Spring AI + MCP: Build Distributed AI Systems with Java

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 buildDuring this course you will build a production-style AI system that includes:Multiple Spring Boot microservicesA PostgreSQL database with schema-per-service isolationA naive AI assistant with manual orchestrationAn MCP-based AI assistant with dynamic tool discoveryDistributed AI workflows across multiple servicesYou will see how an AI assistant can coordinate operations like:Applying employee leaveFinding a replacement engineerReassigning deploymentsTriggering notifications across servicesCourse implementation highlightsThis course is fully hands-on and covers:Enterprise backend setupBuild multiple Spring Boot microservicesUse PostgreSQL with schema-per-service architectureManage schema and seed data using FlywayVerify service isolation and inter-service communicationNaive AI orchestrationBuild an AI assistant using Spring AIExtract structured intent from natural languageImplement manual orchestration using REST APIsUnderstand the limitations of hardcoded AI workflowsModel Context Protocol (MCP)Understand MCP architecture and JSON-RPC communicationConvert microservices into MCP tool providersExpose domain capabilities using Spring AI MCP serverInspect tool schemas generated automaticallyMCP-based AI assistantBuild an MCP client assistant using Spring AIEnable dynamic tool discovery across servicesAllow the LLM to plan and execute workflowsRemove orchestration logic from application codeDebugging and runtime analysisInspect MCP logs and tool execution flowsUnderstand JSON-RPC tool interactionsHandle tool errors and partial workflow executionExtend the system with new MCP tool providersAdvanced MCP capabilitiesThe course also explores additional MCP features including:Prompts capability for reusable reasoning instructionsResources capability for structured artifactsCompletions capability and when it is usedStateless vs streaming MCP transport modelsTechnologies usedJavaSpring BootSpring AIModel Context Protocol (MCP)PostgreSQLFlywayGradleDocker