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This article delineates the evolving operational frameworks required as AI systems transition from static predictive models to dynamic, tool-using agents. It highlights how observability must expand beyond traditional metrics like accuracy to include prompt engineering, retrieval quality, and multi-step task execution in AgentOps environments. The distinction underscores that managing autonomous agents demands a fundamentally different approach to monitoring than standard MLOps or even LLMOps.

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What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

LLMOps vs MLOps vs AgentOps: What Changes When You’re Operating Language Models at Scale

Analytics VidhyaSeptember 17, 20264 min read

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 post LLMOps vs MLOps vs AgentOps: What Changes When You’re Operating Language Models at Scale appeared first on Analytics Vidhya.

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