AGENT OPERATIONS (AGENTOPS)

DEFINING AGENTOPS Agent Operations (AgentOps) means the practice of running agents reliably in production. It covers everything that happens after launch: watching agents, controlling them, measuring them, improving them, and keeping humans in charge where needed. The word follows a pattern you may have heard before. Development Operations (DevOps) is the practice that joined software development and operations so that traditional software can be built, released, and run smoothly. Machine Learning Operations (MLOps) extended those ideas to trained machine learning models, which need data pipelines, retraining, and monitoring for accuracy. [v2 UPDATE] Current industry sources from 2025 and 2026 describe AgentOps as an analogous discipline, but one focused specifically on the full production lifecycle of autonomous agents. It adds concerns that earlier practices did not fully cover: observability of agent-specific artifacts such as goals, plans, and tool calls, governance, cost control, continuous evaluation, and human oversight. The terminology is still settling, and different vendors use the term in somewhat different ways, so when you read about AgentOps, check what a particular source means by it. Why does an agent need its own discipline? A normal program does exactly what its code says. An agent decides what to do as it goes. That means two runs of the same task may take different paths. It may call tools you did not expect, spend different amounts, and fail in new ways. So it is not enough to monitor whether the server is up. You must monitor whether the agent is pursuing the right goal, following sensible plans, and using tools appropriately.