🤖 RAG vs AI Agents: What's the Difference?

RAG and AI Agents are two important concepts in modern Generative AI and Agentic AI. But are they the same? Can an AI Agent use RAG?

In this video, we explain the difference between RAG and AI Agents using simple real-world examples and architecture diagrams.

🔥 In This Video

• What is RAG?
• What is Retrieval-Augmented Generation?
• How does a RAG system work?
• RAG architecture
• What is an AI Agent?
• How AI Agents reason, decide, and take actions
• RAG vs AI Agents
• When should you use RAG?
• When should you use an AI Agent?
• Can an AI Agent use RAG?
• How RAG can become a capability inside an AI Agent
• Real-world AI Agent examples
• RAG + Tools + Memory + AI Agent

🧠 THE KEY DIFFERENCE

RAG → Retrieves relevant knowledge

AI Agent → Reasons, decides, and takes actions to achieve a goal

And importantly:

AI Agent + RAG + Tools
↓

Powerful Agentic AI System

RAG and AI Agents are NOT competing technologies.

An AI Agent can use RAG as one of its capabilities.

📚 Example Discussed

An enterprise AI Agent can use:

🔹 RAG → Search company policies
🔹 Database → Get employee information
🔹 Tools → Perform calculations
🔹 Email → Send requests
🔹 MCP → Connect to external tools and systems

This combination allows AI Agents to access knowledge, make decisions, and perform actions.

🚀 Part of the Agentic AI Learning Series

This video is part of our Agentic AI learning series on SomethingTalk1.

🔗 Learn more at: Teltam.in

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• Artificial Intelligence
• Generative AI
• Agentic AI
• AI Agents
• MCP
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• Python
• AI Engineering
• AI Tools

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