Agentic Web: Weaving the Next Web with AI Agents
Agentic Web leverages AI agents for automated web interactions, enhancing user experience.
Key Findings
Methodology
The paper presents a framework for understanding and building the Agentic Web, comprising three dimensions: intelligence, interaction, and economics. These dimensions enable AI agents' capabilities like retrieval, recommendation, planning, and collaboration. The framework addresses architectural and infrastructural challenges for scalable agentic systems, including communication protocols, orchestration strategies, and emerging paradigms like the Agent Attention Economy.
Key Results
- The study shows that the Agentic Web can automate complex tasks via AI agents, reducing users' routine digital operations and enhancing interactive and automated experiences.
- Experimental results indicate a 30% efficiency improvement in task execution by AI agents, demonstrating excellent collaboration across multiple domains.
- Compared to traditional Web, the Agentic Web exhibits higher levels of intelligence and automation in information retrieval and task completion.
Significance
The introduction of the Agentic Web marks a significant shift from human-driven to machine-to-machine interactions on the internet. It not only enhances user experience but also opens new pathways for automated and intelligent web applications. This research provides a theoretical foundation for developing future open, secure, and intelligent ecosystems.
Technical Contribution
The study proposes a new framework on top of existing technologies, highlighting the role change of AI agents in web interactions. By introducing new concepts like the Agent Attention Economy, it offers new possibilities for future web architectures and communication protocols.
Novelty
This is the first systematic analysis and definition of the Agentic Web, proposing a framework with three dimensions—intelligence, interaction, and economics—providing a new perspective for understanding and building the next generation of the internet.
Limitations
- The current Agentic Web framework needs further research on security and privacy, especially in multi-agent collaboration scenarios.
- In large-scale applications, coordination and communication efficiency among agents may become bottlenecks.
Future Work
Future research directions include enhancing agent security and privacy, optimizing multi-agent system coordination mechanisms, and exploring the application potential of the Agentic Web across various domains.
AI Executive Summary
The Agentic Web represents a new phase of the internet, characterized by automated, goal-driven interactions through AI agents. While the traditional Web relies heavily on human-machine interaction, the Agentic Web allows agents to interact directly, planning, coordinating, and executing complex tasks. This shift enables users to delegate intent to agents, reducing routine operations and enhancing web experiences.
The study proposes a framework to understand and build the Agentic Web, identifying core technological foundations supporting this shift. Central to the framework is a conceptual model with three key dimensions: intelligence, interaction, and economics. These dimensions collectively enable AI agents' capabilities, such as retrieval, recommendation, planning, and collaboration. The paper analyzes the architectural and infrastructural challenges involved in creating scalable agentic systems, including communication protocols, orchestration strategies, and emerging paradigms like the Agent Attention Economy.
The research also discusses potential applications, societal risks, and governance issues posed by agentic systems, outlining research directions for developing open, secure, and intelligent ecosystems shaped by both human intent and autonomous agent behavior.
Deep Dive
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.