[Season 2] Spring AI in Practice: Developing Multi-AI Agent Systems

Building an 'Intelligent Collaboration' Expert Agent Team Using Spring AI Router Pattern + RAG + MCP Beyond a Single Agent to Architecture: The Standard for Router Patterns and Agent Isolation Design

(5.0) 9 reviews

164 learners

Level Basic

Course period Unlimited

Spring Boot
Spring Boot
RAG
RAG
AI Agent
AI Agent
Spring AI
Spring AI
Model Context Protocol
Model Context Protocol
Spring Boot
Spring Boot
RAG
RAG
AI Agent
AI Agent
Spring AI
Spring AI
Model Context Protocol
Model Context Protocol

Reviews from Early Learners

5.0

5.0

bigho98

100% enrolled

It was fascinating and fun to learn that you can create multiple agents with Spring. It was an opportunity to gain a lot of insights into the related structure and code. Thank you for the high-quality lecture.

5.0

문석청

30% enrolled

Thank you for the great lecture.

5.0

em241101

30% enrolled

Thank you for the detailed explanation.

What you will gain after the course

  • Designing a Spring AI Multi-Agent System: Learn how to build a team of expert agents (Reservation, Sommelier, Concierge) that efficiently handles complex business logic by applying the Router Pattern.

  • Implementing Enterprise-Grade Architecture: Designing a secure and scalable practical backend system that moves beyond the limitations of a single agent through **Role Separation (Router-Worker)** and **Tool Isolation**.

  • Practical Application of RAG & MCP: Enhance the quality of AI services by directly implementing **Intelligent Menu Recommendation (RAG)** using Vector DB and a real-time admin notification system integrated with Slack MCP.

[Season 2] Spring AI in Practice: Developing Multi-AI Agent Systems

Enterprise-grade AI agents,
how far have you gone in building them?

Learn how to build an intelligent collaborative AI agent system that handles complex business logic
using Spring AI Router Pattern, RAG, and MCP.

Have you ever felt that a single chatbot is not enough to control complex business logic?

Are you struggling with prompt engineering because it's difficult to get accurate answers due to ChatGPT's hallucination phenomenon?

Beyond RAG and Tool Calling, do you need experience building 'autonomously collaborating agent systems' that can be applied to actual services?

Through this course, you can strengthen your capabilities in designing and implementing AI agent architectures required in actual enterprise environments and complete your ability to build professional-level AI systems applicable to real-world tasks.

The book "Do it! Spring AI," which creates explosive synergy when paired with the Spring AI lecture, has been published (2026-06-15)

🛒 Major Bookstore Links

After learning the practical development process of building a multi-AI agent system
using Spring AI router patterns, RAG, and MCP,

You will be transformed into an expert who can personally design and operate a 'collaborative AI team' that goes beyond simple chatbots.

By the end of this course, you will

You will be able to build and operate multi-AI agent systems yourself.

  • Beyond the limitations of a single chatbot, you will gain experience designing and implementing a team of expert agents (Reservation, Sommelier, Concierge) that handle complex business logic using Spring AI's Router Pattern and the MCP protocol.

Go beyond RAG and Tool Calling to complete a practical architecture for 'Collaborative AI.'

  • Beyond simple LLM integration, you will build a secure and scalable production-grade backend system through role separation (Router-Worker) and tool isolation. Through this, you will learn how to realize the true potential of AI agents.

Integrate and implement a vector DB-based intelligent recommendation system and a real-time notification system.

  • You will directly implement an intelligent menu recommendation (RAG) system using MariaDB Vector DB and a real-time administrator notification system integrated with Slack MCP. Through this, you will enhance the quality of AI services and improve your ability to solve real-world business problems.


As a senior developer, you will acquire expertise in designing and building AI-based systems.

  • You will acquire the latest technology trends in the field of AI engineering and grow into a key talent capable of successfully leading complex AI projects by strengthening your skills in multi-agent system design, RAG, and MCP utilization.

✔️

Spring AI Multi-Agent
New Horizons in Development

Building Multi AI Agents based on
Spring AI and Router Pattern

In this course, we will cover in detail how to use Spring AI to go beyond the limitations of a single agent and build a team of expert agents that handle complex business logic through Router Pattern and agent isolation design. You will learn and directly implement the core principles for designing enterprise-grade, practical backend systems.

