LLMOps vs MLOps vs AgentOps: What Changes When You’re Operating Language Models at Scale
Putting AI into production now takes more than deploying a model and tracking accuracy. MLOps made traditional ML manageable, while LLMOps added concerns around prompts, retrieval, evaluation, latency, and cost.  AgentOps adds another layer for systems that decide, call tools, and complete multi-step tasks. These shifts change what teams monitor and control. In this article, we compare MLOps, LLMOps, and AgentOp
Putting AI into production now takes more than deploying a model and tracking accuracy. MLOps made traditional ML manageable, while LLMOps added concerns around prompts, retrieval, evaluation, latency, and cost.  AgentOps adds another layer for systems that decide, call tools, and complete multi-step tasks. These shifts change what teams monitor and control. In this article, we compare MLOps, LLMOps, and AgentOps, and explain how observability evolves as AI systems move to action.  […] The…