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Agentic Web: Weaving the Next Web with AI Agents

Yingxuan Yang, Mulei Ma, Yuxuan Huang, Huacan Chai, Chenyu Gong, Haoran Geng, Yuanjian Zhou, Ying Wen, Meng Fang, Muhao Chen, Shangding Gu, Ming Jin, Costas Spanos, Yang Yang, Pieter Abbeel, Dawn Song, Weinan Zhang, Jun Wang

arXiv:2507.21206v1cs.AIcs.LG

TL;DR

The paper addresses the gap in systematically reinterpreting Web foundations for autonomous, collaborative agents. It develops a structured framework for the Agentic Web, examining its evolution, enabling technologies, system architecture, and risks; it concludes that agentic systems extend the Web toward adaptive information processing but face substantial safety and security challenges.

  • Problem

    Existing literature lacks a systematic analysis and redefinition of Web fundamentals for an agent-driven future, including protocols, semantics, indexing, search, and recommendation.

  • Method

    The paper reviews Web evolution, conceptualizes the Agentic Web, proposes a three-dimension model, surveys enabling techniques, and analyzes architectural, security, and governance challenges.

  • Results

    The paper concludes that the Agentic Web extends static content retrieval into complex, adaptive information processing through coordinated autonomous agents.

  • Takeaways & Limitations

    Agentic Web development requires systems that support agent discovery, coordination, dynamic workflows, and security across distributed services.

  • Takeaways & Limitations

    Agent guardrails remain difficult to generalize across tasks and environments while adapting to evolving behaviors and cumulative multi-turn risks.

Abstract

from arXiv · show

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.

1 Introduction

The Agentic Web shifts the Web from human-led interaction with static content toward autonomous agents that plan, coordinate, and execute goal-directed workflows across services. This paper frames that shift as a response to emerging agent capabilities and a gap in systematically reinterpreting Web foundations for agent-driven systems.

  • AI agents powered by LLMs can perceive environments, reason, plan, remember, and act autonomously across digital systems.Their growing use spans research, software development, customer support, and personal productivity.
  • The Agentic Web is a distributed ecosystem where autonomous agents persistently plan, coordinate, and execute tasks on behalf of users.Web resources and services become agent-accessible, supporting continuous agent-to-agent interaction and dynamic information exchange.
  • Unlike the Traditional Web, the Agentic Web mediates information access, transactions, and communication through agents capable of reasoning, planning, and acting for users.Interaction shifts from short-term exchanges with static content to sustained sequences of coordinated actions across services and domains.
  • An Agentic Web task involves delegation, negotiation, planning, adaptation, and multi-agent collaboration across domains before results return to the user.The process cycle includes task planning, agent and tool identification, inter-agent discussion, execution, and result reporting.
  • Agentic Web systems extend beyond static retrieval by enabling adaptive information processing through APIs, browser access, and protocols such as MCP.A Deep Research agent can plan multi-stage workflows, retrieve technical content, and assemble findings for a user.
  • The paper argues that Web foundations, including protocols, semantics, indexing, search, and recommendation, require reinterpretation for agent autonomy and collaboration.Its framework reviews Web evolution, conceptualizes the Agentic Web, examines enabling tasks, and discusses system design principles.

2 Historical Evolution of the Web

The Web evolved from manually navigated, search-centered pages to recommendation-driven mobile consumption and agent-mediated task execution. Across these eras, information flows, user behavior, and commercial models changed gradually and often overlapped.

  • PC Web Era: The PC Web centered on static pages, hierarchical browsing, active queries, and keyword-based advertising.Users had to define goals and invest time manually locating information, while search engines and advertising progressively improved retrieval and commercialization.
  • Cross-era transition: The transition across web eras was gradual, with technologies, features, and business models overlapping rather than changing at sharply separated boundaries.The timeline presents the eras as analytically distinct but historically coexisting.
  • PC Web Era: PageRank improved search relevance by evaluating webpage authority through hyperlink structures rather than keyword matching alone.This approach laid the foundation for search engines such as Google and increased user reliance on search.
  • Mobile Web Era: The Mobile Web responded to explosive User-Generated Content by shifting from intent-driven search toward recommendation-driven, personalized discovery.Content growth overwhelmed manual search, while mobile usage favored shorter sessions and instant, context-aware experiences.
  • Agentic Web Era: The Agentic Web shifts competition from human clicks toward agent invocation, with services using ranking, recommendation, and bidding mechanisms to influence agent selection.Its commercial logic includes agent-oriented advertising, capability relevance, invocation frequency, and successful task completion.
  • Agentic Web Era: The Web is reconfigured from human-readable documents and curated feeds toward agent-native knowledge dynamically synthesized, shared, and executed by autonomous systems.This emerging structure includes continuous agent operation and content produced for agent parsing, reasoning, and orchestration.

