Your AI Agent Needs a History, Not Just a Memory.
When an agent gets something wrong, teams often inspect its final answer and the context retrieved for the last model call. That can explain what the agent believed at the end. It rarely explains how it got there. For consequential systems, that distinction matters. An agent needs memory: relevant knowledge from past interactions, retrieved documents, and working state for the task at hand. But memory is not history. Memory answers, “What should I recall now?” History answers, “What happened, in what order, and why did the system reach this state?” ...