workflow.
  3. Decide whether the eval should block CI, alert in production, or create a report.

Step-By-Step Setup

  1. Open AutoJudge or Evals in the console.
  2. Choose a trace, dataset, or process unit as the input source.
  3. Select evaluation dimensions: quality, safety, relevance, policy, or business outcome.
  4. Define pass/fail thresholds.
  5. Run the judge on a historical sample.
  6. Review examples and adjust prompts or rubrics.
  7. Attach the judge to CI, production monitoring, an alert, or a scheduled pipeline.

Example Configuration

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name: support-agent-answer-quality
input:
  source: traces
  workflow: support-agent
judges:
  - quality
  - relevance
  - policy
thresholds:
  quality: 0.85
  relevance: 0.90
  policy: pass
on_failure:
  alert: support-ai-quality-regression

What Appears In The Console

You will see eval score trends, failing examples, related traces, model and prompt versions, affected users or workflows, and recommended next actions.

Common Mistakes

  • Using one generic rubric for every workflow.
  • Evaluating only model output without tool-call or business context.
  • Running evals in CI but not in production.
  • Alerting on every failure instead of alerting on meaningful patterns.