3 The Agentic Web

The Agentic Web is presented as an autonomous, agent-mediated environment that changes how users delegate tasks, how agents interact, and how information is organized. Its foundations include autonomous intermediaries, machine-readable interfaces, interoperable protocols, and semantic discovery across services.

  • 3 The Agentic Web: Users increasingly delegate high-level goals while agents perform the necessary operations across multiple services.This changes interaction from manual navigation and stepwise execution toward delegated orchestration.
  • 3.1 Core Conditions: Building the Agentic Web requires autonomous intermediaries, standardized machine-readable interfaces, and direct value exchange between systems.These conditions depend on semantic interfaces, protocol interoperability, and dynamic capability discovery and orchestration.
  • 3.1 Core Conditions: The paper organizes the Agentic Web around three interrelated dimensions: Intelligence, Interaction, and Economy.The framework treats these dimensions as core requirements for autonomous operation in digital ecosystems.
  • 3.2 Interaction Transformation: Agents proactively discover resources, identify capabilities, and form dynamic connections through semantic relevance rather than static hyperlinks.They can monitor environments, detect opportunities, and negotiate adaptively across services to pursue goals.
  • 3.2.2 Changing Information Structures: Information structures shift from document-based storage and hyperlink navigation toward in-model knowledge, semantic discovery, and agent-generated outputs.Agents can retrieve, reason over, generate, and consume content designed for other agents, creating more autonomous information flows.
  • 3.2 Operational Perspectives: Agents operate both as autonomous web users and as interfaces that translate human intentions into executable multi-step workflows.These roles are complementary: one system can navigate web resources independently while bridging human goals and execution.

3.3 Three Conceptual Dimensions of the Agentic Web

The Agentic Web is organized around Intelligence, Interaction, and Economy: agents reason and adapt, connect through semantic mechanisms, and create value autonomously across digital ecosystems.

  • The framework defines three interrelated dimensions: Intelligence, Interaction, and Economy.Together, they describe the core requirements for autonomous operation within digital ecosystems.
  • Intelligence Dimension: Intelligence equips agents with transferable cognitive abilities for interpreting context, planning across services, learning from feedback, monitoring reasoning, and integrating multiple modalities.These capabilities allow agents to handle ambiguity, recover from failure, and scale across services.
  • Intelligence Dimension: Agents are not passive executors: they independently interpret, strategize, and adapt within open-ended digital environments.Without these abilities, agents cannot handle real-world ambiguity, recover from failure, or scale across services.
  • Interaction Dimension: Interaction replaces static, human-mediated links with semantic protocols, runtime service discovery, safe tool orchestration, and agent-to-agent collaboration.These mechanisms provide the operational substrate for adaptive interaction with heterogeneous digital environments.
  • Economic Dimension: The Economic Dimension makes agents autonomous economic actors that coordinate, produce, transact, and create machine-oriented value directly with one another.This supports self-organizing digital economies, while high-stakes transactions raise urgent questions about liability, transparency, ethics, accountability, and fairness.

4 Algorithmic Transitions for the Agentic Web

The Agentic Web transforms core algorithms from reactive, human-initiated pipelines into adaptive systems that acquire information proactively, plan toward goals, and coordinate multiple agents.

