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 paradigm shift by introducing a framework that leverages autonomous AI agents to transform web interactions from static retrieval to goal-oriented workflows.
• It details a multi-dimensional model addressing intelligence, interaction, and economic dimensions, outlining key protocols like MCP and A2A for agent coordination.
• The study highlights critical infrastructural and governance challenges, offering insights for building secure, efficient multi-agent systems and evolving economic models.

Agentic Web: A Comprehensive Framework for the Next-Generation Internet

Introduction and Motivation

The paper "Agentic Web: Weaving the Next Web with AI Agents" (2507.21206) presents a systematic framework for understanding and architecting the Agentic Web—a paradigm shift in internet infrastructure and interaction, driven by autonomous AI agents. The authors trace the evolution of the Web from the PC and Mobile eras to the Agentic Web, highlighting the transition from human-driven, document-centric interaction to machine-mediated, goal-oriented workflows. The central thesis is that the Web is transforming into a distributed ecosystem where AI agents, powered by LLMs, act as persistent, autonomous intermediaries, planning, coordinating, and executing complex tasks on behalf of users.

Historical Context and Paradigm Shifts

The paper delineates three major eras in Web evolution:

• PC Web Era: Characterized by static content, directory-based navigation, and keyword search. User interaction was goal-directed and manual, with commercial models centered on search advertising and metrics like click-through rate.
• Mobile Web Era: Marked by the explosion of user-generated content, the rise of recommender systems, and the attention economy. Content discovery shifted from search to algorithmic curation, with engagement and conversion rates as key metrics.
• Agentic Web Era: Defined by the emergence of LLM-powered agents capable of autonomous, multi-step task execution. The Web becomes an action network, with agents orchestrating workflows, negotiating with services, and operating in a dynamic, agent-centric economic model.

The authors argue that this transition is not merely technological but fundamentally alters the architecture, economics, and user experience of the Web. Notably, the Agentic Web introduces the "Agent Attention Economy," where services compete for invocation by agents rather than human clicks.

Conceptual Framework: Intelligence, Interaction, and Economy

The core of the paper is a three-dimensional model for the Agentic Web:

1. Intelligence Dimension: Focuses on the cognitive capabilities required for agent autonomy—contextual understanding, long-horizon planning, adaptive learning, self-monitoring, and multimodal integration. Agents must move beyond reactive execution to proactive, strategic behavior, leveraging both in-parameter knowledge and external tools.
2. Interaction Dimension: Addresses the shift from static, human-mediated hyperlinks to dynamic, semantic, and protocol-driven agent interactions. The emergence of agent-native protocols (e.g., Model Context Protocol [MCP], Agent-to-Agent [A2A]) enables persistent, context-rich, and asynchronous communication, supporting tool orchestration and multi-agent collaboration.
3. Economic Dimension: Explores the rise of machine-native economies, where agents autonomously generate, exchange, and allocate value. This includes agent-driven marketplaces, decentralized trust mechanisms, and new forms of incentive alignment. The paper highlights the need for new commercial models, such as service invocation fees and agent-targeted advertising, and discusses the governance challenges posed by autonomous economic actors.

Algorithmic and Systemic Transitions

The authors provide a detailed analysis of the algorithmic shifts underpinning the Agentic Web:

• From User-Centric Retrieval to Agentic Information Acquisition: Traditional IR models (e.g., TF-IDF, BM25, PageRank) are supplanted by agent-driven, context-aware retrieval pipelines, including RAG architectures, self-reflective retrieval, and tool-augmented reasoning.
• From Static Recommendation to Agent Planning: Recommendation systems evolve from collaborative filtering and matrix factorization to agentic planning frameworks (e.g., ReAct, Plan-and-Act, memory-augmented planning), enabling multi-step, goal-driven workflows.
• From Single-Agent Execution to Multi-Agent Coordination: The paper surveys the transition from MDP-based and contextual bandit models to multi-agent orchestration frameworks (e.g., AutoGen, OWL, Octotools), emphasizing distributed intelligence, role specialization, and emergent collaboration.

