Understanding AI Agents: A Simple Guide from Chatbots to Autonomous AI

Most explanations of AI agents are either too technical or too basic. This guide is designed for people who use AI tools regularly but want to understand how AI agents work without getting lost in complex jargon.

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We'll follow a simple three-level learning path, starting with concepts you already know and building up to AI agents.

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Level 1: Large Language Models (LLMs)

Popular AI chatbots like ChatGPT, Google Gemini, and Claude are built on large language models. These tools excel at generating and editing text through a simple process: you provide input, and the LLM produces output based on its training data.

For example, if you ask ChatGPT to draft a professional email, your request is the input, and the polished email response is the output. This works well for text generation tasks.

However, LLMs have two key limitations:

Limited Knowledge: Despite vast training data, they don't have access to your personal information, company data, or real-time information like your calendar.

Passive Nature: They wait for your prompt and respond, but can't take independent action.

Level 2: AI Workflows

AI workflows extend LLM capabilities by connecting them to external tools and data sources. Instead of just responding to prompts, they can follow predefined steps to complete tasks.

Consider this example: You tell an LLM, "Every time I ask about a personal event, search my Google calendar first, then provide a response." Now when you ask "When is my coffee chat with John?" the system will check your calendar before answering.

You could expand this workflow further by adding weather data through an API, so the system can also tell you the forecast for your meeting day.

The key characteristic of AI workflows is that they follow predefined paths set by humans. Even with hundreds of steps, if a human designed the decision-making logic, it's still just a workflow.

Retrieval Augmented Generation (RAG) is simply a type of AI workflow that helps AI models look up information before answering questions.

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Real-World Workflow Example

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

Step 1: Compile news article links in Google Sheets
Step 2: Use Perplexity to summarize the articles  
Step 3: Use Claude to draft LinkedIn and Instagram posts
Step 4: Schedule to run automatically daily at 8 AM

This follows a predefined path: do this, then this, then this. If the output isn't satisfactory, a human must manually adjust the prompts and iterate.\

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Level 3: AI Agents

The fundamental difference between AI workflows and AI agents is decision-making authority. While workflows follow human-designed paths, AI agents make their own decisions about how to achieve goals.

An AI agent must be able to:

Reason: Think about the best approach to solve a problem
Act: Use tools to take action toward the goal
Iterate: Evaluate results and improve autonomously

Using our social media example, instead of following predetermined steps, an AI agent would:

Reason about the most efficient way to compile news articles
Decide which tools to use (Google Sheets vs. Word vs. Excel)
Evaluate its own output and iterate until meeting quality standards

The REACT Framework

Most AI agents use the REACT framework, which stands for Reason and Act. This simple structure captures the essence of autonomous AI behavior.

A key advantage of AI agents is autonomous iteration. Instead of requiring human intervention to improve outputs, agents can critique their own work and refine results automatically.

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Real-World AI Agent Example

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Andrew Ng created a demo showing an AI vision agent that can search video footage. When you search for "skier," the agent:

Reasons what a skier looks like
Acts by analyzing video clips
Identifies and indexes relevant footage
Returns the appropriate clips

The agent completes this entire process autonomously, without human pre-tagging or manual review.

Summary: Three Levels of AI Capability

Level 1 - LLMs: You provide input, the model responds with output
Level 2 - AI Workflows: You provide input and predefined steps; the system follows your programmed path using external tools
Level 3 - AG Agents: You provide a goal; the AI reasons about the best approach, takes action using tools, evaluates results, iterates as needed, and produces the final output

The crucial distinction is decision-making: workflows follow human logic, while agents make autonomous decisions to achieve objectives.

Understanding these differences helps you choose the right AI approach for your needs and better evaluate AI tools as they continue evolving.

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