Permission Manifests for Web Agents

• URL: http://arxiv.org/abs/2601.02371v1
• Date: Sun, 07 Dec 2025 17:45:01 GMT
• Title: Permission Manifests for Web Agents
• Authors: Samuele Marro, Alan Chan, Xinxing Ren, Lewis Hammond, Jesse Wright, Gurjyot Wanga, Tiziano Piccardi, Nuno Campos, Tobin South, Jialin Yu, Alex Pentland, Philip Torr, Jiaxin Pei,
• Abstract summary: The rise of Large Language Model (LLM)-based web agents represents a significant shift in automated interactions with the web.<n>Without a way to specify what interactions are and are not allowed, website owners increasingly rely on blanket blocking and CAPTCHAs.<n>We introduce agent-permissions, a robots.txt-style interfaces manifest where websites specify allowed interactions, complemented by API references.
• Score: 30.22217505383227
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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