  • Three foundational transitions define the algorithmic evolution: proactive information acquisition, goal-oriented agent planning, and multi-agent coordination.These transitions replace fixed pipelines requiring explicit intervention with adaptive strategies that navigate uncertainty and complexity autonomously.
  • Information Retrieval to Agentic Information Acquisition: Agentic retrieval dynamically determines what information to obtain, when to obtain it, and which modalities, tools, APIs, or procedures to use.This reframes retrieval as an active component of autonomous cognition rather than static keyword-based lookup.
  • Information Retrieval to Agentic Information Acquisition: RAG architectures ground language-model outputs in external content by retrieving relevant passages and integrating them into generation.Fusion-in-Decoder uses sparse or dense indexes, while FLARE iteratively forms pseudo-queries from low-confidence predictions.
  • Recommendation to Agent Planning: Recommendation shifts from isolated user–item preference prediction to multi-step planning and execution oriented toward complex goals.Agentic approaches integrate high-level reasoning with executable actions, memory, self-reflection, and long-horizon navigation.
  • Single-Agent Execution to Multi-Agent Coordination: Multi-agent frameworks enable task decomposition, role specialization, communication, shared goals, and orchestrated execution for problems difficult to solve in isolation.Systems such as AutoGen, Alita, OWL, and Octotools emphasize distributed intelligence, modularity, flexibility, and specialization.

5 Systematic Transitions of the Agentic Web

Supporting the Agentic Web requires redesigning traditional web infrastructure for persistent, context-aware, dynamically discoverable, semantically interoperable agent execution.

  • Traditional stateless protocols, user-initiated interfaces, and static interaction models are poorly suited to autonomous agents.Agentic computation requires continuous contextual awareness, persistent sessions, dynamic service discovery, and semantically rich interaction protocols.
  • The paper identifies system-level transformations that operationalize agent-native execution across web-scale environments.It examines current protocol and runtime limitations, infrastructure requirements for persistent agents, and communication standards for agent-to-agent interaction.
  • Deploying autonomous agents at web scale introduces core system challenges that must be addressed before the Agentic Web can operate effectively.The section frames these challenges as requirements for moving from passive web infrastructure toward agent-native execution.

5.1 Motivation for an Agentic Web System

The Agentic Web requires infrastructure that can dynamically discover collaborators, interpret semantic service interfaces, account for distributed activity, and provision task-specific quality of service.

  • Agent discovery is a primary impediment because agents are ephemeral and lack fixed network locations.Dynamic just-in-time matchmaking must assess collaborators’ skills, readiness, and suitability for specific operational demands.
  • Current APIs provide syntactic but not semantic interoperability, limiting autonomous interpretation of their functions and purposes.Agent-oriented APIs would embed machine-readable semantics such as ontologies or logical specifications within API definitions.
  • Distributed delegation and service consumption make resource attribution and billing difficult across chains of agent interactions.The infrastructure needs persistent, reliable, and auditable methods for monitoring interactions and associating computational usage.
  • Agentic infrastructure must evolve from best-effort networking toward intelligent, personalized quality-of-service provisioning.Traditional network-centric optimization around aggregated KPIs does not express the task-specific demands of autonomous operations.
  • Service Requirement Zones: A Service Requirement Zone profiles a task’s quality-of-experience needs across eight dimensions, including cost, delay, security, data rate, and knowledge.Smaller zones indicate more stringent requirements; deep research and ticket purchase tasks illustrate distinct orchestration needs.
  • Service Requirement Zones: Supporting Service Requirement Zones requires granular, task-level provisioning that can adjust resources dynamically and handle multimodal demands.The infrastructure must interpret diverse service profiles rather than treat all data flows as equivalent.

5.2 Toward a Next-Generation Agentic Web System

The Agentic Web requires an execution-oriented architecture that translates high-level user goals into coordinated digital actions. Its system combines a User Client, Intelligent Agent, and Backend Services, supported by routing, orchestration, and emerging planning approaches.

  • The Agentic Web must evolve from a content-centric medium into execution-oriented infrastructure supporting agent-native interaction, persistent context, and tool orchestration.
  • Its core architecture integrates a User Client, Intelligent Agent, and Backend Services to translate user goals into executable actions.
  • The User Client mediates human-agent interaction, while the Intelligent Agent interprets intent, decomposes objectives, and selects backend tools.
  • Backend Services, tools, and plugins provide distributed, extensible computational and domain-specific capabilities that agents can invoke.
  • Roadmap of the Agentic Web System: The proposed architecture adds demand-skill mapping, real-time task routing, and a cross-agent billing ledger for autonomous, value-aware service orchestration.
  • Interaction Process Example: A travel-planning workflow demonstrates request parsing, sub-task decomposition, MCP-based service calls, result synthesis, and direct map feedback.

