Agentic Web: Weaving the Next Web with AI Agents (2507.21206v1)

Abstract: The emergence of AI agents powered by 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.

• The paper presents a novel framework where autonomous AI agents orchestrate web interactions, shifting agency from humans to software.
• It details a tripartite model of Intelligence, Interaction, and Economy that reshapes digital protocols and economic dynamics.
• The work highlights significant challenges in agent safety, multi-agent coordination, and secure, scalable system architectures.

Agentic Web: A Framework for the Next-Generation Internet Mediated by AI Agents

Introduction and Motivation

The paper "Agentic Web: Weaving the Next Web with AI Agents" (2507.21206) presents a comprehensive conceptual and technical framework for the evolution of the Web into an agent-mediated, autonomous, and interactive ecosystem. The authors argue that the traditional Web—centered on human-driven search, browsing, and transactional activities—is being fundamentally transformed by the emergence of AI agents powered by LLMs. These agents are capable of persistent, goal-driven planning, coordination, and execution of complex tasks, shifting the locus of agency from human users to autonomous software intermediaries.

The work situates this transition within a historical context, tracing the progression from the PC Web Era (search and static content), through the Mobile Web Era (recommendation and user-generated content), to the Agentic Web Era (action and agentic orchestration). The authors propose a three-dimensional model—Intelligence, Interaction, and Economy—to structure the analysis of this new paradigm, and provide a detailed examination of the architectural, algorithmic, and socio-economic implications.

Conceptual Model: Intelligence, Interaction, and Economy

Intelligence Dimension

The Intelligence dimension encompasses the cognitive capabilities required for agents to function autonomously in open-ended digital environments. This includes contextual understanding, long-horizon planning, adaptive learning, cognitive monitoring, and multi-modal integration. The paper emphasizes that agentic intelligence must be transferable and robust, supporting reasoning and adaptation across diverse tasks and domains. The shift from passive retrieval to proactive, context-aware information acquisition is highlighted as a core transition, with retrieval-augmented generation (RAG) architectures and tool-augmented reasoning as key enablers.

Interaction Dimension

The Interaction dimension addresses the mechanisms by which agents communicate, coordinate, and orchestrate actions within digital ecosystems. The authors identify the limitations of traditional web protocols (HTTP/RPC) for agentic workflows, and analyze the emergence of agent-native protocols such as the Model Context Protocol (MCP) and Agent-to-Agent (A2A). These protocols support semantic interoperability, persistent context, dynamic capability discovery, and privacy-aware collaboration, enabling agents to form ad hoc coalitions, share intermediate outputs, and manage complex, multi-step workflows. The transition from static hyperlinks to semantic, context-driven connections is a central architectural shift.

Economic Dimension

The Economic dimension explores the reconfiguration of digital value creation and exchange in an agent-mediated web. Agents are positioned as autonomous economic actors capable of initiating transactions, negotiating terms, and allocating resources without direct human intervention. The paper introduces the concept of the Agent Attention Economy, where services and tools compete for invocation by agents rather than human clicks. This shift necessitates new commercial models, metrics (e.g., service invocation frequency, capability relevance), and governance frameworks to address issues of liability, transparency, and alignment in machine-native economies.

Algorithmic and Systemic Transitions

From User-Centric Retrieval to Agentic Information Acquisition

The paper details the evolution from traditional, human-initiated information retrieval (e.g., TF-IDF, BM25, PageRank) to agentic, context-aware, and multi-modal retrieval pipelines. RAG, Fusion-in-Decoder, FLARE, SELF-RAG, and Toolformer are cited as exemplars of architectures that integrate retrieval, reasoning, and tool use, supporting autonomous knowledge construction and procedural decision-making.

From Recommendation to Agent Planning

Recommendation systems, once focused on matching users to items, are reinterpreted as frameworks for agentic planning and execution. The integration of reasoning traces (ReAct), program synthesis (WebAgent), closed-loop planning (AdaPlanner), and hierarchical memory (Task Memory Engine) enables agents to autonomously interpret instructions, decompose tasks, and execute multi-step workflows. The emergence of standardized evaluation environments (WebArena, Mind2Web, ST-WebAgentBench) is noted as critical for benchmarking agentic capabilities.

