A Goal-Oriented Framework for AI Agent Context Governance
The article synthesizes Anthropic’s context-engineering guidance into a framework for organizing AI-agent context around goals, evidence, permissions, memory, exploration, and progress. Using a synthetic fund-comparison scenario, it argues that context should preserve source boundaries and support the next useful judgment rather than simply expand the context window. It also distinguishes model judgments from actual tool execution records and describes memory operations including writing, recall, correction, expiry, and forgetting.
The framework provides AI-agent builders with a structured way to connect current goals, available evidence, authorized actions, retained history, and remaining gaps. It also emphasizes that prompt guidance must work with deterministic execution and persistent records to enforce boundaries and verify actual outcomes.
一个模型可能知道怎样比较基金，却不知道用户这次在意费用还是持仓重叠；可能刚读过一份报告，却不知道下一次调用还能不能看见原文；可能记得用户偏好简短回答，却没注意到用户这次明确要求展开。
这些问题不能只归结为模型“不够聪明”。通用知识与眼前处境之间，还有一段需要系统搭建的联系。
上下文的作用，是让模型在当前这一步知道：要达成什么目标，能够依据什么判断，允许采取哪些行动，以及怎样接着已有工作向前推进。 系统提示词、工具说明、当前资料、记忆和进度，都是这份任务视图的组成部分。它们需要协同，却不能混成一段没有来源和职责之分的背景文字。
Anthropic 在《Effective context engineering for AI agents》中，将工程关注点从提示词措辞扩展到每次推理时的信息选择与维护。[1] 沿着这个方向，我们更关心一个目标导向的问题：不是“还能往窗口里装什么”，而是“为了作出有用的下一步判断，模型此刻需要知道什么”。