[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83786-en":3,"doc-seo-83786-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83786,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Agentic IoT Architectures Applications and Challenges Toward the Internet of Agents","AI integration in Internet of Things (AIoT) systems is shifting from passive sensing to intelligent capabilities such as anomaly detection, predictive maintenance, classification, forecasting, and optimization. Many existing approaches remain limited to task-specific models, leaving system-wide real-time reasoning, adaptive planning, autonomous coordination, learning, tool use, and contextual decisions insufficient. The paper introduces Agentic IoT as a next-generation cognitive paradigm combining autonomous agent perception, reasoning, planning, learning, and action with cyber-physical systems across device–edge–fog–cloud.","Agentic IoT: Architectures, Applications, and Challenges Toward the Internet of Agents  \nR¨umeysa Hilal Sevinc¸, Bahaeddin T¨urko˘glu, and ˙Ibrahim Kk  \narXiv :2607 .042 19v 1 [ cs .AI ] 5 Jul 2026  \nAbstract—The integration of AI into Internet of Things (AIoT) systems has gradually transformed them from passive data collection infrastructures into intelligent systems capable of anomaly detection, predictive maintenance, classification, forecasting, and optimization. However, most existing solutions still rely on task-specific models that infer from sensor data; thus, system-wide capabilities such as real-time reasoning, adaptive planning, autonomous coordination, learning, tool use, and contextual decision-making remain limited. This paper examines Agentic IoT as a next-generation cognitive IoT paradigm that integrates the perception, reasoning, planning, learning, and action capabilities of autonomous AI agents with cyber-physical systems. Agentic IoT aims to transform IoT from data-centric sensing and inference infrastructures into distributed cognitive agent ecosystems operating across the device/edge–fog–cloud continuum. The paper first grounds this transition as a paradigm shift and positions Agentic IoT in relation to AIoT, edge intelligence, multi-agent systems, and the Internet of Agents. It then systematically reviews current studies, presents a holistic architectural framework, discusses domain-specific application potential, and identifies key technical, operational, and research challenges together with future research directions.  \nIndex Terms—Agentic IoT, Internet of Agents, Internet of Things, Autonomous Agents, Large Language Models(LLMs)  \nI. INTRODUCTION  \nIn recent years, rapid developments in Large Language Models (LLMs), autonomous AI agents, and the design and development of intelligent systems have initiated a fundamental transformation [1] . While traditional machine and deep learning models effectively perform classification, prediction, or optimization tasks, today’s agentic AI systems are capable of perceiving contextual information, performing reasoning, creating multi-step plans, utilizing external tools, learning from feedback, and collaborating with other agents to accomplish complex goals [2] . Thanks to these advanced capabilities, a significant transformation is currently taking place from reactive AI systems toward a world of autonomous and goaloriented intelligent agents.  \nThe implications of this transformation are particularly significant for the Internet of Things (IoT) . As the number of heterogeneous devices connected to IoT increases, the management of heterogeneous devices, dynamic operating conditions, and real-time decision-making requirements have also increased exponentially [3] . Smart-X approaches in many domains, such as smart homes, energy smart environments, smart cities, intelligent transportation networks, and precision  \nThe authors are with the Department of Artificial Intelligence and Data Engineering, Ankara University, Ankara, T¨urkiye.  \nCorresponding author: ˙Ibrahim Kk (e-mail: [ikok@ankara.edu.tr](ikok@ankara.edu.tr)) .  \nagriculture applications, increasingly highlight the requirements for distributed decision-making, contextual awareness, real-time coordination, autonomous operation, and adaptation to changing environmental conditions [4] . Therefore, nextgeneration IoT systems are expected not only to collect data and provide communication, but also to understand context, make autonomous decisions, coordinate their actions, and adapt to changing environmental conditions.  \nIn line with these needs in the IoT domain, as in many other fields, new concepts have emerged aiming to enable autonomous agents to operate effectively in large-scale and distributed systems [5] . For example, recent developments in agentic AI have inspired the emergence of a broader vision referred to as the Internet of Agents (IoA) [6] . IoA evolves the traditional Internet paradigm from ","cbCaioP8LCLvqDRA","https://ap.wps.com/l/cbCaioP8LCLvqDRA","pdf",341857,1,14,"English","en",105,"# Introduction\n## Motivation for agentic AI in IoT\n## Internet of Agents vision\n## From AIoT task models to Agentic IoT ecosystems\n## System characteristics and challenges","[{\"question\":\"What problem does Agentic IoT address compared with existing AIoT solutions?\",\"answer\":\"Agentic IoT targets the gap where many AIoT solutions rely on task-specific models and therefore provide limited system-wide reasoning, planning, coordination, learning, tool use, and contextual decision-making.\"},{\"question\":\"How does Agentic IoT extend the Internet of Agents concept into IoT systems?\",\"answer\":\"Agentic IoT integrates autonomous agent capabilities—perception, reasoning, planning, learning, and action—into cyber-physical IoT ecosystems so that devices, edge, and cloud jointly operate as distributed cognitive agent systems.\"},{\"question\":\"Which architectural and research challenges does the paper plan to cover?\",\"answer\":\"The paper reviews current studies, proposes a holistic architectural framework, evaluates domain-specific application potential, and identifies key technical, operational, and research challenges along with future research directions.\"}]",1784190396,35,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"agentic-iot-architectures-applications-and-challenges-toward-the-internet-of-agents","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/agentic-iot-architectures-applications-and-challenges-toward-the-internet-of-agents/83786/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does Agentic IoT address compared with existing AIoT solutions?","Question",{"text":75,"@type":76},"Agentic IoT targets the gap where many AIoT solutions rely on task-specific models and therefore provide limited system-wide reasoning, planning, coordination, learning, tool use, and contextual decision-making.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does Agentic IoT extend the Internet of Agents concept into IoT systems?",{"text":80,"@type":76},"Agentic IoT integrates autonomous agent capabilities—perception, reasoning, planning, learning, and action—into cyber-physical IoT ecosystems so that devices, edge, and cloud jointly operate as distributed cognitive agent systems.",{"name":82,"@type":73,"acceptedAnswer":83},"Which architectural and research challenges does the paper plan to cover?",{"text":84,"@type":76},"The paper reviews current studies, proposes a holistic architectural framework, evaluates domain-specific application potential, and identifies key 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