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Agentic AI using Spring Boot MCP Server
New
Rating: 5.0 out of 5(1 rating)
8 students

Agentic AI using Spring Boot MCP Server

Build Production-Ready AI Agents in Java — Tool Calling, MCP Servers, and Agentic Loops with Spring Boot and Claude
New
Created byAnup Bhagwat
Last updated 5/2026
English

What you'll learn

  • MCP is the emerging standard, and it's early The Model Context Protocol is gaining rapid adoption as the de facto way to connect AI models to real tools
  • You learn patterns which can be applied to real projects the next day, not just theoretical understanding.
  • The debugging and prompt engineering depth is rare Most courses show the happy path. Your content goes deeper.
  • Rare, job-market-ready skill combination Most AI courses teach Python. A course that bridges Spring Boot—the dominant enterprise Java framework with Agentic AI

Course content

4 sections • 24 lectures • 1h 23m total length

  • Agentic AI using spring boot - Course Introduction2:09
  • What and Why MCP4:54
  • Traditional LLM vs MCP Approach2:56
  • Code Overview3:41
  • Project Structure1:46
  • Application structure2:01
  • List Categories API4:25
  • Run List Categories API3:24

Requirements

  • Java 17 +
  • Spring boot
  • Spring Boot generative AI
  • Maven

Description

AI is no longer just about chatbots. The next generation of intelligent systems can reason, plan, and take action — and they're being built right now inside enterprise Java stacks.

This course teaches you how to build Agentic AI systems using Spring Boot and the Model Context Protocol (MCP) — the emerging standard for connecting large language models to real-world tools and services. You'll go far beyond basic prompt-and-response patterns and learn how to design AI agents that autonomously call tools, make decisions across multiple steps, and integrate cleanly into production Java backends.

What you'll build: You'll create a fully working AI-powered financial transaction classifier backed by a Spring Boot MCP Server — with real tool registration, dynamic category resolution via "listCategories" and "createCategory", and a robust agentic loop that handles edge cases production systems actually face.

What makes this course different: Most AI courses live in Python notebooks. This one lives where your production code lives — in a Spring Boot application, using the Anthropic Java SDK and Spring AI. You'll learn not just the happy path but the hard parts: why tool calls silently get skipped, how BeanOutputConverter conflicts with agentic prompts, how to structure two-phase prompts that force the model to call tools before producing output.

What you'll learn:

  • How the Model Context Protocol works and why it's becoming the industry standard

  • Registering and invoking MCP tools inside a Spring Boot application

  • Designing prompts that reliably trigger tool-calling loops

  • Debugging agentic failures — token limits, output conflicts, and silent fallbacks

  • Parsing and validating LLM responses safely in a Java backend

  • Building for production: error handling, observability, and cost control

This course is for you if: You're a Java or Spring Boot developer who wants to build real AI-powered features — not toy demos — inside the tech stack you already use at work.

By the end, you won't just understand Agentic AI in theory. You'll have shipped one.

Who this course is for:

  • CS Students & Bootcamp Graduates with Java Background Junior developers who learned Java academically or through a bootcamp and want to stand out in the job market. Adding Agentic AI to a Java portfolio is a strong differentiator when competing for backend roles.
  • Java / Spring Boot Developers Developers who already know Spring Boot and want to add AI capabilities to their skill set without switching to Python. They're comfortable with Java but feel left out of the AI wave — this course is their on-ramp.
  • Backend Engineers at Enterprise Companies Engineers working at banks, fintechs, or large enterprises where Java is the standard stack. Their companies are actively exploring AI integration, and they need someone who can build it in the existing tech landscape — not greenfield Python microservices.
  • Software Architects & Tech Leads People responsible for designing AI-powered systems who need to understand how agentic loops, tool calling, and MCP fit into a production architecture. They need to make build-vs-buy and design decisions confidently.

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