Multi-agent Retrieval-Augmented Generation (RAG) systems are transforming enterprise applications by providing real-time AI interactions with advanced technologies like AWS Bedrock and SingleStore. Naive RAG, a foundational concept, is limited in its ability to handle complex tasks due to its reliance on a single retrieval approach. Advanced RAG models overcome these limitations by incorporating multiple retrieval strategies and leveraging contextual embeddings. These systems employ various techniques such as input/output validation, guardrails, caching, hybrid search, re-ranking, and continuous self-learning to enhance accuracy, speed, scale, and security. Multi-agent RAG systems break down tasks into smaller components, allowing for parallel processing and more accurate responses. They facilitate improved collaboration among agents, enabling them to share insights and findings dynamically. The architecture of these systems reflects the growing complexity of enterprise needs, evolving from monolithic to modular structures with the integration of supervisory agents. Agents are the building blocks of multi-agent RAG systems, serving specific purposes and contributing to the overall functionality and intelligence of the system. By leveraging AWS Bedrock and SingleStore, organizations can build powerful applications that leverage the strengths of both platforms, enabling unified data handling, robust security features, and an accelerated go-to-market strategy. The collaboration between AWS and SingleStore enables significant improvements in response times, customer satisfaction, and operational efficiency, positioning these systems as pivotal advancements in enterprise AI. Looking ahead, the future of multi-agent RAG systems is poised for significant advancements driven by ongoing innovations in AI technologies and data management practices, ultimately transforming how businesses interact with their customers and providing a more personalized and efficient experience.