From MLOps to AgentOps: Shipping Autonomous Agents You Can Actually Trust

We spent a decade learning to ship models reliably. Agents break most of those assumptions: outputs are nondeterministic, behavior depends on tools and context and “good” is defined by domain experts, not a binary test. AgentOps is what MLOps becomes when your system can reason, call tools and act on its own.

In 20 minutes, we’ll trace the jump from MLOps to LLMOps to AgentOps and distill it into a practical operating model on Databricks: choosing the right architecture tier (managed AgentBricks vs. code-first), making evaluation a first-class citizen with MLflow LLM judges, governing tools through Unity Catalog and promoting agents across environments with Databricks Asset Bundles.

You’ll leave with the stakeholder map, anti-patterns, and DevOps principles needed to move an agent from a demo that impresses to a system production can depend on.