In this article, you will learn how to design reliable memory systems for AI agents, covering both the patterns that work and the common architectural mistakes that cause persistent, hard-to-trace failures.
Topics we will cover include:
What agent memory actually means and how it differs from context, prompts, and static knowledge bases.
Write and retrieval strategies — including importance scoring, memory scoping, and provenance tracking — that support reliable multi-session behavior.
The memory architectures and compression approaches that break down as systems grow, and how to avoid them.
