AgentOps
Practicaagent ops
AgentOps is the practice of managing the full lifecycle of AI agents once they move from prototype to production: deploying, observing, versioning, cost-controlling and governing them. It combines DevOps, MLOps, LLMOps and observability applied to agents that act, not just models that predict.
The term is pushed mainly by Red Hat and IBM. AgentOps answers a concrete problem: an agent in production is not a model, it is an actor that calls tools, chains steps and touches real systems, so it needs its own operations. It covers logging and traces for every action, continuous evaluations, permission control, per-task cost management and a way to pause it when it drifts. In practice it is the technical toolbox that makes agent governance possible.
How it differs from
- AgenticOps
- AgentOps is the operations/tooling for agents; AgenticOps (Cisco's term) emphasizes operations where the agents themselves reason and act.
Examples
- Tracing every tool call an agent makes and capping its cost per conversation is AgentOps.
- Versioning an agent's prompt and tools so you can roll back a change that broke its accuracy.
FAQ
- Is AgentOps only for engineers?
- The toolbox is technical, but the decisions (what the agent may do, with which approvals, what cost is acceptable) are business decisions. That is why AgentOps and AI Operations go together.
Related terms
Sources & further reading
- Red Hat - AgentOps · 2026