5.3 Agentic Communication

Agentic Web tasks exceed traditional protocol assumptions because they require semantic coordination, persistent context, and asynchronous multi-party interaction. MCP and A2A provide complementary protocol models for agent-resource and agent-agent communication.

  • Multi-step agentic tasks require communication mechanisms supporting semantic interoperability, persistent task states, and asynchronous multi-party interaction.
  • HTTP and RPC primarily support data transport or procedure calls, leaving insufficient support for semantic alignment, contextual understanding, and long-running interactive workflows.
  • MCP: MCP standardizes interactions between agents and non-agent resources through recognizable service units that expose tools, resources, and prompts.
  • A2A: A2A enables direct heterogeneous-agent interaction through public AgentCards that describe capabilities, interfaces, and communication specifications.
  • MCP: MCP’s client-server mediation reduces fragmentation in tool invocation and improves semantic accuracy for agent-resource interactions.
  • A2A: A2A tracks context across tasks, turns, and agents while supporting asynchronous communication and task-progress updates.

5.4 Emerging Directions of Agentic Web Systems

Emerging Agentic Web systems challenge established assumptions about browser interaction and digital commerce. Key open questions concern user control and trust in delegated execution, alongside billing models for variable multi-agent computation.

  • The Agent Browser: Agent browsers replace passive, user-driven navigation with proactive, goal-oriented execution of delegated objectives.
  • The Agent Browser: Delegated autonomy creates interface and trust challenges because agents may follow dynamic, non-linear execution paths that users cannot easily understand.
  • Billing Challenge: Advanced agent tasks incur variable costs from LLM tokens, third-party API calls, and prolonged high-performance computing use.
  • Billing Challenge: A central unresolved problem is designing billing that remains equitable for users and sustainable for service providers.

6 Applications of the Agentic Web

The Agentic Web supports transactional, informational, and communicational activity through agents that execute tasks, reason over knowledge, and coordinate with other systems. Applications range from semantic web interfaces and autonomous browsing to hybrid digital-physical agents and robots.

  • The three core paradigms are transactional task execution, informational retrieval and synthesis, and communicational coordination among agents and systems.
  • Transactional Applications: Transactional agents can coordinate multiple providers and complete compound activities such as flights, accommodations, and car rentals.
  • Agent-as-Interface: Microsoft NLWeb proposes schema- and MCP-based semantic website interfaces that reduce reliance on brittle scraping and improve transparency in web automation.
  • Agent-as-User: Anthropic Computer Use controls desktop and web interfaces through vision-based perception and GUI manipulation without backend APIs, reaching 22.0% success with reasoning steps on OSWorld.
  • Agent-as-User: Google Project Mariner combines long-horizon research, multi-step workflows, autonomous form filling, and natural-language action explanations, achieving 83.5% on WebVoyager.
  • Emerging Directions: Emerging systems increasingly combine API calls and GUI automation, while agent-with-physics systems extend agentic infrastructure toward coordinated digital and physical operation.
  • Agent-with-Physics: Physical agents improve generalization across tasks but must address safety, latency, actuation uncertainty, and physical affordances.

7 Risks, Security & Governance

Agentic Web safety spans LLMs, agent frameworks, safety infrastructure, and diverse devices in a distributed ecosystem. The paper organizes its safety treatment around threats, red teaming, defenses, and evaluation.

  • The safety ecosystem integrates LLMs, agent frameworks, and safety infrastructures across laptops, desktops, servers, and mobile phones.The figure emphasizes secure deployment and communication across these interconnected components and devices.
  • Agents interact with cloud services, third-party tools, and one another to perform goal-directed tasks on users’ behalf.This distributed interaction makes consistent cross-platform security protocols important.
  • The paper analyzes threats, introduces red-teaming methods, presents defense strategies, and discusses evaluation techniques for agentic-web security.These topics are organized across Sections 7.1 through 7.4.

