Stage 1: Foundations

Stage 1: Foundations

Welcome to the beginning of your journey into AI agents. I have designed this stage for people who have never written a line of code, never opened a terminal, and think machine learning is something that happens in factories. By the end of these 20 hours, you will have run your first AI model on your own computer and had a conversation with it.

Module 1.1: Understanding AI (4 hours)

Learning Objectives

By the end of this module, you will be able to:

1. Define artificial intelligence, machine learning, and deep learning
2. Explain how neural networks process information (at a conceptual level)
3. Identify AI applications in everyday life
4. Distinguish between narrow AI, general AI, and superintelligence
5. Assess claims about AI capabilities critically

1.1.1 What is Artificial Intelligence?

Let me start with what AI is not. AI is not a conscious being. It does not think the way you think. It does not have feelings, desires, or goals of its own. Understanding this from the start will help you build better systems and avoid common mistakes.

The Human Analogy

Think of AI like teaching a child to recognise cats. You do not give the child a rulebook saying "cats have four legs, whiskers, and pointy ears." Instead, you show them thousands of pictures of cats and non-cats until they develop an internal understanding of what makes something a cat.

This is precisely how modern AI works. Instead of programming explicit rules (if whiskers AND four legs AND pointy ears THEN cat), we show the AI millions of examples and let it discover the patterns itself.

What AI Can and Cannot Do (As of 2026)

I think it is important to be honest about current capabilities. AI has made remarkable progress, but it also has significant limitations.

1.1.2 Key Terminology

Before we go further, let me define some terms you will encounter throughout this course.

1.1.3 Common Misconceptions

I want to address some things you may have heard about AI that are not quite accurate.

Module 1.2: From LLMs to Agents (4 hours)

Learning Objectives

By the end of this module, you will be able to:

1. Explain what Large Language Models are and how they work
2. Distinguish between chatbots, LLMs, and AI agents
3. Understand why agents represent an evolution beyond chatbots
4. Identify use cases where agents are more appropriate than simple LLMs

1.2.1 Understanding Large Language Models

A Large Language Model (LLM) is like an incredibly well-read assistant who has consumed most of human knowledge available on the internet. When you ask it a question, it does not "look up" the answer. It predicts what text should come next based on patterns learned during training.

Key LLM Characteristics:

• Stateless: Each conversation starts fresh unless you provide the history
• Reactive: They respond to prompts but do not initiate action
• Knowledge-bounded: Limited to what was in their training data
• Single-turn focus: Optimised for question-answer exchanges

1.2.2 What Makes an AI Agent Different?

Here is where things get interesting. An AI agent is not just a chatbot that can answer questions. It is a system that can:

1. Perceive: Sense and understand its environment
2. Reason: Plan and make decisions
3. Act: Execute tasks in the real world

The difference is action. The agent does not just tell you it cannot help. It goes and gets the information.

🎯 Interactive: Explore the Differences

Use this interactive tool to explore the fundamental differences between LLMs, chatbots, and AI agents. Click on each concept to compare their capabilities, test your understanding with scenarios, and see concrete examples.

1.2.3 The Anatomy of an AI Agent

Every AI agent has four core components:

🎯 Interactive: ReAct Pattern in Action

The ReAct pattern is fundamental to how modern AI agents work. This simulator shows you step-by-step how an agent thinks through a problem.

P(action | state) = softmax(W × [thought_embedding, context])

Where:
  softmax(x_i) = exp(x_i) / Σ exp(x_j)  // Normalises to probability
  W = learned weight matrix
  
Decision threshold: P > 0.85 → execute action

Module 1.3: Your Computer's Command Line (4 hours)

Learning Objectives

By the end of this module, you will be able to:

1. Open a terminal on Windows or macOS
2. Navigate the file system using basic commands
3. Run Python scripts from the command line
4. Understand what environment variables are

1.3.1 Why Learn the Command Line?

I know the command line can look intimidating. A black screen with blinking text feels like something from a 1980s film. But here is the truth: the command line is the most powerful way to interact with your computer, and almost all AI development happens here.

1.3.2 Opening Your Terminal

On macOS:

1. Press Cmd + Space to open Spotlight
2. Type "Terminal"
3. Press Enter

You will see a window with text ending in a $ or % symbol. This is called the prompt. It is waiting for your command.

On Windows:

1. Press Win + X
2. Select "Windows Terminal" or "PowerShell"

You will see a window with text ending in >. This is your prompt.

1.3.3 Essential Commands

Here are the commands you will use most often. I am giving you both macOS/Linux and Windows versions.

Seeing where you are:

# macOS/Linux
pwd

# Windows
cd

This shows your current directory (folder). When you first open the terminal, you are usually in your home directory.

Listing files:

# macOS/Linux
ls

# Windows
dir

This shows all files and folders in your current location.

