Agentic retrieval-augmented generation (RAG) is revolutionizing enterprise AI by enabling businesses to automate complex knowledge tasks and enhance decision-making processes. Unlike traditional RAG systems, which focus on retrieving and generating information, agentic RAG systems add a layer of autonomy that allows them to handle multi-step tasks independently, maintain context across interactions, and use reasoning to gather and process information from multiple sources. This capability is particularly beneficial in industries such as finance, healthcare, legal, energy, and manufacturing, where agentic RAG can streamline operations, enhance resource allocation, and improve compliance with regulations. By automating data-intensive tasks, these systems free employees to focus on strategic and creative work, thus creating competitive advantages for companies. As enterprises adopt agentic RAG, they must carefully plan their implementation strategy, ensuring the integration of secure and efficient AI systems that align with their unique regulatory and operational requirements.