The Seven-Year Itch of Lifelong Agents: When Perfect Memory Prevents Adaptation

Long-lived AI agents rely on memory to personalize over time, yet the user they remember may change. An assistant can accurately retain years of past preferences and still make poor decisions for the user now if obsolete evidence continues to shape current responses. We call this crossover the seven-year itch of lifelong agents: beyond some interaction history, retaining more behaviorally active memory can reduce current-state adaptation. We characterize the effect with a drifting-user model that separates the benefit of averaging more observations from the bias created by stale evidence. This analysis motivates (Finitude-Aware Memory), which preserves historical episodes while adapting how strongly they influence current personalization; commitments follow a protected path, and a compact handoff state carries forward still-valid information. We evaluate the resulting design with , a controlled benchmark spanning eight forms of user change, six LLM backbones, and repeated runs. The study reframes long-term memory as a temporal-validity problem: the challenge is not only to remember the past, but to decide how much authority the past should retain over the present.