AgentOps: The Discipline Missing From Your AI Deployment Stack

AWS made its DevOps Agent generally available on March 31, 2026. It investigates incidents, executes SRE tasks, and operates across multicloud and on-prem environments autonomously. Enterprises that haven't shipped their own agents yet are about to have one handed to them. The question nobody is asking loudly enough: how do you operate it once it's running? MLOps gave teams a discipline for managing models — training pipelines, versioning, drift detection, retraining schedules. It was the right answer to the right problem. But the problem has changed. An agent isn't a model. It's a system that uses models to take actions in the world: querying databases, calling APIs, spawning sub-agents, writing files, triggering workflows. The failure mode isn't a bad prediction. It's a bad action. And there's no MLOps playbook for that. AgentOps is the emerging discipline that fills this gap. Not a rebrand of MLOps with "agent" swapped in, but a different set of operational concerns, practices, and