Building Effective Agents

https://docs.spring.io/spring-ai/reference/api/effective-agents.html(Refer to official documentation)

Agentic Systems (Routing Workflow)

Implementing a Real-time
AI Collaboration System using RAG and MCP

Enhance the quality of AI services by directly building an intelligent menu recommendation (RAG) system using Vector DB and a real-time admin notification system through Slack MCP integration. You will gain practical experience in implementing complex collaboration scenarios with a team of AI agents.

Retrieval Augmented Generation

https://docs.spring.io/spring-ai/reference/concepts.html (Refer to official documentation)

Spring AI Slack MCP Server Integration

Slack Real-time Admin Notification Service

Core Technologies and
Hands-on Code Provided

We provide all the source code and configurations necessary to build a functional multi-AI agent system based on the latest technology stack, including Spring Boot, Spring AI, AI Agent, MCP, and RAG. This allows you to immediately apply and scale what you have learned in real-world practical applications.

📚 Spring AI Multi-Agent
System Architecture Design

Season 2 Introduction and Development Environment Setup

In this section, we introduce the overview of Season 2 for developing enterprise-grade multi-AI agent systems using Spring AI. We emphasize the necessity of multi-agent architecture for processing complex business logic and cover the development environment setup in detail, including project creation, Docker-based MariaDB VectorDB installation, and Slack MCP server and App integration.

Development Environment
IntelliJ IDEA, Spring AI, Spring Boot, JPA, Docker, MariaDB, Slack

Data Modeling and DTO Design

This section lays the foundation for building an AI agent system. You will learn how to design entities based on master and relational tables, define DTOs (Records) for efficient data communication with AI, and design repositories using JPA.

ERD (Entity-Relationship Diagram)

Logical structure of Entity

Implementation of Business Logic and Tools

Learn the process of implementing core business logic such as reservation and order processing. Develop ReservationService and OrderService, and based on these, design and implement ReservationTools and SommelierTools that can be utilized by AI agents.

Tool Calling

Vector DB-based Recommendation System (RAG)

Build an intelligent recommendation system using Retrieval Augmented Generation (RAG) technology. Load and embed menu description data to construct a VectorDB, and generate dummy data for testing to understand and practice how the RAG system operates.

MariaDB VectorDB

Multi-Agent System Architecture Design

Learn the essence of multi-agent system architecture design that goes beyond the limitations of a single agent. We will cover in-depth the AiConfig setup including ChatModel and ChatMemory configurations, the Router Agent responsible for routing based on user intent, the Orchestrator that coordinates the overall flow, and the design of each agent (ReservationAgent, SommelierAgent, ConciergeAgent).

Router Agent Pattern

Prompt Engineering and Final Integration

We will focus on learning prompt engineering to define the core logic and safety guardrails for each agent. We will design system prompts (.st) for the reservation, recommendation/ordering, and information agents, implement controllers for external API integration, and finally integrate and test the complete system.

Practical Test

Testing in Backend Postman

Frontend Testing

Node.js, VS Code, React.js, JavsScript, Tailwind CSS, Vite Tool

We can solve the concerns
of people like this!

📌

Senior Backend Developer

Those who want to control complex business logic with AI beyond the limitations of a single chatbot, but feel overwhelmed by the actual implementation
Those who want to build enterprise-grade AI systems through Router Pattern and Agent Isolation design

📌

AI Engineer

Those who are having difficulty designing and building systems where multiple AI agents collaborate autonomously, going beyond RAG and Tool Calling
Those who want to strengthen their capabilities in designing multi-AI agent system architectures that can be utilized in actual service environments

📌

New AI Service Planner

Those who want to enhance the competitiveness of existing services or devise new business models using AI agent technology
Those who want to review the feasibility of practical service implementation through cases of building intelligent collaborative agent teams based on Spring AI

Notes before taking the course

Practice Environment

💻 Development Environment

  • IDE: IntelliJ IDEA Community Edition.

  • Language: Java 17 or 21.

  • Framework: Spring Boot 3.5.8 (Latest Stable).

  • Library: Spring AI 1.0.3 (or 1.1.0 Snapshot).

  • Database: MariaDB 11.8.

  • AI Model: OpenAI (gpt-4o-mini or gpt-5-mini).

