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 Build

• Working memory using conversation history

• Persona memory for persistent user facts

• Episodic memory using conversation summaries

• Semantic memory using learned preferences

• Vector similarity search with pgvector

• Async memory processing pipelines

• Centralized prompt assembly using Spring AI Advisors

What You’ll Learn

• Why chat history is not real AI memory

• How modern AI memory systems are structured

• How to design layered memory architectures

• How embeddings and vector search work in practice

• How to retrieve relevant memory dynamically

• How to build scalable AI backend pipelines

• How to personalize AI behavior across conversations

Technologies Used

• Java

• Spring Boot

• Spring AI

• PostgreSQL

• pgvector

  
  

By 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.