Video by MLflow via YouTube
Making sure AI agents stay useful, accurate, and safe still needs a human in the loop. MLflow Review Queues make that review process easier and help you build a dataset of failure modes you can use to improve your app.
In this walkthrough, Khalil Kafrouni uses a support agent for an ecommerce store, with every interaction traced in MLflow, and show how to:
• Create a Hallucinations Check queue
• Create a Usefulness Check queue
• Build pass/fail, numeric, and free-text questions
• Reuse questions across queues
• Flag traces for review
• Review traces with the full tool-call context
• Bake assessments back into traces for future evaluation and iteration
You’ll also see the difference between:
• Feedback questions, where reviewers grade the response
• Expectation questions, where reviewers provide the best possible answer
No more Excel files going back and forth. Review Queues give humans a cleaner way to improve agent quality.
Learn more: https://mlflow.org/blog/review-queue-feature/
⭐ Star MLflow on GitHub: https://github.com/mlflow/mlflow
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