Agentic Retrieval-Augmented Generation (RAG) combines real-time data retrieval with autonomous decision-making to overcome the limitations of traditional Large Language Models.

• Solves the problem of outdated information in LLMs by integrating dynamic data sources
• Enables more accurate, relevant, and contextually appropriate responses
• Introduces autonomous capabilities that allow AI systems to make decisions without constant human intervention
• Creates foundation for specialized AI applications across industries

Medical Impact: In healthcare, Agentic RAG systems can deliver up-to-date medical information, assist with clinical decision-making, and provide personalized patient care while maintaining access to the latest research and treatment protocols.

Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG