In Part 1 of this series, we built a functional AI agent using Java, Spring AI, and Amazon Bedrock. However, we discovered a critical limitation: the agent couldn’t remember previous conversations. When we asked “What is my name?” after introducing ourselves, the agent had no recollection of our earlier interaction.
This lack of memory creates a frustrating user experience and limits the agent’s usefulness for real-world applications. Imagine a customer service agent that forgets your issue every time you send a message, or a travel assistant that can’t recall your preferences from previous conversations.
In this post, we’ll enhance our AI agent with a three-tier memory architecture that provides both short-term conversation context and long-term user knowledge. We’ll implement this incrementally, starting with persistent session memory, then adding conversation summaries, and finally user preferences—all backed by PostgreSQL for production reliability.
