Agentic AI using Spring Boot MCP Server

About this course

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 "list

Categories" and "create

Category", 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 con

Prices, discounts and availability are set by Udemy. We may earn a commission when you purchase through links on this site.