MLflow isn’t just for data scientists anymore—and pretending it is is holding teams back. Corey Zumar, Jules Damji, and Danny Chiao break down how MLflow is being rebuilt for GenAI, agents, and real production systems where evals are messy, memory is risky, and governance actually matters. The takeaway: if your AI stack treats agents like fancy chatbots or splits ML and software tooling, you’re already behind.

Generative AI has evolved at lightning speed from LLMs and RAG to autonomous AI agents capable of reasoning, planning, and acting. But creating a single agent is easy; managing thousands in an enterprise requires a full AI Agent Platform. This guide breaks down the architecture of a production-grade platform, covering layers like Interaction, Development, Core, Foundation, Information, Observability, and Trust. It shows how to build systems that are secure, scalable, and capable of delivering real business impact.

Agents sound smart until millions of users show up. A real talk on tools, UX, and why autonomy is overrated.