Agentic Web: Weaving the Next Web with AI Agents

TL;DR

The paper introduces the concept of the Agentic Web, where AI agents interact autonomously to perform complex tasks, transforming internet interactions and raising new technological, societal, and governance challenges.

Contribution

It provides a structured framework and conceptual model for understanding, building, and analyzing the emerging Agentic Web driven by AI agents and large language models.

Findings

Defines the core dimensions of intelligence, interaction, and economics for AI agents.

Identifies architectural challenges like communication protocols and orchestration strategies.

Discusses societal risks and governance issues related to autonomous agent systems.

Abstract

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.