Controlled knowledge updating in memory-enabled AI agents: beyond model editing and recall

Abstract

Large language model agents that persist across sessions, tools, users, and changing environments do more than answer isolated prompts; they accumulate state. When new evidence arrives, the central question is where that change should live: transient context, external memory, tool or workflow definitions, activation states, or model parameters. Recent benchmarks compare update mechanisms and assess memory over time, while emerging architectures coordinate multiple memory types. In this Perspective, we examine how a decision layer should select among update substrates and evaluate whether its choices remain appropriate. A controlled update is not merely a successful edit or a recalled fact; it is a decision to alter the least invasive substrate sufficient for the claim's scope, expected persistence, and evidentiary strength, including when the correct action is not to write persistent state. We propose a substrate-aware view of controlled knowledge updating organized by three principles: substrate proportionality, temporal defeasibility, and auditable continuity. This framing treats knowledge updating as a longitudinal control problem and motivates evaluation criteria that include update selection, temporal consistency, interference, reversibility, efficiency, and robustness. The resulting agenda connects memory, knowledge editing, agent evolution, and benchmarking under a single practical question: how should an agent decide what to change so that future behavior improves without accumulating uncontrolled state?