7.1 Safety and Security Threats

Agentic Web threats arise from autonomous operation across open, persistent, and economically consequential environments. The paper organizes them by architectural layer while emphasizing cross-layer cascades and a shift toward adaptive security.

  • Threat landscape: Agentic Web agents create novel risks by operating autonomously across the open internet, executing real transactions, and maintaining persistent states.The risk evolution is presented as a shift from controlled systems toward autonomous web operations.
  • Threat landscape: Threats are organized across intelligence, interaction, and value layers according to their primary attack vectors.The layers cover cognitive reasoning attacks, protocol and communication attacks, and autonomous transaction and economic risks.
  • Threat cascades: Threats can cascade vertically across cognitive, protocol, and economic layers, horizontally between agents, and temporally through persistent memory.For example, goal drift can lead to protocol exploitation and unauthorized purchases.
  • Threat landscape: Agentic Web knowledge-base poisoning extends beyond prompt injection to web-scale information corruption.The paper distinguishes this scope from existing frameworks such as OWASP’s Agentic AI Threat Model and CSA’s MAESTRO.
  • Security implications: Security requires zero-trust architecture, adaptive defenses, and cascade prevention rather than perimeter security, static rules, and incident isolation.These three shifts target the distributed, adaptive, and propagating nature of agentic-web threats.
  • Security implications: Traditional bounded, stateless security models are incompatible with persistent web agents, requiring adaptation for internet-scale deployments.The paper identifies enterprise patterns such as MCP as needing this adaptation.
  • Security implications: Quantitative models for cascade probability and impact remain underdeveloped, especially for emergent behavior in complex multi-agent systems.Cross-jurisdictional governance and continuously evolving threats add further research challenges.

7.2 Safety and Security Red Teaming

Red teaming is presented as a way to uncover agentic-web vulnerabilities before deployment, with automated and multi-agent methods improving scale and coverage. The paper also stresses that these approaches remain difficult to secure and generalize.

  • Motivation: Agentic-web deployments create risks including privacy leakage, fairness concerns, hallucination, and permission escalation.These risks arise as agents operate across complex, multi-platform environments on users’ behalf.
  • Human red teaming: Red teaming simulates adversarial behavior to uncover vulnerabilities and evaluate system robustness before real-world deployment.Human-involved approaches provide domain expertise but are costly, time-consuming, and limited in diversity and scalability.
  • Automatic red teaming: Automated red teaming is increasingly important because human-designed rules, labels, and vulnerability searches struggle to scale across dynamic, high-autonomy systems.Relevant scenarios include ticketing, financial transactions, and cross-platform web operations.
  • Automatic red teaming: LLMs can generate adversarial prompts and scenarios, while reinforcement-learning approaches can harden decision boundaries and improve robustness.MART is described as an automatic framework for adversarial scenario generation, and RL-based methods support resistance to red-teaming attacks.
  • Automatic red teaming: Nie et al. report that their RL-based red-teaming method outperforms strong baselines in exposing model information leakage.The method trains an adversarial agent with a designed reward function to generate diverse adversarial examples.
  • Emerging directions: Backdoor-triggered, retrieval-augmented, agent-based, and data-poisoning methods expose hidden vulnerabilities across domains including shopping, healthcare, question answering, and autonomous driving.These approaches target privacy leakage, compromised safety, and instruction-based weaknesses.
  • Open challenges: LLM-driven red teaming offers scalable coverage, but automated methods may be unreliable when complex reasoning, contextual understanding, or ethical judgment is required.The paper frames the gap between human and automated red teaming as an open challenge.
  • Open challenges: Red-teaming frameworks can themselves be attacked during multi-agent interactions, misleading evaluation and leaking sensitive data.This creates risks for both safety assessment and trustworthiness.

7.3 Safety and Security Defense

Agentic-web defenses combine external guardrails with safer generation, planning, and access control. The paper argues that effective defenses must reason over goals and actions, learn continually, and address deployment-time uncertainty.

