Ai Agents & Agentic Workflows With Spring Ai, Mcp And Java
Published 5/2026
Created by Vinoth Selvaraj
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 190 Lectures ( 11h 53m ) | Size: 5 GB
Model Context Protocol, Spring Boot, MCP Servers, ChatClient, ChatMemory, OpenAI, Gemini, Ollama, Integration Testing
What you’ll learn
⚡ Build AI Agents using Spring AI and Java
⚡ Design Agentic Workflows and multi-turn reasoning systems
⚡ Implement MCP Servers using Spring Boot
⚡ Build and expose MCP Tools, Resources and Prompts
⚡ Integrate OpenAI, Gemini and local LLMs using Ollama
⚡ Build Human-in-the-Loop workflows using Elicitation
⚡ Handle asynchronous workflows using Progress Notifications
⚡ Write Integration Tests for MCP-based AI systems
⚡ Implement Structured Output and Prompt Engineering techniques
⚡ Use ChatClient, ChatMemory and Advisors effectively
Requirements
❗ No prior AI knowledge is required. We will start from the fundamentals with a hands-on approach.
❗ Knowledge of Java and Spring Boot is required.
❗ OpenAI and Gemini APIs may incur small usage costs. Expected cost for this course is approximately 1 USD.
Description
Build AI Agents and Agentic Workflows using Spring AI, MCP and Java.
This course is a deep-dive, architecture-first masterclass on building production-grade AI Agents and Agentic Workflows using Java, Spring AI and the Model Context Protocol (MCP).
What you will master
✨ Building AI Agents using Spring AI and Java
✨ Designing Agentic Workflows and multi-turn reasoning systems
✨ Understanding MCP Architecture and communication flow
✨ Implementing MCP Tools, Resources and Prompts
✨ Building Human-in-the-Loop workflows using Elicitation
✨ Handling asynchronous workflows using Progress Notifications
✨ Integrating OpenAI, Gemini and local models using Ollama
✨ Using ChatClient, ChatMemory and Advisors effectively
✨ Implementing Structured Output and Prompt Engineering techniques
✨ Designing AI-Powered Microservices using Spring Boot
✨ Writing Integration Tests for MCP-based systems
✨ Applying real-world AI architecture patterns and implementation best practices
By the end of the course, you will be able to
✨ Build production-grade AI Agents and Agentic Workflows using Spring AI and Java
✨ Design and implement MCP Servers with Tools, Resources and Prompts
✨ Integrate OpenAI, Gemini and local LLMs into Spring Boot applications
✨ Build context-aware AI systems using ChatMemory, Advisors and Structured Output
✨ Apply production-oriented AI architecture patterns, testing strategies and best practices
Throughout the course, we will build practical, production-style AI systems using Spring Boot, Spring AI and MCP.
Who this course is for
⭐ Java and Spring Developers exploring AI Agents and MCP
Homepage
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