The autonomy and contextual complexity of LLM-based agents render traditional access control (AC) mechanisms insufficient. Static, rule-based systems designed for predictable environments are fundamentally ill-equipped to manage the dynamic information flows inherent in agentic interactions. This position paper argues for a paradigm shift from binary access control to a more sophisticated model of information governance, positing that the core challenge is not merely about permission, but about governing the flow of information. We introduce Agent Access Control (AAC), a novel framework that reframes AC as a dynamic, context-aware process of information flow governance. AAC operates on two core modules: (1) multi-dimensional contextual evaluation, which assesses not just identity but also relationships, scenarios, and norms; and (2) adaptive response formulation, which moves beyond simple allow/deny decisions to shape information through redaction, summarization, and paraphrasing. This vision, powered by a dedicated AC reasoning engine, aims to bridge the gap between human-like nuanced judgment and scalable Al safety, proposing a new conceptual lens for future research in trustworthy agent design.

翻译：LLM智能体的自主性和上下文复杂性使得传统的访问控制机制显得力不从心。为可预测环境设计的静态、基于规则的系统，从根本上无法有效管理智能体交互中固有的动态信息流。本立场论文主张从二元访问控制向更复杂的信息治理模式进行范式转变，认为核心挑战不仅在于权限管理，更在于对信息流的治理。我们提出智能体访问控制这一新颖框架，将访问控制重新定义为动态、上下文感知的信息流治理过程。AAC通过两个核心模块运作：(1) 多维上下文评估模块，不仅评估身份，还评估关系、场景和规范；(2)自适应响应生成模块，超越简单的允许/拒绝决策，通过内容编辑、摘要生成和语义转述来塑造信息。这一愿景由专用的访问控制推理引擎驱动，旨在弥合类人精细判断与可扩展AI安全之间的鸿沟，为可信智能体设计的未来研究提供新的概念视角。