What if your AI agent could remember not just the current conversation, but facts about users across sessions? A few months ago, I built a custom three-tier memory system with PostgreSQL, Testcontainers, and AI-powered summarization. It worked, but required significant code and infrastructure.

Now there’s a simpler way. The Spring AI Amazon Bedrock AgentCore starter I’ve been contributing to integrates with Amazon Bedrock AgentCore Memory - a fully managed service that handles both short-term conversation history and long-term knowledge extraction for you.

In this post, I’ll show you how to build an AI agent with persistent memory in under 50 lines of Java using JBang - no Maven project setup required. Your agent will remember previous messages and automatically extract facts from conversations - all without managing a database.

What if you could build an AI assistant with access to your own data in under 40 lines of Java? That’s now possible with my contribution to the just-released Spring AI 2.0.0 M2 - Amazon Bedrock Knowledge Base support. It’s a fully managed RAG (Retrieval-Augmented Generation) service that handles document ingestion, embeddings, and vector storage for you - and now you can use it with Spring AI! RAG lets AI models answer questions using your own documents instead of relying solely on their training data.

In this post, I’ll show you how to build a working AI agent with RAG in minutes using JBang - no Maven project setup required. You’ll have an AI assistant answering questions from your company documents with minimal code.