AI Agents Explained: From Chatbots to Autonomous AI Systems

Most explanations of AI agents are either too technical or too basic. This guide is designed for people who have zero technical background but use AI tools regularly and want to learn just enough about AI agents to understand how they affect their work and daily life.

We'll follow a simple learning path by building on concepts you already understand like ChatGPT, then moving on to AI workflows, and finally AI agents. All examples come from real-world situations you'll actually encounter. Those intimidating terms you see everywhere like RAG or ReAct are much simpler than you think.

‍

Level 1: Large Language Models - The Foundation

Popular AI chatbots like ChatGPT, Google Gemini, and Claude are applications built on top of large language models (LLMs). They excel at generating and editing text through a simple process: you provide an input, and the LLM produces an output based on its training data.

For example, if you ask ChatGPT to draft an email requesting a coffee chat, your prompt is the input and the resulting professional email is the output. Simple and straightforward.

However, if you asked ChatGPT when your next coffee chat is scheduled, it would fail because it doesn't have access to your calendar. This highlights two key traits of large language models:

- Despite being trained on vast amounts of data, they have limited knowledge of proprietary information like personal data or internal company information
- LLMs are passive - they wait for your prompt and then respond

Level 2: AI Workflows - Following Predefined Paths

Building on our calendar example, imagine telling an LLM: "Every time I ask about a personal event, perform a search query and fetch data from my Google calendar before providing a response."

With this logic implemented, when you ask "When is my coffee chat with my colleague?" you'll get the correct answer because the LLM will first search your Google calendar.

But here's the limitation: if your next question is "What will the weather be like that day?" the LLM fails because it's programmed to only search your Google calendar, which doesn't contain weather information.

This demonstrates the fundamental trait of AI workflows: they can only follow predefined paths set by humans. This path is also called the control logic.

You could expand this workflow by adding more steps - accessing weather via an API, using text-to-audio models to speak the answer. No matter how many steps you add, if a human is the decision maker, there's no AI agent involvement.

Real-World AI Workflow Example

Here's a practical AI workflow using make.com:

1. Compile links to news articles in Google Sheets
2. Use Perplexity to summarize those articles  
3. Use Claude with custom prompts to draft LinkedIn and Instagram posts
4. Schedule this to run automatically every day at 8 AM

This is an AI workflow because it follows a predefined path. If the LinkedIn post output isn't satisfactory, you'd manually rewrite the prompt for Claude through trial and error - a process currently done by humans.

Understanding RAG (Retrieval Augmented Generation)

RAG is a process that helps AI models look up information before they answer, like accessing your calendar or weather services. Essentially, RAG is just a type of AI workflow that retrieves relevant information to enhance responses.

Level 3: AI Agents - Autonomous Decision Making

Continuing with our social media example, as the human decision maker creating posts from news articles, you need to:

1. Reason about the best approach (compile articles, summarize them, write posts)
2. Take action using tools (Google Sheets, Perplexity, Claude)

The one massive change needed to transform this AI workflow into an AI agent is replacing you, the human decision maker, with an LLM.

The AI agent must:

- Reason: "What's the most efficient way to compile news articles? Should I copy and paste into a Word document? No, it's easier to compile links and use another tool to fetch data."
- Act: "Should I use Microsoft Word? No, Google Sheets is more efficient for inserting links into rows, especially since the user already connected their Google account."

The ReAct Framework

The most common configuration for AI agents is the ReAct framework - all AI agents must Reason and Act. This sounds simple once broken down.

A third key trait of AI agents is their ability to iterate autonomously. Instead of manually rewriting prompts to improve output, an AI agent would automatically add another LLM to critique its own work:

"I've drafted V1 of a LinkedIn post. How do I ensure it's good? I'll add another step where an LLM critiques the post based on LinkedIn best practices and repeat this until all criteria are met."

Real-World AI Agent Example

Consider an AI vision agent that searches video footage. When you search for "skier," the agent:

1. Reasons what a skier looks like (person on skis, moving fast in snow)
2. Acts by examining video clips to identify skiers
3. Indexes relevant clips and returns results

Instead of humans manually reviewing footage and adding tags like "skier," "mountain," "ski," "snow," the AI agent handles this entire process autonomously.

Summary: The Three Levels Explained

Level 1 (LLMs): You provide input, the LLM responds with output. Simple and direct.

Level 2 (AI Workflows): You provide input and tell the LLM to follow a predefined path that may involve retrieving information from external tools. The key trait is that humans program the path for the LLM to follow.

Level 3 (AI Agents): The AI agent receives a goal and the LLM performs reasoning to determine the best approach, takes action using tools, produces interim results, observes those results, decides if iterations are required, and produces a final output. The key trait is that the LLM becomes the decision maker in the workflow.

Understanding these distinctions helps you recognize which AI tools you're using and their capabilities. As AI agents become more sophisticated, they'll handle increasingly complex tasks with minimal human intervention, transforming how we work with artificial intelligence.

Level up your team's AI usage—collaborate with Promptus. Be a creator at https://www.promptus.ai