# Towards an Agent-First Web: Redesigning the Web for AI Agents

arXiv: https://arxiv.org/abs/2606.19116 · HTML: https://mlsanity.com/paper/2606.19116/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 60 references that are in the map.
- First posted on arXiv 17 Jun 2026.

## Builds on (most cited first, 12 of 12)

- [Agent AI: Surveying the Horizons of Multimodal Interaction](https://mlsanity.com/paper/2401.03568/) (2024)
- [Foundation Models and Fair Use](https://mlsanity.com/paper/2303.15715/) (2023)
- [Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes](https://mlsanity.com/paper/2304.09433/) (2023)
- [A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows](https://mlsanity.com/paper/2512.08769/) (2025)
- [Agentsway -- Software Development Methodology for AI Agents-based Teams](https://mlsanity.com/paper/2510.23664/) (2025)
- [Towards Responsible and Explainable AI Agents with Consensus-Driven Reasoning](https://mlsanity.com/paper/2512.21699/) (2025)
- [Model Context Contracts - MCP-Enabled Framework to Integrate LLMs With Blockchain Smart Contracts](https://mlsanity.com/paper/2510.19856/) (2025)
- [ASTRIDE: A Security Threat Modeling Platform for Agentic-AI Applications](https://mlsanity.com/paper/2512.04785/) (2025)
- [A Practical Guide to Agentic AI Transition in Organizations](https://mlsanity.com/paper/2602.10122/) (2026)
- [AI Trust OS -- A Continuous Governance Framework for Autonomous AI Observability and Zero-Trust Compliance in Enterprise Environments](https://mlsanity.com/paper/2604.04749/) (2026)
- [Standardization of Neuromuscular Reflex Analysis -- Role of Fine-Tuned Vision-Language Model Consortium and OpenAI gpt-oss Reasoning LLM Enabled Decision Support System](https://mlsanity.com/paper/2508.12473/) (2025)
- [Think Before You Act -- A Neurocognitive Governance Model for Autonomous AI Agents](https://mlsanity.com/paper/2604.25684/) (2026)

## Built on by (most cited first, 2 of 2)

- [EMBL AI Librarian: Life-Sciences Knowledge Layer for AI Agents](https://mlsanity.com/paper/2607.28229/) (2026)
- [Who Keeps the Gains from Personal AI Assistants? Seller Adaptation and the Unassisted in a Language-Model Market Simulation](https://mlsanity.com/paper/2610.05823/) (2026)

## Abstract (arXiv)

The World Wide Web was built on an assumption held for three decades: the primary consumer of web content is a human being. This permeates every layer; its access model presumes human visitors, its economics rest on human attention, and its content targets human perception. The rapid emergence of AI agents as intermediaries between humans and web content invalidates this assumption. Yet the web resists agents through blanket blocking, CAPTCHA-based exclusion, and economic models that treat agent access as extraction rather than legitimate interaction. This paper proposes a principled redesign across three layers. At the access layer, agents acting for humans should inherit equivalent access rights, governed by rate limiting and agent identification metadata in HTTP requests, analogous to browser headers, alongside a dual-layer architecture serving human-readable and agent-optimized content from the same domain. At the economic layer, we propose an intent-based tier framework grounded in the agent-as-human-proxy principle: an agent's economic obligation mirrors that of the human it represents. A token-based subscription model meters content in tokens rather than pageviews, alongside a commissioned content economy anchoring AI content production in human intentionality. At the content layer, we identify epistemic recursion, the self-referential loop in which AI-generated content is consumed by agents to produce further content, progressively detaching web knowledge from human ground truth. We propose the Agent Text Markup Language (ATML), a four-level human supervision tier model, and a cryptographic provenance chain to counter this threat. Together these constitute ten design principles for an agent-first internet, one in which agents are first-class citizens whose integration requires renegotiating the web's foundational social contract across access, economics, and content.