AI Agents
Build AI agents from first principles — the think-act-observe loop, tools, memory, planning, reflection, and how to keep them safe — starting from 'what even is an agent?'
AI Agents: Zero to Mastery 🚀
Welcome! This is your complete, beginner-friendly course on AI Agents. By the end, you’ll understand how modern AI agents think, plan, use tools, remember, and work together — and you’ll be able to talk about (and build) them like an industry expert.
Who this is for: Total beginners. If you know what ChatGPT is and you can read a little bit of Python, you’re ready. Every term is explained in plain language with examples.
How to use this course
Read the modules in order. Each one builds on the last. Every module has:
- Plain-English explanations (no jargon without a definition)
- Real-world analogies (so the idea sticks)
- Diagrams in text (so you can see the flow)
- Code examples (simple, commented)
- “Check yourself” questions (to test understanding)
- Common mistakes (so you avoid them)
Take your time. It’s better to truly understand Module 2 than to rush to Module 8.
The Roadmap (Modules)
| # | Module | What you’ll learn | File |
|---|---|---|---|
| 1 | Foundations: What Is an AI Agent? | The difference between a chatbot and an agent, the agent loop, key vocabulary | 01_Foundations_What_Is_An_AI_Agent.md |
| 2 | The Brain: LLMs & Prompting for Agents | How the language model powers an agent, prompting basics, context windows | 02_LLMs_And_Prompting_For_Agents.md |
| 3 | Planning | How agents break big goals into steps, planning strategies | 03_Planning.md |
| 4 | Tool Use (Function Calling) | How agents use calculators, search, APIs, and code | 04_Tool_Use.md |
| 5 | Multi-Step Reasoning | ReAct, Chain-of-Thought, reasoning + acting loops | 05_Multi_Step_Reasoning.md |
| 6 | Agent Memory | Short-term vs long-term memory, vector databases, RAG | 06_Agent_Memory.md |
| 7 | Reflection & Self-Correction | How agents check and improve their own work | 07_Reflection_And_Self_Correction.md |
| 8 | Multi-Agent Systems | Teams of agents, roles, orchestration patterns | 08_Multi_Agent_Systems.md |
| 9 | Agent Frameworks & Tools | LangGraph, LlamaIndex, CrewAI, AutoGen, and more | 09_Frameworks_And_Tools.md |
| 10 | Evaluation, Observability & Debugging | How to know if your agent is actually good | 10_Evaluation_And_Observability.md |
| 11 | Safety, Guardrails & Security | Prompt injection, permissions, keeping agents safe | 11_Safety_Guardrails_And_Security.md |
| 12 | Building & Shipping a Real Agent | Putting it all together into a production project | 12_Capstone_Build_A_Real_Agent.md |
| — | Glossary | Every term, defined simply | 13_Glossary.md |
A 6-week study plan (suggested)
- Week 1: Modules 1–2 (foundations + the brain)
- Week 2: Modules 3–4 (planning + tools)
- Week 3: Module 5–6 (reasoning + memory)
- Week 4: Modules 7–8 (reflection + multi-agent)
- Week 5: Modules 9–10 (frameworks + evaluation)
- Week 6: Modules 11–12 (safety + capstone project)
Don’t worry if it takes longer. Mastery is not a race.
Before you start: a one-sentence definition
An AI agent is a system that uses a language model as its “brain” to decide, step by step, what actions to take to accomplish a goal — including using tools, remembering things, and adjusting its plan along the way.
If that sentence feels fuzzy right now, that’s totally fine. By the end of Module 1 it’ll click. Let’s go! 👉 Open 01_Foundations_What_Is_An_AI_Agent.md