Stop Reviewing AI Traces in Spreadsheets | MLflow Review Queues

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 #MLflow #LLMOps #AIAgents #HumanInTheLoop #AIObservability