This paper presents a novel approach to multi-agent systems through the implementation of Agentic Retrieval-Augmented Generation (RAG), a technology that extends traditional RAG models by enabling dynamic data ingestion and real-time reasoning. We explore how Agentic RAG leverages large language models (LLMs) to create adaptive agents capable of interacting with dynamic environments and updating their knowledge bases on the fly. The proposed architecture includes Function Calling Agents, ReAct agents, and advanced agents such as the LLMCompiler and Chain-of-Abstraction agents. We examine applications of Agentic RAG in complex multi-agent simulations, such as smart city environments, economic systems, and social dynamics. Comparative evaluations against traditional RAG models demonstrate significant improvements in performance and flexibility. We also discuss the potential for Agentic RAG to build dynamic schemas for digital twins, setting the stage for future applications in real-time decision-making systems.