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Mastering AI Agents: Practical Development of LangGraph, RAG, MCP, and Multi-Agents

In the era of generative AI, simply writing prompts is not enough to solve real-world business problems. In actual services, you must be able to design AI systems that can make autonomous decisions, call tools, retrieve data, and facilitate collaboration between multiple agents. This course is a practice-oriented program designed for everyone, from developers designing AI agents for the first time to engineers looking to build production-ready systems. You will learn step-by-step, covering agent architecture design using LangGraph, State Management, Tool Calling, API and MCP integration, natural language database control, RAG implementation, semantic search, Few-shot Learning, Reflection, and Fine-tuning. Furthermore, you will master multi-agent collaboration structures, Supervisor patterns, Actor-Critic strategies, asynchronous communication, automated evaluation systems, and Quality Gate construction through hands-on projects. Finally, you will learn MLOps and AI Agent operation strategies essential for real-world production environments, including Metrics/Logs/Tracing-based monitoring, Shadow Deployment, Canary Deployment, Drift Detection, RCA automation, and A/B testing. By the end of this course, you will be able to design, develop, and operate high-performance AI systems applicable to real-world services using the latest AI agent technologies.

7 learners are taking this course

Level Basic

Course period Unlimited

Python
Python
LangGraph
LangGraph
AI Agent
AI Agent
RAG
RAG
mcps
mcps
Python
Python
LangGraph
LangGraph
AI Agent
AI Agent
RAG
RAG
mcps
mcps

What you will gain after the course

  • Agent Architecture Design and UX Implementation - Model, Tool, and State Management Design, LangGraph Hands-on, Validation Strategies, and Text/Graphic/Voice UX Implementation

  • RAG and Learning Techniques - Mastering Rolling Context, Semantic Search, Chunking, RAG Pipeline Construction, and Few-shot, Reflection, and Fine-tuning

  • Monitoring and Operations Optimization - Metrics, Logs, Traces, Shadow and Canary Deployment, Drift Detection, RCA Automation, AB Test Decision Making

  • Tool Integration and Orchestration - Local/API/MCP tool integration, natural language DB control, semantic and hierarchical tool selection, and execution topology design

  • Multi-Agent Collaboration and Evaluation - Specialist/Supervisor Design, Coordination Strategies, Asynchronous Communication, Automated Scoring, and Quality Gate Construction

AI Agent Perfect Mastery

From practical development to operation of LangGraph, RAG, MCP, and Multi-Agents

AI agents utilizing LLMs have now evolved beyond simple chatbots to become a core technology that automates actual services and performs tasks.

In this course, you will learn the entire process of AI agent development through hands-on practice, covering everything from AI agent design using LangGraph to Tool Calling, MCP (Model Context Protocol), RAG (Retrieval-Augmented Generation), multi-agent collaboration, and real-world operations (Monitoring & Deployment).

You will build AI agents based on real-world projects and learn how to connect search, databases, APIs, and external tools, as well as how to design systems where multiple agents collaborate.

Additionally, it covers MLOps and AI operation strategies used in actual service operations, such as Shadow Deployment, Canary Deployment, Drift Detection, RCA, and A/B Testing, so you can apply them directly in practice.

This course is a practical program designed not only for AI Engineers, Backend Developers, Software Engineers, MLOps Engineers, and AI Product Managers but also for any developer looking to build AI services themselves.

What You’ll Learn

① AI Agent Design and UX Implementation

What You’ll Learn

  • Understanding the core concepts of AI agents

  • Building LangGraph-based workflows

  • State Management

  • Tool Calling

  • Agent Validation Strategies

  • Text UX

  • Streamlit-based GUI

  • Voice UX Implementation

② Tool Integration & Orchestration

Learning Content

  • Local Tool Integration

  • REST API Integration

  • MCP(Model Context Protocol)

  • MySQL Natural Language Query

  • Multi-MCP

  • Human Approval

  • Orchestration Pattern

  • Chain

  • Parallel

  • Single Agent Workflow

③ RAG & AI Learning Techniques

Learning Content

  • Rolling Context Window

  • Semantic Search

  • Embedding

  • Vector Search

  • Chunking

  • Building a RAG Pipeline

  • Few-shot Learning

  • Reflection

  • Fine-tuning

④ Multi-Agent System

Learning Content

  • Single Agent

  • Multi-Agent

  • Specialist Agent

  • Supervisor Agent

  • LangGraph Workflow

  • Actor-Critic

  • Democratic Collaboration

  • Message Broker

  • Asynchronous Communication

  • Quality Gate

⑤ Operations and Optimization

Learning Content

  • Monitoring

  • Metrics

  • Logs

  • Tracing

  • Dashboard

  • Alert

  • Shadow Deployment

  • Canary Deployment

  • Drift Detection

  • RCA

  • A/B Testing

Before You Enroll

Information Before You Enroll

Prerequisites

It will be easier to learn if you are already familiar with the following content.

  • Basic Python Syntax

  • Functions and classes

  • Basic API concepts

  • Basic Database Knowledge (SQL)

  • Basic understanding of LLMs (OpenAI, Claude, Gemini, etc.)

※ It is not mandatory, and necessary concepts will be explained during the lecture.


Learning Environment

Recommended Development Environment

  • Python 3.11 or higher

  • VS Code

  • Jupyter Notebook

  • LangGraph

  • OpenAI API or compatible LLM API

  • MySQL

  • Streamlit


Course Curriculum

  • Total of 5 parts

  • 45 lectures

  • Approximately 32 hours

  • Practice-oriented

  • Project-based learning


Questions and Feedback

If you have any questions during the course, please feel free to ask them through the Q&A board at any time.

We will respond as quickly as possible, and frequently asked questions will be continuously reflected in the course materials.


Update Notice

AI technology is evolving rapidly.

We plan to continuously update the course as new LangGraph features, MCP updates, and the latest AI Agent technologies and practical use cases are added.


Recommended for the following people

  • Developers who want to develop AI agents from scratch themselves

  • Those who want to learn LangGraph properly

  • Those who want to build RAG at a professional/practical level

  • Those who want to create AI systems using MCP

  • Those who want to design multi-agent systems

  • Engineers who want to deploy AI services to actual production environments

  • Any developer who wants to apply the latest AI Agent technology to their projects

Recommended for
these people

Who is this course right for?

  • AI engineers and software developers who will directly implement and operate AI agents

  • Tech leaders and PMs who need to determine the tech stack and automation scope

  • Developers seeking to introduce multi-agents to go beyond the limitations of a single agent

  • Practitioners who want to operate agents in real-world server environments and optimize them based on data

Need to know before starting?

  • Python programming experience (at a level where basic syntax and libraries can be used)

  • Experience using LLM APIs (not required)

  • Understanding basic concepts of databases and APIs (advantageous for learning related content)

  • A computer with a development environment configured

Hello
This is eunj45339

As a computer education instructor, I specialize in teaching computer basics, C++ programming, Microsoft Excel, and the fundamentals of machine learning. I explain concepts easily and systematically through a step-by-step teaching method that is easy for beginners to intermediate learners to understand, providing practice-oriented learning. My goal is to help all learners build a solid foundation and develop the skills to apply them to real-world projects.

Curriculum

All

45 lectures ∙ (32hr 4min)

Course Materials:

Lecture resources
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