Agentic AI using Spring Boot MCP Server

Spring Boot构建Agentic AI实战

掌握用Spring Boot和Claude构建生产级AI代理的核心技能，涵盖MCP标准、工具调用与智能循环。适用所有水平，1.5小时课程助你快速上手。Java开发者职场进阶首选。

Published 5/2026
Created by Anup Bhagwat
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 24 Lectures ( 1h 23m ) | Size: 1.92 GB

Build Production-Ready AI Agents in Java — Tool Calling, MCP Servers, and Agentic Loops with Spring Boot and Claude

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

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 buildAgentic 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-poweredfinancial 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, howBeanOutputConverterconflicts 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.