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AI Agents & Agentic Workflows with Spring AI, MCP and Java
Role Play
Hot & New
Rating: 4.9 out of 5(6 ratings)
167 students
Created byVinoth Selvaraj
Last updated 5/2026
English

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

Course content

17 sections190 lectures11h 53m total length
  • What You Will Learn5:30

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. (Latest Spring Boot 4.0, Spring AI 2.0)

This entire course is about developing our own AI Agents From Scratch. It 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