RAG in AI stands for Retrieval-Augmented Generation. It is a technique that gives AI models access to external information so it can answer questions more accurately. This because a model trained in 2023 doesn’t know the details of events that happened in 2024. The key word here is “Know”.
Agentic AI doesn’t just know, it can do, it has agency! It uses a reasoning loop (like ReAct or Chain-of-Thought) to figure out what it needs to do. Agentic AI can use RAG to look up information it doesn’t know (Agentic RAG), and then use that information to take actions or make decisions. Using MCP (Model Context Protocol), an agent AI is able to act on its knowledge that it has acquired either from its training data or from RAG lookups. The key word here is “Know” and “Do”.
For example, a customer support AI could work like this:
With RAG: It reads the company policy to see if a refund is allowed.
With MCP: It securely connects to the payment processor to issue the refund.
So, whats MCP?
Model Context Protocol (MCP) is an open-source, standardized communication framework designed to connect AI models (LLMs) to external tools, data, and systems
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Citation
@misc{kabui2026,
author = {{Kabui, Charles}},
title = {Agentic {AI} - {RAG} {Agents} with {MCP:} {Know} and {Do}},
date = {2026-01-05},
url = {https://toknow.ai/posts/rag-agents-mcp/},
langid = {en-GB}
}