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For implementers, the locking model adds a fair amount of non-trivial internal bookkeeping. Every operation must check lock state, readers must be tracked, and the interplay between locks, cancellation, and error states creates a matrix of edge cases that must all be handled correctly.
针对这些记忆缺陷，智能体工程通常采取以下手段进行增强：构建外部记忆库将用户偏好、业务知识、历史交互等存储在数据库中，智能体在需要时通过检索机制按需提取相关信息，应对长期记忆缺陷；对过长的上下文进行摘要和压缩，保留核心信息，释放Token空间，来应对信息过载导致的短期记忆遗忘。
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