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:
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Multiple Spring Boot microservices
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A PostgreSQL database with schema-per-service isolation
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A naive AI assistant with manual orchestration
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An MCP-based AI assistant with dynamic tool discovery
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Distributed AI workflows across multiple services
You will see how an AI assistant can coordinate operations like:
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Applying employee leave
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Finding a replacement engineer
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Reassigning deployments
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Triggering notifications across services
Course implementation highlights
This course is fully hands-on and covers:
Enterprise backend setup
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Build multiple Spring Boot microservices
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Use PostgreSQL with schema-per-service architecture
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Manage schema and seed data using Flyway
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Verify service isolation and inter-service communication
Naive AI orchestration
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Build an AI assistant using Spring AI
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Extract structured intent from natural language
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Implement manual orchestration using REST APIs
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Understand the limitations of hardcoded AI workflows
Model Context Protocol (MCP)
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Understand MCP architecture and JSON-RPC communication
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Convert microservices into MCP tool providers
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Expose domain capabilities using Spring AI MCP server
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Inspect tool schemas generated automatically
MCP-based AI assistant
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Build an MCP client assistant using Spring AI
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Enable dynamic tool discovery across services
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Allow the LLM to plan and execute workflows
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Remove orchestration logic from application code
Debugging and runtime analysis
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Inspect MCP logs and tool execution flows
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Understand JSON-RPC tool interactions
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Handle tool errors and partial workflow execution
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Extend the system with new MCP tool providers
Advanced MCP capabilities
The course also explores additional MCP features including:
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Prompts capability for reusable reasoning instructions
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Resources capability for structured artifacts
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Completions capability and when it is used
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Stateless vs streaming MCP transport models
Technologies used
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Java
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Spring Boot
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Spring AI
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Model Context Protocol (MCP)
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PostgreSQL
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Flyway
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Gradle
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Docker
