📄 中文摘要
AI 代理在运行过程中面临一个普遍问题：上下文窗口结束后，之前学习到的所有信息都会消失。虽然向量检索增强（Vector RAG）可以帮助解决部分问题，但它主要是检索文档，而无法建模实体之间的关系、跨会话跟踪决策或对代理行为施加宪法约束。因此，AI 代理需要一个结构化且持久的世界模型，以便在会话边界之外持续表示现实。这种世界模型能够有效地积累和管理代理在长时间运行中所做的决策及其结果。
每个 AI 代理都需要一个持久的世界模型
出处: Why Every AI Agent Needs a Persistent World Model
发布: 2026年2月23日
📄 English Summary
Why Every AI Agent Needs a Persistent World Model
AI agents encounter a common issue where the context window ends, causing all previously learned information to vanish. While Vector RAG provides some assistance by retrieving documents, it fails to model relationships between entities, track decisions across sessions, or enforce constitutional constraints on agent actions. What AI agents truly require is a structured and persistent world model that maintains a representation of reality beyond session boundaries. This world model enables effective accumulation and management of decisions and their outcomes made by the agent over extended periods.