From Single-Agent to Multi-Agent Coordination

The transition from isolated, single-agent systems to collaborative, multi-agent architectures is analyzed in depth. Frameworks such as AutoGen, AgentOccam, WebPilot, and OWL demonstrate the benefits of role specialization, task decomposition, and orchestrated execution. The paper highlights the importance of modularity, flexibility, and context management in enabling distributed intelligence and emergent behavior.

System Architecture and Protocols

The authors propose a tripartite system architecture comprising the User Client, Intelligent Agent, and Backend Services. This model supports the translation of high-level user intent into executable operations, with the agent serving as the cognitive and decision-making nexus. The need for dynamic agent discovery, semantic API specification, persistent billing and accounting, and SRZ-centric (Service Requirement Zone) resource orchestration is emphasized. The inadequacy of traditional client-server models is addressed, advocating for a client-agent-server paradigm with integrated demand mapping, task routing, and cross-agent billing ledgers.

The paper provides detailed analyses of MCP and A2A protocols, illustrating their roles in standardizing agent-to-tool and agent-to-agent communication, respectively. The protocols' support for capability negotiation, context management, authentication, and asynchronous task control is positioned as foundational for scalable, secure agentic systems.

Applications and Use Cases

The Agentic Web is shown to enable three core functional paradigms: transactional (autonomous execution of web-based services), informational (autonomous knowledge discovery and synthesis), and communicational (inter-agent collaboration and negotiation). The paper surveys current and emerging applications, including:

• Agent-as-Interface: AI-augmented browsers (Opera Neon, Perplexity Comet, Browser Dia, Microsoft Copilot, NLWeb) that provide context-aware assistance and task orchestration.
• Agent-as-User: Autonomous agents (ChatGPT Agent, Anthropic Computer Use, Google Project Mariner, Genspark Super Agent) that operate as proxies, executing complex workflows across web and desktop environments.
• Agent-with-Physics: Embodied agents (RT-1, RT-2, RT-X, PaLM-E, Mobile ALOHA) that bridge digital and physical tasks, integrating high-level planning with real-world actuation.

The convergence of API-based and GUI-based automation, the rise of hybrid architectures, and the extension to embodied systems are identified as key trends.

Safety, Security, and Governance

The paper provides a rigorous taxonomy of safety and security threats across cognitive, interaction, and economic layers. Unique risks such as persuasion-based goal drift, knowledge base poisoning, context injection, service registry poisoning, and transaction authority abuse are analyzed. The cascading nature of threats—vertical, horizontal, and temporal—is emphasized, necessitating zero-trust architectures, adaptive defenses, and cascade prevention.

Red-teaming methodologies, both human-involved and automated (LLM-driven), are discussed as essential for vulnerability discovery and robustness evaluation. The development of reasoning-based and lifelong guardrails (ThinkGuard, GuardReasoner, AGrail, LlamaFirewall, GuardAgent), safe decoding, and access control mechanisms (Progent, SudoLM) is presented as critical for operational safety. The need for certifiable, environment-grounded defenses and scalable, adaptive evaluation frameworks (SafeArena, ST-WebAgentBench, Agent-SafetyBench) is highlighted.

Open Challenges and Future Directions

The authors identify a comprehensive set of open problems spanning foundational cognition, learning, multi-agent coordination, human-agent alignment, systemic risk, and socio-economic impact. Key challenges include:

• Robust, long-horizon planning and memory management for agents
• Secure and reliable tool use under adversarial conditions
• Reward design and continual learning without catastrophic forgetting
• Standardized, expressive communication protocols for global interoperability
• Trust and reputation mechanisms in decentralized agent ecosystems
• Effective human-in-the-loop oversight and preference elicitation
• Safety, security, and error recovery in open, dynamic environments
• Viable business models and equitable economic distribution in an agentic economy

The paper argues that addressing these challenges requires a systems-thinking approach, recognizing the interdependencies between technical, economic, and governance domains.

Conclusion

"Agentic Web: Weaving the Next Web with AI Agents" provides a detailed, multi-dimensional framework for understanding and engineering the next phase of the Internet as an agent-mediated, autonomous, and interactive ecosystem. The work synthesizes historical analysis, conceptual modeling, algorithmic innovation, system architecture, and socio-economic considerations, offering a roadmap for both researchers and practitioners. The implications are profound: the Web is being redefined from a human-readable, content-centric medium to an agent-native, action-oriented substrate, with far-reaching consequences for information access, digital commerce, and the structure of online interaction. The realization of this vision will depend on advances in agent cognition, protocol standardization, safety engineering, and the development of new economic and governance models.