As 2025 approaches, advancements in Retrieval-Augmented Generation (RAG) technology are gaining momentum, with a focus on creating more adaptable and autonomous AI systems. Researchers from Cleveland State University, Northeastern University, and MathWorks have introduced Agentic Retrieval-Augmented Generation (Agentic RAG), which enhances traditional RAG frameworks by integrating autonomous agents for dynamic data retrieval and workflow optimization. This innovation aims to overcome the limitations of static RAG systems, enabling AI to handle complex tasks more efficiently. Additionally, new frameworks such as MiniRAG are being developed to make RAG systems more accessible for edge devices and resource-constrained environments. Other emerging concepts include AirRAG, which utilizes Monte Carlo Tree Search for intrinsic reasoning, and FRAG, a flexible modular framework based on knowledge graphs. These developments suggest a transformative shift in how AI can autonomously execute tasks across various sectors, including medicine and engineering. The convergence of these technologies is expected to revolutionize the capabilities of AI, making systems like ChatGPT appear rudimentary in comparison.