sthwin

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.

tootoo

Spring AI Multi-LLM & Orchestrated Multi-Agent System

실습 중심

ai in practice

AI 활용법

SpringAI

springai

ai 코딩

AI 코딩

multi-agent

입문

초급

중급이상

스프링부트

Spring Boot

orchestration

AI 에이전트

AI Agent

1⃣ Setting up Spring AI development environment and Multi-LLM environment in SpringBoot

2️⃣ Chapter 1. Multi-LLM Architecture (Multi-Model and AI Architecture Design)

3️⃣ Chapter 2. Agentic Workflow Patterns (5 Agent Workflow Patterns)

4⃣Chapter 3. Orchestrated Multi-Agent Patterns (Implemented as Pipelines)

Chapter 4. Multi-Agent Architecture (Main Agent & SubAgent Separation Strategy)

Chapter 5. Tool-Orchestrated Multi-Agent (Separation of Tool-based Execution Layer)

Chapter 6. Task-Orchestrated Multi-Agent(TaskTool Agent Runtime)

Chapter 7. DAG-Orchestrated Multi-Agent (DAG-based AI Workflow Design)

Chapter 8. Declarative Agent Workflow with YAML (YAML-based DAG)

Chapter 9. Validated Agent Workflow DSL (DSL Validation-based DAG)

Appendix A. React Front-End & REST API Server Integration

Appendix B. MCP Integration (Integrating Tool/Agent Runtimes with MCP)