Preface

This article was written in collaboration with Claude 3.5 and ChatGPT 🙂

Introduction

Have you ever wondered how AI agents actually work? These digital helpers are becoming a big deal, showing up everywhere. But while they seem magical on the surface, their true power lies in the basics—how they’re built and how they operate under the hood.

In this article, we’ll take a deep dive into the core building blocks of AI agents, exploring what makes them tick. We won’t focus on building highly complex agents or crafting an entire library from scratch. Instead, we’ll start small, breaking the concept into manageable pieces and using those to construct a very simple agent.

Here’s the plan:

• We’ll examine the fundamental components of an agent, such as how it processes user input, decides on actions, and executes them
• We’ll discuss how to transform standard Python functions into powerful tools for your agent.
• Finally, we’ll bring everything together by building a basic agent to see these concepts in action.

By the end, you’ll have a hands-on understanding of how agents work, and you’ll be ready to explore more advanced concepts on your own. No Hogwarts-level wizardry required—just the magic of Python. Let’s get started!

Building Blocks

The building blocks for an agent are:

• an LLM with multimodal capabilities (audio, images, etc.).
• a set of tools (a.k.a. Python functions) that perform specific tasks. These can range from simple functions like def sum(a, b) to complex ones like def extremely_complicated_function(params).
• a system prompt that tells LLM what to do

Tools

To transform a “classic Python function” into an “Agent tool,” here’s what we need:

1. Define the function (duh!)
2. Make the function accessible to the LLM, including its name, arguments, docstring, and source code
3. Allow the LLM to autonomously call the function, enabling it to act as an Agent (because being an LLM in 2025 is just boring 🥱)