Building Trustworthy, High-Quality AI Agents with MLflow

Building AI agents presents unique challenges, as outputs can be free-form and unpredictable, often requiring specialized domain expertise to evaluate quality. This session explores how MLflow provides a unified platform to manage the full agent development life cycle. Key topics include using MLflow tracing for end-to-end observability and debugging, leveraging automated LLM judges to scale expert feedback, and employing the prompt registry for versioning and optimization. The talk also highlights the role of an AI gateway in providing essential governance through permissions, rate limits, and input guards to manage costs and data privacy. Key Takeaways: - Implementing end-to-end observability with MLflow tracing for step-by-step execution analysis. - Scaling quality assessments through automated LLM-as-a-Judge evaluations and human expert alignment. - Iteratively improving agent performance using evaluation datasets and automated prompt optimization. - Ensuring production-grade governance and cost control with a centralized AI gateway.