AI Agent Memory Architecture with Spring AI

Most AI applications do not truly remember users. They simply replay chat history. In this course, you will learn how to design and implement real memory systems for AI agents using Java, Spring AI, PostgreSQL, and pgvector.

Using a practical AI Travel Planner project, you will build a layered memory architecture that enables AI assistants to remember users correctly across conversations. This is a backend engineering focused course designed for developers who want to move beyond basic chat applications and build production-style AI systems. What You’ll BuildWorking memory using conversation historyPersona memory for persistent user factsEpisodic memory using conversation summariesSemantic memory using learned preferencesVector similarity search with pgvectorAsync memory processing pipelinesCentralized prompt assembly using Spring AI AdvisorsWhat You’ll LearnWhy chat history is not real AI memoryHow modern AI memory systems are structuredHow to design layered memory architecturesHow embeddings and vector search work in practiceHow to retrieve relevant memory dynamicallyHow to build scalable AI backend pipelinesHow to personalize AI behavior across conversationsTechnologies UsedJavaSpring BootSpring AIPostgreSQLpgvectorBy the end of this course, you will have a complete understanding of how real AI memory systems are designed and implemented in modern backend applications.