  • Guardrails: Guardrails are external mechanisms that identify and mitigate potentially harmful LLM inputs or outputs across different model implementations.Earlier systems often treated moderation as classification using rules, lexicons, or heuristics.
  • Guardrails: Agentic guardrails require deliberative reasoning and lifelong learnability to support context-sensitive oversight of autonomous behavior.Reasoning addresses nuanced goals and values, while lifelong learning adapts to new information, norms, and edge cases.
  • Inference-time guardrails: Reasoning guardrails assess intent, context, and risks through structured reasoning rather than merely predicting threat labels.They are presented as prerequisite to constraining multi-step planning and decision-making reliably.
  • Inference-time guardrails: Agentic guardrails govern action execution, overseeing or constraining how agents interact with external systems and environments.They differ from reasoning guardrails, which shape what an agent thinks and says.
  • Deployment challenges: Lifelong agent guardrails must adapt to evolving agent behavior and mitigate threats from external environments, adversarial states, and unsafe system interactions.This creates both technical and contextual challenges for deployment.
  • Controllable generation and planning: Safe decoding reshapes token distributions toward harmless outputs, while access-control systems enforce least privilege during tool invocation.Progent supports fine-grained policies, fallback behaviors, and automated policy updates.
  • Open challenges: Threat mitigation after deployment remains preliminary, with efficiency, generalizability, and reliable operation in open environments still unresolved.Reasoning-based guardrails improve robustness and interpretability but introduce additional inference overhead.

7.4 Safety and Security Evaluation

Agentic web safety evaluation remains underexplored, despite emerging benchmarks that assess misuse, policy compliance, robustness, and risk awareness. Existing results reveal substantial safety deficiencies alongside promising guardrail performance, while multimodal and reasoning safety remain open areas.

  • Agentic web safety evaluation remains largely underexplored compared with established web, LLM, multimodal, and robot-learning safety research.
  • SafeArena evaluates 250 safe and 250 harmful tasks across misinformation, cybercrime, and social-bias categories for LLM-based web agents.
  • ST-WebAgentBench evaluates enterprise-style web-agent safety across consent, preferences, scope boundaries, execution strictness, distribution shifts, and error recovery.
  • Agent-SafetyBench covers 349 interaction environments and 2,000 test cases across eight safety-risk categories, while GuardAgent achieves 98% accuracy on safety-critical tasks.
  • Further investigation is needed into multimodal agentic-web safety and reasoning safety for agentic-web agents.

8 Challenges and Open Problems

The Agentic Web requires more than stronger individual models: it demands reliable cognition, learning, coordination, interoperability, trust, alignment, and secure economic infrastructure. These challenges are interconnected and must be addressed as a system.

  • The Agentic Web faces interconnected challenges spanning individual cognition, multi-agent coordination, human-agent alignment, systemic security, and socioeconomic structures.
  • Foundational Challenges in Single-Agent Cognition and Autonomy: Multi-step reasoning and planning are foundational but brittle, while memory management must reconcile stateless LLMs, finite context windows, and cross-task knowledge retention.
  • Foundational Challenges in Single-Agent Cognition and Autonomy: External tools enable real-world agency but also create security vulnerabilities, motivating zero-trust architectures that validate inputs and tool outputs against security policies.
  • The Learning Conundrum: From Static Models to Dynamic Learners: Dynamic learning remains constrained by reward-design bottlenecks, catastrophic forgetting, and the specialization–generalization trade-off in interactive task learning.
  • Multi-Agent Coordination and Interoperability: Effective multi-agent systems require architectural choices, standardized communication protocols, and trust mechanisms for decentralized collaboration and delegation.
  • Human-Agent Alignment and Systemic Risks: Human-agent interaction must address ambiguous and evolving intent through preference discovery and oversight, while autonomous payments introduce major security, regulatory, and social hurdles.

9 Conclusion

The Agentic Web is presented as a shift from a passive information repository to a dynamic environment where autonomous systems perceive, reason, and execute goal-directed tasks. This transition distinguishes agentic AI from generative AI’s prompt-response paradigm.

  • The Agentic Web evolves the internet from a passive information repository into a dynamic environment of autonomous action.
  • Agentic systems perceive environments, reason through complex problems, and execute tasks to achieve specified goals.
  • This paradigm marks a shift from generative AI responding to human prompts toward agentic AI performing goal-directed actions.
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