
Me too! I can do AI with Spring (Inflearn Part 1)
bitcocom
Developing AI Applications with Spring Boot and Spring AI: Mastering OpenAI for Real-World Solutions
Basic
Java, Spring, Spring Boot
# Building an 'Intelligent Collaboration' Expert Agent Team Using Spring AI Router Pattern + RAG + MCP Beyond Single Agents to Architecture: The Definitive Guide to Router Pattern and Agent Isolation Design
157 learners
Level Basic
Course period Unlimited
Reviews from Early Learners
5.0
bigho98
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
Thank you for the great lecture.
5.0
em241101
Thank you for the detailed explanation.
Spring AI Multi-Agent System Design: Learn how to build a team of specialized agents (Reservation, Sommelier, Concierge) that efficiently handle complex business logic by applying the Router Pattern.
Enterprise-Grade Architecture Implementation: Beyond Single Agent Limitations, designing a safe and scalable production-ready backend system through **Role Separation (Router-Worker)** and **Tool Isolation**.
RAG & MCP Practical Application: Implement an **intelligent menu recommendation system (RAG)** using vector DB and a real-time administrator notification system integrated with Slack MCP to enhance the completeness of AI services.
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 it's difficult to control complex business logic with a single chatbot?
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 an 'autonomously collaborating agent system' that can be applied to real services?
Through this course, you can strengthen your AI agent architecture design and implementation capabilities required in actual enterprise environments, and complete expert-level AI system building skills that can be applied in practice.
You will grow into an expert who designs and operates a 'collaborative AI team' that goes beyond simple chatbots.
You can build and operate a multi-AI agent system yourself.
Beyond the limitations of a single chatbot, you will gain experience in designing and implementing a team of specialized agents (Reservation, Sommelier, Concierge) that handle complex business logic using Spring AI's Router Pattern and MCP protocol.
Beyond RAG and Tool Calling: Complete the Practical Architecture of 'Collaborative AI'.
Beyond simple LLM integration, you'll build a safe and scalable production-grade backend system through role separation (Router-Worker) and tool isolation. Through this, you'll learn how to realize the true potential of AI agents.
Implement an integrated intelligent recommendation system based on vector DB and real-time notification system.
We 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, we will enhance the completeness of AI services and improve the ability to solve real business problems.
As a senior developer, I possess expertise in designing and building AI-based systems.
You will grow into a key talent capable of successfully leading complex AI projects by acquiring the latest technology trends in the AI engineering field, and particularly by strengthening your abilities in multi-agent system design and utilizing RAG and MCP.
This course provides detailed coverage of how to build a team of specialized agents that handle complex business logic by going beyond the limitations of a single agent through Router Pattern and agent isolation design using Spring AI. You will learn and directly implement the core principles for designing enterprise-grade, practical backend systems.
Building Effective Agents
Agentic Systems(Routing Workflow)
Build an intelligent menu recommendation (RAG) system using vector DB and a real-time administrator notification system through Slack MCP integration to enhance the completeness of your AI service. You'll gain practical experience implementing complex collaboration scenarios with AI agent teams.
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
We provide all the source code and configurations needed to build a fully functional multi-AI agent system based on the latest technology stack including Spring Boot, Spring AI, AI Agent, MCP, RAG, and more. This enables you to immediately apply what you've learned to real-world projects and expand upon it.
This section introduces the overview of Season 2 for developing enterprise-grade multi-AI agent systems using Spring AI. It emphasizes the necessity of multi-agent architecture for handling complex business logic and covers in detail the development environment setup, 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
This section lays the foundation for building an AI agent system. You'll design Entities based on master tables and relational tables, define DTOs (Records) for efficient data communication with AI, and learn how to design Repositories using JPA.
ERD (Entity-Relationship Diagram)
Entity Logical Structure
You will learn the process of implementing core business logic such as reservation and order processing. You'll develop ReservationService and OrderService, and based on these, design and implement ReservationTools and SommelierTools that AI agents can utilize.
Tool Calling
We build an intelligent recommendation system using Retrieval Augmented Generation (RAG) technology. We load and embed menu description data to construct a VectorDB, and create dummy data for testing to understand and practice how the RAG system works.
MariaDB VectorDB
Learn the essence of multi-agent system architecture design that goes beyond the limitations of single agents. This covers in-depth the AiConfig configuration including ChatModel and ChatMemory settings, 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
You will intensively learn prompt engineering to define the core logic and safety mechanisms for each agent. You'll design system prompts (.st) for each agent—reservation, recommendation/ordering, and guidance—implement controllers for external API integration, and integrate and test the final system.
Node.js, VS Code, React.js, JavaScript, Tailwind CSS, Vite Tool
Individuals who want to control complex business logic with AI beyond the limitations of a single chatbot but feel overwhelmed by actual implementation methods
Individuals who want to build enterprise-grade AI systems through Router Pattern and Agent Isolation design
Those struggling to design and build systems where multiple AI agents autonomously collaborate, beyond RAG and Tool Calling
Those who want to strengthen their ability to design multi-AI agent system architectures that can be utilized in real service environments
Those who want to enhance the competitiveness of existing services or envision new business models by leveraging AI agent technology
Those who want to explore practical service implementation possibilities through case studies of building intelligent collaborative agent teams based on Spring AI
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: Understanding basic SQL (SELECT, JOIN concepts).
Learning Materials
The source code (backend and frontend) is provided in Lecture 30 at the end of the video course.
Lecture materials are provided as PDF files.
Source code is provided through Github.
If you encounter any parts that are difficult to understand while studying, please feel free to ask immediately through the Q&A board or 1:1 open chat
👩🎓Spring AI Practice (1:1 Open Chat) : https://open.kakao.com/o/sXXxSI5h
Who is this course right for?
Hitting the limits of a single chatbot, a senior developer who wants to control complex business logic with AI
Beyond RAG and Tool Calling, AI Engineers Who Want to Build 'Autonomously Collaborative Agent Systems'
Need to know before starting?
You need foundational knowledge of the Java programming language.
It would be helpful to have a basic understanding of the Spring Boot framework.
It is helpful to have basic knowledge of databases and SQL.
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Courses
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
All
30 lectures ∙ (7hr 51min)
Course Materials:
All
9 reviews
5.0
9 reviews
Reviews 24
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Average Rating 5.0
5
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.
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~~
Reviews 9
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Average Rating 4.9
5
Thank you for the detailed explanation.
Thank you. I hope the lecture is helpful to you. Keep it up until the very end! ^^
Reviews 40
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Average Rating 5.0
5
Thank you for the great lecture.
Thank you. I hope the lecture is helpful to you.^^
Reviews 2
∙
Average Rating 5.0
5
Thank you. I hope the course is helpful to you. Keep fighting until the end.^^
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