#11 RAG vs. Agents
Every tech leader should know the differences between both elements
In one of my last conferences in Barcelona, I had the opportunity to talk in a room full of CTOs and CIOs that hit me with a surprising question:
"What’s the difference between Retrieval-Augmented Generation (RAG) and AI Agents?"
It’s more than a technical distinction: it proves how AI is evolving to reshape how we work, plan, and solve problems.
Let’s break it down.
First, What’s an LLM?
Before exploring RAG and Agents, we need to understand the foundation: large language models (LLMs).
Definition: LLMs are powerful AI systems trained to process and generate human-like text. Think of them as the world’s most knowledgeable writers, capable of answering questions, crafting content, or summarizing complex information.
Analogy: Imagine a genius with an incredible memory but no direct access to live or external data. They rely on what they’ve learned (training data) to provide answers.
LLMs are great at patterns, context, and creating coherent text, but they aren’t naturally connected to real-time, specific, or external knowledge.
What is RAG?
RAG (Retrieval-Augmented Generation) gives LLMs a bridge to external knowledge.
How It Works: Instead of relying only on what the LLM “knows” from training, RAG connects it to external data sources, like PDFs, databases, or live information.
Analogy: Picture a well-read expert consulting a library. They don’t guess; they verify.
Why It’s Useful:
More accurate and grounded responses.
Fewer “hallucinations” (wrong but confident-sounding answers).
RAG turns LLMs into informed advisors, delivering precise answers by referencing reliable sources.
What Are AI Agents?
Agents take things a step further. They don’t just answer questions, they act.
How They Work:
They use LLMs for reasoning but are equipped with tools (APIs, search engines, calendars, or workflows).
They adapt to new information, learn from past interactions, and execute multi-step tasks.
Analogy: Instead of a librarian who answers questions, think of an experienced guide. They not only give directions but also plan the trip, book the tickets, and adapt if the road is blocked.
Why It’s Useful:
Actionable intelligence: Agents don’t stop at answering, they solve problems.
Dynamic decision-making: They navigate complexity and orchestrate solutions in real-time.
RAG vs. Agents: A Simple Comparison
Why This Matters
Understanding this distinction is key for leaders looking to harness AI effectively:
RAG excels in precision. It’s your go-to for grounded, fact-based responses.
Agents shine in creativity and execution. They’re the backbone of dynamic problem-solving.
It’s like moving from having a brilliant advisor to building a whole team that thinks and acts.
Welcome to the era of actionable intelligence.