How Agentic AI is different from RAG

Artificial Intelligence (AI) is evolving quickly, and two of the most exciting developments in this field are AI Agents and Agentic AI. Many of us use AI daily in the form of chatbots like ChatGPT, Siri, or Google Assistant. But now, we're stepping into the next level where AI not only answers questions but does work on your behalf.

In this blog, we’ll understand what AI Agents and Agentic AI are, how they differ from traditional models like RAG (Retrieval-Augmented Generation), and explore some real-life examples. We will also see some Python code snippets to help you understand how Agentic AI can be implemented step by step.

What is Agentic AI?

Agentic AI is a broader concept where the AI doesn’t just respond, it thinks, plans, and acts.

For example:

You say: "Book me the cheapest flight to Delhi tomorrow morning."

An Agentic AI system will:

• Understand your query

• Open flight websites (like MakeMyTrip, Goibbo, etc.)

• Compare flight prices

• Show options to you

• Once you choose, it proceeds to book it

  
  

That’s the power of agency.

What is RAG (Retrieval-Augmented Generation)? 

Before Agentic AI, we had systems like RAG, which are still very powerful. 

RAG works like this: 

• You ask a question. 

• The system searches a database or document store for relevant information (this is called  retrieval). 

• Then it passes that information as context to a language model (like GPT).

• The model then gives you an answer. 

  
  

For example: 

You ask: “What’s the latest price of gold?” 

RAG will: 

• Search its connected database for gold prices 

• Pass that info to GPT 

• GPT replies with the answer 

  
  

It does not take any action like visiting websites or updating prices — it only provides answers based  on the data it can fetch.

Difference Between RAG and Agentic AI 

Let’s understand the difference through a simple example: 

User says: “Book a flight from Mumbai to Delhi for tomorrow.” 

RAG will: 

• Search its knowledge base for info related to flights 

• Respond with “You can book flights on websites like MakeMyTrip, Goibibo, etc.”

Agentic AI will: 

• Go to MakeMyTrip, Goibibo, etc. 

• Search for flights for tomorrow 

• Compare and show you options 

• Take your confirmation and then book the flight 

So in short: 

RAG retrieves → adds context → generates response 

Agentic AI plans → reasons → performs actions using tools

Architecture of RAG vs Agentic AI 

When you design AI systems, their architecture looks very different. 

RAG works in a straight line: 

Basic RAG architecture

Agentic AI has multiple steps and loops:

Basic Agentic AI Architecture

This is why Agentic AI feels more like a personal assistant not just a chatbot.

Are AI Agents and Agentic AI Different?

• They are closely related but not exactly the same. 

• AI Agent refers to the individual assistant that can think and act. 

• Agentic AI refers to the entire system or approach where AI has agency (freedom to act). So: 

• Siri, when updated to plan and act, becomes an AI Agent. 

• The system that allows Siri to think, reason, and act is Agentic AI. 

Where Can Agentic AI Be Used? 

Agentic AI is the future of automation. It can be used in: 

• Customer Support: Auto-handle complaints and process refunds. 

• Travel: Plan full trips, book tickets, hotels. 

• HR: Read resumes, schedule interviews, send offers. 

• Finance: Compare credit cards, auto-invest money. 

  
  

Agentic AI is like hiring a smart intern who cannot just talk, but actually get things done. While RAG  is useful for fetching and answering based on documents, Agentic AI brings us closer to true AI  assistants — ones who plan, reason, and act for us. 

Soon, we might not just “talk” to AI we’ll delegate full tasks to them.