90-Minute Complete Guide: From LLM Agent Basics to Practice – Learning AI Agents Through Hands-on Experience

Learn the principles of AI Agents that judge and act, in just 90 minutes.🤔

AI is now evolving from 'a tool that answers' to 'an agent that acts'.

This course is an introductory lecture that teaches the core principles and structure of LLM Agents through hands-on practice in just 90 minutes.

Without complex formulas or lengthy explanations,

You can directly check through code the process of AI making its own judgments and selecting and using the necessary tools.

Without complex theory, just 90 minutes is enough.

Please note that currently only Agent code practice lectures are provided, and lectures on AI LLM Agent theory are scheduled to be uploaded by November 2025!

Features of this course

📌 1-Hour Complete Hands-on Course

I've included only the essential core content. It's structured for learning by following along, without complex theory.

📌 Hands-on practice available with the latest models

This is a hands-on practice based on the latest LLM using Google Gemini API and ChatGPT API.

📌 From basics to practice all at once

LLM model loading → tool binding → custom tool registration → step-by-step practice up to graph design.

📌 Experience Various Agent Architectures

It covers the latest architectures including ReAct, conditional branching, memory, Human-in-the-loop, multi-agent collaboration, and more.

📌 Structural understanding that connects to real-world practice

You'll learn practical designs that can be immediately applied in real-world scenarios, including agent decision logic, data flow, and state management.

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After taking the course

• You will understand the logic of how LLMs make their own judgments and use tools.

• You can create your own AI agent directly with code.

• Learn the core flow and structural design methods of Agent logic based on LangChain / LangGraph.

• I clearly understand the concepts of AI agent memory, conditional branching, and collaboration systems.

• Get AI automation ideas that you can use immediately in your work.

  
  

You'll learn this kind of content.

The person who created this course

Hello, I'm Jin-gyu Lee, CEO of HappyAI, who is passionate about generative AI and LLM Agents.

I majored in Natural Language Processing and LLM at AI Graduate School, and since then have accumulated practical experience in LLM solution development, Private LLM construction, fine-tuning, and multimodal RAG by conducting over 200 AI·RAG projects with Samsung Electronics, Seoul National University, Korea Electric Power Corporation, and others.

Recently, I have been conducting numerous hands-on lectures on LLM-related topics such as RAG, Agent, and fine-tuning for leading domestic companies and public institutions.

This course is designed with a hands-on learning structure based on extensive practical experience, so that ❝ even beginners can easily learn and follow along with LLM Agents ❞ by quickly practicing only the essential concepts.

📌 Key Career Summary

• 2024~ CEO of HappyAI (Operating a company specializing in Generative AI and RAG)

• AI Graduate School PhD Coursework Completed (LLM & Natural Language Processing Major)

• Former invited researcher at Software Policy & Research Institute

• Former government-funded research institute researcher

• Over 200 LLM·RAG projects with practical experience

📚 Lecture and Activity Examples

• KT – LLM-based Agent LLM Development Course

• Samsung SDS – LangChain & RAG Hands-on Course

• Seoul Digital Foundation – LLM Theory and RAG Chatbot Development

In addition, I have conducted LLM big data lectures at numerous companies

Pre-enrollment Reference Information