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This paper surveys agent memory in AI agents, addressing the problem of context explosion and proposing a framework for self-evolving agents through various memory types and management strategies.
A 100-page survey from UIUC, Meta, and Stanford introduces three harness layers (Interface, Mechanisms, Scaling) for AI agents, arguing that most agent failures stem from harness issues rather than reasoning flaws, and provides a taxonomy for auditing agent stacks.