Permission Manifests for Web Agents
On the map: LLM agents and tool use → Web, search, and research agents
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 13 of its 43 references that are in the map. First posted on arXiv 7 Dec 2025.
Builds on
13 of 43 references.
Closest ancestors · 2
Same direction, newest first
- 2024WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?12 Mar
- 2023WebArena: A Realistic Web Environment for Building Autonomous Agents25 Jul
Other references · 11
- 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,722
- 2024ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Modelscited by 200
- 2025Plan-and-Act: Improving Planning of Agents for Long-Horizon Taskscited by 190
- 2024WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Taskscited by 81
- 2025Authenticated Delegation and Authorized AI Agentscited by 63
- 2025WebInject: Prompt Injection Attack to Web Agentscited by 48
- 2024OSCAR: Operating System Control via State-Aware Reasoning and Re-Planningcited by 37
- 2024Enabling Uniform Computer Interaction Experience for Blind Users through Large Language Modelscited by 9
- 2024LLaSA: Large Language and E-Commerce Shopping Assistantcited by 8
- 2025Scrapers selectively respect robots.txt directives: evidence from a large-scale empirical studycited by 5
- 2024Web Scraping for Research: Legal, Ethical, Institutional, and Scientific Considerationscited by 4
Built on by
Citing papers per year: 2026: 3.
3 papers.
- 2026Will the Agent Recuse, and Will It Stop? Measuring LLM-Agent Compliance with In-Band Governance Signals at the Access Door and Mid-Flightcited by 2
- 2026Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliabilitycited by 0
- 2026The Agentic Web Requires New Normative Infrastructurecited by 0
- 2026The Agentic Web Requires New Normative Infrastructure9 Jun 2026
- 2026Will the Agent Recuse, and Will It Stop? Measuring LLM-Agent Compliance with In-Band Governance Signals at the Access Door and Mid-Flight4 Jun 2026
- 2026Securing Computer-Use Agents: A Unified Architecture-Lifecycle Framework for Deployment-Grounded Reliability8 May 2026
Similar, not cited
Close in wording, no citation either way
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- 2026From Secure Agentic AI to Secure Agentic Web: Challenges, Threats, and Future Directions
- 2025Agentic Web: Weaving the Next Web with AI Agents
- 2025WALT: Web Agents that Learn Tools
- 2026terms.txt: A Consent and Compensation Protocol for Agentic Web Access