Why Your AI Agent Keeps Forgetting (Even With 1M Tokens)

This article argues that large context windows are not a substitute for proper memory in AI agents. Instead, effective systems rely on layered memory architectures—working, session, condensed, durable, and retrieval memory—to manage context growth, reduce cost, and improve accuracy. By focusing on memory lifecycle design rather than raw context size, developers can build more reliable, scalable agents that avoid common failures like repetition, context drift, and missed recall.