Agentic AI – RAG Agents with MCP: Know and Do (toknow.ai)

A groundbreaking development in AI has emerged with the introduction of Agentic AI, which combines Retrieval-Augmented Generation (RAG) with Model Context Protocol (MCP) to create systems that not only possess knowledge but can also take action based on that knowledge. RAG enhances AI models by allowing them to access up-to-date external information, thereby improving their ability to answer queries accurately. Agentic AI elevates this capability by incorporating a reasoning mechanism that enables the system to determine the necessary actions it should take, making it more autonomous in its decision-making processes. The significance of this advancement lies in its potential applications, such as in customer support where an AI can utilize RAG to verify company policies and, via MCP, interact securely with external systems like payment processors to execute actions—like issuing refunds—efficiently. MCP serves as an open-source standard that facilitates communication between AI models and external tools, enhancing their operational scope. This combination of knowing and doing represents a substantial leap in AI's functionality, encouraging more sophisticated and interactive AI applications across various sectors, from customer service to enterprise automation.