Artificial Intelligence (AI) has rapidly evolved beyond just chatbots and text generators. If you’re someone who regularly uses AI tools like ChatGPT or Claude and wants to understand what’s really going on behind the scenes, especially when it comes to AI agents, this guide is for you.
In this post, we’ll simplify terms like “agentic workflows,” “multi-agent design patterns,” and “RAG” (Retrieval Augmented Generation) using real-world analogies and practical insights. We’ll explore three progressive levels of AI, from passive responders to autonomous multi-agent systems.
3 Levels of AI Pyramid
Level 1: Large Language Models (LLMs) — The Foundation
AI tools like ChatGPT, Gemini, and Claude operate on top of LLMs, which are trained to respond to prompts by generating text. This is what most people are familiar with.
Example: You ask ChatGPT to write a birthday message. It responds instantly with a warm, well-structured paragraph. But it has limits:
- LLMs can’t access real-time or personal data, such as your Google Calendar, unless they are explicitly integrated.
- LLMs are passive — they only act when prompted and don’t take initiative.
This is known as “non-agentic” or one-hot prompting, which involves a single prompt and a single response.