  • Container : Docker Desktop

Prerequisites and Important Notes

  • Java: Understanding of basic Java syntax (Java 17+ recommended).

  • Spring Boot: Basic usage of DI/IoC, JPA (Repository), and Controller.

  • Database: Basic understanding of SQL (SELECT, JOIN concepts).

Learning Materials

  • The source code (Backend, Frontend) is provided in Lecture 30 at the very end of the video course.

  • Lecture materials are provided as PDF files.

  • The source code is provided via Github.

✏Questions & Inquiries

If there are any parts you don't understand while studying, please feel free to ask immediately using the Q&A board or the 1:1 open chat room.

👩‍🎓Spring AI Hands-on (1:1 Open Chat) : https://open.kakao.com/o/sXXxSI5h

Recommended for
these people

Who is this course right for?

  • Senior developers who have hit the limits of single chatbots and want to control complex business logic with AI

  • AI engineers who want to go beyond RAG and Tool Calling to create 'autonomously collaborating agent systems'

Need to know before starting?

  • Basic knowledge of the Java programming language is required.

  • A basic understanding of the Spring Boot framework is recommended.

  • Basic knowledge of databases and SQL is helpful.

Hello
This is bitcocom

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Hello, I am instructor Park Mae-il.
I run an SW education center and provide consulting and commissioned SW training for universities, government offices, and corporations.


📄 Major teaching experience and many others

- Goorm Specialized High School Major Camp Lectures (Full Stack Course)
- Software Meister High School Industry-Academic Cooperation Teacher
- Gwangju Artificial Intelligence Academy Lectures
- Fast Campus Backend Bootcamp Lectures
- Smart Human Resources Development Center Education Director and Lecturer
- Korea Electric Power Corporation (KEPCO) In-House Coding Entrusted Education
- Hanyang University ERICA Online Lectures
- Bit Software Education Center Operation (Overseas Employment, Government-funded Education)
- SW Recruitment Training Project (Ministry of Science, ICT and Future Planning)
- Vocational Skills Development Training Teacher for AI, IT Development, etc.
* Education Inquiries and Partnerships (KakaoTalk Channel)
* Ongoing Lectures: https://itscoding.kr

🎤 Online Educational Content Provider

Inflearn: Java, DB, MVC, Spring, Spring AI & Agent, IoT
Fast Campus: Java, Spring Boot

email : bitcocom@empas.com

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Curriculum

All

30 lectures ∙ (7hr 51min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

9 reviews

5.0

9 reviews

  • em2411014552님의 프로필 이미지
    em2411014552

    Reviews 9

    Average Rating 4.9

    5

    30% enrolled

    Thank you for the detailed explanation.

    • bitcocom
      Instructor

      Thank you. I hope the lecture is helpful to you. Keep it up until the very end! ^^

  • bigho982715님의 프로필 이미지
    bigho982715

    Reviews 24

    Average Rating 5.0

    5

    100% enrolled

    It was fascinating and fun to learn that you can create multiple agents with Spring. It was an opportunity to gain a lot of insights into the related structure and code. Thank you for the high-quality lecture.

    • bitcocom
      Instructor

      Yes, thank you. Since agent performance depends heavily on agent design and prompting, I recommend studying more in that direction. These days, many people design agents with vibe coding, but as a developer, I suggest you try developing the backend with pure coding first, then evolve to a hybrid approach (e.g., Spring AI + n8n, etc.) later on. I hope everything goes smoothly for you throughout this year. Thank you~~

  • seukchungmoon8847님의 프로필 이미지
    seukchungmoon8847

    Reviews 40

    Average Rating 5.0

    5

    30% enrolled

    Thank you for the great lecture.

    • bitcocom
      Instructor

      Thank you. I hope the lecture is helpful to you.^^

  • 아트님의 프로필 이미지
    아트

    Reviews 11

    Average Rating 5.0

    5

    30% enrolled

    • bitcocom
      Instructor

      Thank you. I hope the course is helpful to you. Keep fighting until the end.^^

  • yanghohu5207님의 프로필 이미지
    yanghohu5207

    Reviews 2

    Average Rating 5.0

    5

    30% enrolled

    • bitcocom
      Instructor

      Thank you. I hope the course is helpful to you. Keep fighting until the end.^^

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