AgentOps is an emerging discipline focused on the operationalization, monitoring, and optimization of agentic AI systems, especially in the context of generative AI. This paper provides an overview of AgentOps, its relationship to MLOps and GenAIOps, and best practices for deploying and managing agentic AI in enterprise environments. This paper shows how the evolution happened form operational methodologies in AI, from traditional Machine Learning Operations (MLOps) to finally specialized Large Language Model Operations (LLMOps) and then to Generative AI Operations (GenAIOps). We examine the key challenges in operationalizing generative AI, including model monitoring, prompt management, agent debugging, and ethical considerations. Through a systematic review of over 100 recent articles and industry practices, we identify critical gaps in current operational approaches and summarize based on recent literature an integrated framework that combines MLOps, LLMOps, and AgentOps principles. We present case studies demonstrating successful implementations and provide recommendations for organizations transitioning to generative AI at scale. We delineate the distinct characteristics, challenges, and best practices associated with each stage, emphasizing how AgentOps extends prior concepts to manage the unique complexities of multi-agent systems, including monitoring, debugging, and secure lifecycle management. We provide a holistic overview of the current landscape and future directions for operationalizing advanced AI systems.