The rise of Large Language Model (LLM)-based web agents represents a significant shift in automated interactions with the web. Unlike traditional crawlers that follow simple conventions, such as robots$.$txt, modern agents engage with websites in sophisticated ways: navigating complex interfaces, extracting structured information, and completing end-to-end tasks. Existing governance mechanisms were not designed for these capabilities. Without a way to specify what interactions are and are not allowed, website owners increasingly rely on blanket blocking and CAPTCHAs, which undermine beneficial applications such as efficient automation, convenient use of e-commerce services, and accessibility tools. We introduce agent-permissions$.$json, a robots$.$txt-style lightweight manifest where websites specify allowed interactions, complemented by API references where available. This framework provides a low-friction coordination mechanism: website owners only need to write a simple JSON file, while agents can easily parse and automatically implement the manifest's provisions. Website owners can then focus on blocking non-compliant agents, rather than agents as a whole. By extending the spirit of robots$.$txt to the era of LLM-mediated interaction, and complementing data use initiatives such as AIPref, the manifest establishes a compliance framework that enables beneficial agent interactions while respecting site owners' preferences.

翻译：基于大型语言模型（LLM）的网络代理的兴起，标志着网络自动化交互方式的重大变革。与传统爬虫遵循简单规范（如robots.txt）不同，现代代理能以复杂方式与网站交互：导航复杂界面、提取结构化信息并完成端到端任务。现有治理机制并非为这些能力设计。由于缺乏明确允许与禁止交互的规范方式，网站所有者日益依赖全面屏蔽和验证码机制，这反而损害了高效自动化、便捷电商服务及无障碍工具等有益应用。我们提出agent-permissions.json——一种仿robots.txt风格的轻量级清单机制，允许网站声明许可的交互行为，并在可行时提供API参考。该框架建立了低摩擦协调机制：网站所有者仅需编写简单JSON文件，代理则可轻松解析并自动执行清单条款。网站所有者因而能专注于拦截违规代理，而非全盘禁止代理访问。通过将robots.txt的设计理念延伸至LLM中介交互时代，并补充AIPref等数据使用倡议，该清单建立了一个合规框架，在尊重网站所有者偏好的同时，为有益的代理交互提供支持。