On the systems side, the paper identifies critical infrastructural requirements:

• Agent Discovery and Service Registries: Dynamic, capability-based discovery mechanisms are needed for just-in-time agent recruitment and collaboration.
• Semantic APIs and Protocols: Agent-oriented APIs must expose machine-readable semantics, enabling autonomous comprehension and invocation.
• Billing and Resource Attribution: Fine-grained, auditable accounting frameworks are required to support economic sustainability and user trust.
• Quality of Service (QoS) and Service Requirement Zones (SRZs): The network must evolve from best-effort delivery to intent-driven, task-specific resource orchestration, accommodating diverse agent requirements.

Communication Protocols: MCP and A2A

The paper provides an in-depth exposition of two leading agentic communication protocols:

• Model Context Protocol (MCP): Standardizes agent-to-tool/resource interactions, supporting capability negotiation, session management, and real-time notifications. MCP enhances semantic accuracy and interoperability in tool invocation.
• Agent-to-Agent (A2A): Facilitates direct, asynchronous, and authenticated agent-to-agent communication, with structured task/message/artifact management and decentralized identity. A2A is optimized for multi-agent coordination, context consistency, and robust state tracking.

These protocols address the limitations of HTTP/RPC in supporting persistent, semantically rich, and multi-party agentic workflows.

Applications and Early Deployments

The Agentic Web enables three primary modalities:

• Transactional: Autonomous execution of goal-oriented tasks (e.g., booking, purchasing, scheduling) via direct agent-service interaction.
• Informational: Persistent, adaptive knowledge discovery and synthesis (e.g., deep research agents, cross-platform monitoring).
• Communicational: Structured inter-agent negotiation, delegation, and collaboration (e.g., multi-institutional research, supply chain coordination).

The paper surveys both Agent-as-Interface (e.g., Opera Neon, Perplexity Comet, Microsoft NLWeb) and Agent-as-User (e.g., ChatGPT Agent, Anthropic Computer Use, Google Project Mariner) paradigms, noting the trend toward hybrid architectures that combine API-based and GUI-level automation.

The Agent-with-Physics extension is also discussed, highlighting the integration of embodied agents (e.g., RT-1/2/X, PaLM-E, Mobile ALOHA) that bridge digital and physical environments.

Safety, Security, and Governance

A significant portion of the paper is devoted to the risks and governance challenges of the Agentic Web:

• Threat Taxonomy: The authors categorize threats across cognitive (goal drift, knowledge poisoning), interaction (context injection, registry poisoning), and economic (transaction abuse, market manipulation) layers, emphasizing the potential for cross-layer and temporal cascades.
• Red Teaming and Evaluation: Both human-in-the-loop and automated (LLM-driven) red teaming are advocated for vulnerability discovery. The paper reviews emerging benchmarks (e.g., SafeArena, ST-WebAgentBench, Agent-SafetyBench) and highlights the need for scalable, adaptive safety evaluation.
• Defensive Strategies: The authors discuss inference-time guardrails (reasoning-based, lifelong learning), controllable generation and planning (safe decoding, access control), and the challenges of efficiency, generalizability, and certifiable defense.

Open Challenges and Future Directions

The paper concludes with a taxonomy of open problems:

• Single-Agent Cognition: Robust long-horizon planning, structured memory, and secure tool use remain unsolved.
• Learning and Adaptation: Reward design, continual learning, and interactive grounding are bottlenecks for dynamic agent improvement.
• Multi-Agent Ecosystems: Standardized communication, decentralized trust, and coordination architectures are required for scalable agentic systems.
• Human-Agent Alignment: Disambiguating user intent, eliciting preferences, and designing effective oversight mechanisms are critical for safe deployment.
• Systemic Risk and Robustness: Ensuring safety, error recovery, and secure autonomous payments is essential for real-world adoption.
• Socio-Economic Impact: The transition from advertising-based to agent-centric business models, and the broader implications for labor markets and economic equity, are highlighted as areas for further research.

Conclusion

"Agentic Web: Weaving the Next Web with AI Agents" provides a comprehensive, multi-layered framework for understanding and engineering the next generation of the internet. By systematically analyzing the historical evolution, conceptual foundations, algorithmic and infrastructural requirements, and safety/governance challenges, the paper establishes a rigorous foundation for both academic research and practical development in agentic systems. The implications extend beyond technical innovation, demanding new approaches to economic modeling, regulatory oversight, and human-AI collaboration. The Agentic Web, as articulated, represents a decisive inflection point in the trajectory of digital ecosystems, with profound consequences for the future of autonomous intelligence and collective computation.