Changing directories:

# Both platforms
cd Documents

# Go up one level
cd ..

# Go to home directory
cd ~

Creating a folder:

# macOS/Linux
mkdir my-ai-project

# Windows
mkdir my-ai-project

Creating a file:

# macOS/Linux
touch hello.py

# Windows
echo. > hello.py

Module 1.4: Setting Up Your Environment (4 hours)

Learning Objectives

By the end of this module, you will be able to:

1. Install Python on your computer
2. Create and activate virtual environments
3. Install Ollama for local AI model inference
4. Set up VS Code for AI development

1.4.1 Installing Python

Python is the language of AI development. Almost every AI framework, library, and tool is written in or has bindings for Python. Let us install it.

On macOS:

# Check if Python is already installed
python3 --version

# If not, install using Homebrew
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
brew install python@3.12

On Windows:

1. Go to python.org/downloads
2. Download Python 3.12 or later
3. Run the installer
4. Important: Check "Add Python to PATH" before clicking Install

Verify the installation:

python3 --version
# Should output something like: Python 3.12.x

1.4.2 Virtual Environments

A virtual environment is an isolated space for your project's dependencies. Without it, different projects might conflict with each other.

Creating a virtual environment:

# Navigate to your project folder
cd my-ai-project

# Create the environment
python3 -m venv venv

# Activate it (macOS/Linux)
source venv/bin/activate

# Activate it (Windows)
.\venv\Scripts\activate

When activated, you will see (venv) at the start of your prompt. This means you are inside the virtual environment.

1.4.3 Installing Ollama

Ollama lets you run powerful AI models locally on your own computer. No API keys needed. No data leaving your machine. Free and private.

On macOS:

# Using Homebrew
brew install ollama

# Or download from ollama.com

On Windows:

1. Go to ollama.com/download
2. Download and run the Windows installer
3. Follow the setup wizard

Verify the installation:

ollama --version

Download your first model:

# Start the Ollama server (runs in background)
ollama serve

# In a new terminal, download a model
ollama pull llama3.2:3b

This downloads the Llama 3.2 model with 3 billion parameters. It is about 2GB and runs well on most modern computers.

Module 1.5: Your First AI Interaction (4 hours)

Learning Objectives

By the end of this module, you will be able to:

1. Run a local LLM using Ollama
2. Have a conversation with an AI model
3. Write and run a Python script that talks to an LLM
4. Understand what prompts are and how to craft them

1.5.1 Chatting with Ollama

Let us have your first conversation with a local AI model.

Start a chat:

ollama run llama3.2:3b

You will see a prompt like >>>. Type your message and press Enter.

>>> Hello! Can you explain what you are?

The model will respond. Try a few more questions:

• "What is the capital of France?"
• "Write a haiku about programming."
• "Explain machine learning to a 10-year-old."

To exit, type /bye or press Ctrl+D.

1.5.2 Your First Python Script

Now let us write a Python script that talks to Ollama. This is your first step toward building AI agents.

Create the file:

cd my-ai-project
touch first_llm.py

Write the code:

"""
My First LLM Interaction
=========================
A simple script that sends a prompt to a local LLM
and prints the response.

Author: [Your Name]
Date: January 2026
"""

import requests
import json


def ask_llm(prompt: str, model: str = "llama3.2:3b") -> str:
    """
    Send a prompt to the local Ollama server and get a response.
    
    Args:
        prompt: The question or instruction to send
        model: Which model to use (default: llama3.2:3b)
        
    Returns:
        The model's response as a string
    """
    response = requests.post(
        "http://localhost:11434/api/generate",
        json={
            "model": model,
            "prompt": prompt,
            "stream": False
        }
    )
    
    if response.status_code == 200:
        return response.json().get("response", "No response received")
    else:
        return f"Error: {response.status_code}"


def main():
    """Main function to demonstrate LLM interaction."""
    print("Welcome to your first LLM interaction!")
    print("=" * 50)
    print()
    
    # Ask the model a question
    question = "What are three interesting facts about the moon?"
    
    print(f"Question: {question}")
    print()
    print("Thinking...")
    print()
    
    answer = ask_llm(question)
    
    print("Answer:")
    print(answer)


if __name__ == "__main__":
    main()

Install the required package:

pip install requests

Run the script:

python first_llm.py

Congratulations! You have just written a program that talks to an AI. This is the foundation of everything we will build.

1.5.3 Understanding Prompts

A prompt is the text you send to an AI model. Crafting good prompts is a skill that takes practice. Here are some principles:

🎯 Interactive: Build Your Own Tool Schema

AI agents use tools by calling functions with specific parameters. Understanding how to design clear tool schemas is essential for building reliable agents. Use this interactive builder to explore and create tool definitions.

Stage 1 Assessment

Summary

In this stage, you have learned:

1. What AI really is: Pattern matching at scale, not consciousness or understanding

2. The difference between LLMs and Agents: Agents can perceive, reason, and act. LLMs can only generate text.

3. Command line basics: Navigating your file system and running commands

4. Environment setup: Python, virtual environments, and Ollama

5. Your first AI interaction: Talking to a local model and writing Python code to do it programmatically