Your AI agent shouldn’t flinch at every tiny change, but it also shouldn’t treat a career switch like background noise.

Richard Emate, a developer, explains how a theory of leftover surprise influenced the design of a memory layer for AI agents. The approach treats experience as the part of reality the model did not already predict, aiming to balance sensitivity to small changes with stability during major shifts.

How a theory of leftover surprise changed a memory layer Richard Emate Richard Emate Richard Emate Follow Aug 18 How a theory of leftover surprise changed a memory layer python ai llm opensource Add Comment 9 min read