Agentic Web: Weaving the Next Web with AI Agents
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911081444671488 |
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| author | Yang, Yingxuan Ma, Mulei Huang, Yuxuan Chai, Huacan Gong, Chenyu Geng, Haoran Zhou, Yuanjian Wen, Ying Fang, Meng Chen, Muhao Gu, Shangding Jin, Ming Spanos, Costas Yang, Yang Abbeel, Pieter Song, Dawn Zhang, Weinan Wang, Jun |
| author_facet | Yang, Yingxuan Ma, Mulei Huang, Yuxuan Chai, Huacan Gong, Chenyu Geng, Haoran Zhou, Yuanjian Wen, Ying Fang, Meng Chen, Muhao Gu, Shangding Jin, Ming Spanos, Costas Yang, Yang Abbeel, Pieter Song, Dawn Zhang, Weinan Wang, Jun |
| contents | The emergence of AI agents powered by large language models (LLMs) marks a pivotal shift toward the Agentic Web, a new phase of the internet defined by autonomous, goal-driven interactions. In this paradigm, agents interact directly with one another to plan, coordinate, and execute complex tasks on behalf of users. This transition from human-driven to machine-to-machine interaction allows intent to be delegated, relieving users from routine digital operations and enabling a more interactive, automated web experience. In this paper, we present a structured framework for understanding and building the Agentic Web. We trace its evolution from the PC and Mobile Web eras and identify the core technological foundations that support this shift. Central to our framework is a conceptual model consisting of three key dimensions: intelligence, interaction, and economics. These dimensions collectively enable the capabilities of AI agents, such as retrieval, recommendation, planning, and collaboration. We analyze the architectural and infrastructural challenges involved in creating scalable agentic systems, including communication protocols, orchestration strategies, and emerging paradigms such as the Agent Attention Economy. We conclude by discussing the potential applications, societal risks, and governance issues posed by agentic systems, and outline research directions for developing open, secure, and intelligent ecosystems shaped by both human intent and autonomous agent behavior. A continuously updated collection of relevant studies for agentic web is available at: https://github.com/SafeRL-Lab/agentic-web. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21206 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agentic Web: Weaving the Next Web with AI Agents Yang, Yingxuan Ma, Mulei Huang, Yuxuan Chai, Huacan Gong, Chenyu Geng, Haoran Zhou, Yuanjian Wen, Ying Fang, Meng Chen, Muhao Gu, Shangding Jin, Ming Spanos, Costas Yang, Yang Abbeel, Pieter Song, Dawn Zhang, Weinan Wang, Jun Artificial Intelligence Machine Learning The emergence of AI agents powered by large language models (LLMs) marks a pivotal shift toward the Agentic Web, a new phase of the internet defined by autonomous, goal-driven interactions. In this paradigm, agents interact directly with one another to plan, coordinate, and execute complex tasks on behalf of users. This transition from human-driven to machine-to-machine interaction allows intent to be delegated, relieving users from routine digital operations and enabling a more interactive, automated web experience. In this paper, we present a structured framework for understanding and building the Agentic Web. We trace its evolution from the PC and Mobile Web eras and identify the core technological foundations that support this shift. Central to our framework is a conceptual model consisting of three key dimensions: intelligence, interaction, and economics. These dimensions collectively enable the capabilities of AI agents, such as retrieval, recommendation, planning, and collaboration. We analyze the architectural and infrastructural challenges involved in creating scalable agentic systems, including communication protocols, orchestration strategies, and emerging paradigms such as the Agent Attention Economy. We conclude by discussing the potential applications, societal risks, and governance issues posed by agentic systems, and outline research directions for developing open, secure, and intelligent ecosystems shaped by both human intent and autonomous agent behavior. A continuously updated collection of relevant studies for agentic web is available at: https://github.com/SafeRL-Lab/agentic-web. |
| title | Agentic Web: Weaving the Next Web with AI Agents |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.21206 |