5 hours agoDevelopmentBuild AI assistants using Spring AI, Model Context Protocol (MCP), and microservices with real enterprise architecture.
Course Description
Modern AI systems are no longer simple chatbots.
Real-world applications require AI assistants that can interact with backend services, execute actions, retrieve data, and coordinate workflows across distributed systems.
In this course, you will learn how to build these systems using Spring AI and Model Context Protocol (MCP).
Instead of toy examples, you will implement a complete distributed AI architecture built with Spring Boot microservices. The course is based on a realistic enterprise system called NexaCorp, where an AI assistant interacts with services such as HR, deployment management, notifications, and ticket management.
Includes free 90-day access to IntelliJ IDEA Ultimate for a professional development experience.
What you will build
During this course you will build a production-style AI system that includes:
Multiple Spring Boot microservices
A PostgreSQL database with schema-per-service isolation
A naive AI assistant with manual orchestration
An MCP-based AI assistant with dynamic tool discovery
Distributed AI workflows across multiple services
You will see how an AI assistant can coordinate operations like:
Applying employee leave
Finding a replacement engineer
Reassigning deployments
Triggering notifications across services
Course implementation highlights
This course is fully hands-on and covers:
Enterprise backend setup
Build multiple Spring Boot microservices
Use PostgreSQL with schema-per-service architecture
Manage schema and seed data using Flyway
Verify service isolation and inter-service communication
Naive AI orchestration
Build an AI assistant using Spring AI
Extract structured intent from natural language
Implement manual orchestration using REST APIs
Understand the limitations of hardcoded AI workflows
Model Context Protocol (MCP)
Understand MCP architecture and JSON-RPC communication
Convert microservices into MCP tool providers
Expose domain capabilities using Spring AI MCP server
Inspect tool schemas generated automatically
MCP-based AI assistant
Build an MCP client assistant using Spring AI
Enable dynamic tool discovery across services
Allow the LLM to plan and execute workflows
Remove orchestration logic from application code
Debugging and runtime analysis
Inspect MCP logs and tool execution flows
Understand JSON-RPC tool interactions
Handle tool errors and partial workflow execution
Extend the system with new MCP tool providers
Advanced MCP capabilities
The course also explores additional MCP features including:
Prompts capability for reusable reasoning instructions
Resources capability for structured artifacts
Completions capability and when it is used
Stateless vs streaming MCP transport models
Technologies used
Java
Spring Boot
Spring AI
Model Context Protocol (MCP)
PostgreSQL
Flyway
Gradle
Docker
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