Learn Agentic AI by Building One — A Hands-On Guide - Introduction
Introduction to how Agentic AI works behind the scene
You've probably heard the term "agentic AI" thrown around a lot lately. AI agents that can reason, use tools, and take actions autonomously. It sounds complex, but the core ideas are surprisingly simple once you see them in code.
This guide breaks down agentic AI into 7 progressive lessons, each one a runnable Python script. No frameworks, no abstractions — just raw API calls so you can see exactly what's happening under the hood. By the end, you'll have built a working AI agent from scratch and understood every piece of the stack.
Who is this for?
Anyone curious about how AI agents actually work. You don't need ML experience. If you can read Python and have used an API before, you're good.
What you'll build
Starting from a single API call, you'll progressively layer on concepts until you have a fully autonomous agent that can reason through multi-step problems, call tools, and maintain conversation context — the same patterns used by enterprise AI assistants.
Prerequisites
pip install boto3
You'll need AWS credentials configured with access to Amazon Bedrock (Claude). The scripts use us-east-1 by default.
The Lessons
| Lesson | What you'll learn |
|---|---|
| Lesson 1 - What is an LLM? Making Your First AI Call | An LLM is just a text-in, text-out function. Make your first call and see what it can and can't do. |
| Lesson 2 - Prompt Engineering — Shaping AI Behavior | The same model behaves completely differently based on how you ask. System prompts are the secret sauce behind every AI product. |
| Lesson 3 - Structured Output — Getting JSON from AI | AI doesn't have to return paragraphs. Teach it to return JSON so you can use it as a smart function inside your code. |
| Lesson 4 - Memory — Giving AI Conversation History | LLMs are stateless — they forget everything between calls. "Memory" is just you sending the full conversation history each time. |
| Lesson 5 - Tools — Letting AI Call Functions | The big unlock. Describe your functions to the AI, and it decides when to call them. The AI never runs code — it asks you to. |
| Lesson 6 - The Agent Loop — Think → Act → Observe → Repeat | The agent loop: Think → Act → Observe → Repeat. This is the pattern that turns a chatbot into an agent. |
| Lesson 7 - A Real Agent — Multiple Tools, Reasoning, and Autonomy | Put it all together. A real agent with multiple tools, conversation memory, and autonomous multi-step reasoning. |
Code Repository
The code used for this guide can be found here ->. Each script is self-contained with detailed comments explaining every concept discussed in this guide.
Read the code, run it, tweak it. The comments are written to be read top-to-bottom like an article — the code is the explanation.
The big picture
AI agents — from simple chatbots to autonomous coding assistants — are built from the same five building blocks:
┌─────────────────────────────────────────────────┐
│ THE AGENTIC AI STACK │
│ │
│ PERSONA → System prompt shapes behavior │
│ MEMORY → Conversation history as context │
│ STRUCTURE → JSON output for app integration │
│ TOOLS → Functions the AI can request │
│ LOOP → Think → Act → Observe → Repeat │
│ │
│ Combine them all = an AGENT │
└─────────────────────────────────────────────────┘
The difference between products is just the tools, the prompts, and the scale. The patterns are the same. Once you see them, you can't unsee them.
- Language
- English
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