Spring AI Multi-LLM & Orchestrated Multi-Agent System
This is an advanced course focused on designing and implementing Multi-LLM architectures and orchestration-centered Agent systems (Main/Sub, Tool, Task Runtime, Agent Registry) by strategically combining GPT, Gemini, and LLaMA (local) based on Spring AI and Spring Boot. Moving beyond simple LLM calls, the curriculum covers the implementation of scalable, stable, and continuously improving AI systems. This involves applying Agentic Workflow Patterns (Chain, Parallel, Routing, Orchestrator–Workers, Evaluator–Optimizer) and Multi-Agent structures, separating execution layers like RAG and external APIs/DBs via Tool/ToolRegistry, and utilizing DAG engines, YAML declarative workflows, and Validated DSL (validation immediately after loading). Furthermore, the course includes Circuit Breakers, Reactive Streams, Redis monitoring, parallel processing, and iterative evaluation loops. It expands from Thymeleaf (SSR) exercises to decoupled Front-end/Back-end architectures using React and REST, while integrating tool and agent runtimes via MCP (Model Context Protocol) to build AI architecture design capabilities at a production-ready level. The ultimate goal is to evolve beyond being a simple AI user who merely integrates APIs and writes prompts, becoming a developer capable of designing AI systems—explaining and balancing Multi-LLM, agents, workflows, declarations, and validation within a single execution architecture.
68 learners
Level Basic
Course period Unlimited
Spring AI Multi-LLM Architecture and Orchestration-focused Agent System Course - Scheduled for Upgrade
Hello, I am Jinman Lee (tootoo), your knowledge sharer.
Spring AI Multi-LLM Architecture and Orchestration-focused Agent System
I am sharing news regarding an upgrade to the course.
The upgrade is scheduled for mid-April, and it will be released with the following structure.
For those who have already purchased, please watch up to Chapter 3 and then continue from Chapter 4 onwards.
The price of this course is scheduled to increase slightly starting in mid-April, so it might be a good idea to purchase it in advance.
Thank you.
The overall course materials have been revised, and while Chapters 1 through 3 focused primarily on studying Patterns,
Chapters 4 through 8 are composed of the process of creating practice-oriented Multi-Agents.
Chapter 1. Multi-LLM Architecture (Multi-Model Strategies and Enterprise AI Architecture Design)
Chapter 2. Agentic Workflow Patterns (5 Agent Workflow Patterns Used in Practice)
Chapter 3. Orchestrated Multi-Agent Patterns (Implementing Multi-Agent Structures into Actual Service Pipelines)
Chapter 4. Multi-Agent Architecture (SubAgent Separation Strategy and Agent Registry Internal Structure)
Chapter 5. Tool-Orchestrated Multi-Agent (Architecture for Separating Execution Layers Using Tools)
Chapter 6. Task-Orchestrated Multi-Agent (Designing Agent Runtime based on TaskTool)
Chapter 7. DAG-Orchestrated Multi-Agent (Enterprise-grade DAG-based AI Workflow Design)
Chapter 8. Declarative Agent Workflow with YAML (Separating YAML-based DAG Definition and Execution Engine)