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Journal articles on the topic 'Agentic AI'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Gaurav Samdani, Yawal Dixit, and Ganesh Vishwanathan. "Agentic AI in autonomous financial advisories." World Journal of Advanced Engineering Technology and Sciences 9, no. 1 (2023): 410–20. https://doi.org/10.30574/wjaets.2023.9.1.0138.

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Agentic AI strengthens financial technology by letting autonomous financial advisories use flexible systems to block and control their procedures. The new financial products deliver specific customer solutions in real-time, which make users' financial decisions better informed. Our study examines today's beneficial uses, development methods, and essential results of agentic AI technology in financial advising. Research studies look at current systems plus conduct real-world studies to show how agentic AI fares compared to usual technology. The research examines all the main ethical, security,

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Marius, Vlad Pop, Tonț Gabriela, Flonta Flavius-Viorel, and Flore Marius. "Agentic AI in STEM Education: Enhancing Cognitive Flexibility and Workforce Readiness." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 16, Special Issue 1 (2025): 239–49. https://doi.org/10.70594/brain/16.S1/20.

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This scientific article examines the transformative potential of Agentic AI in Science, Technology, Engineering, and Mathematics (STEM) education. It highlights how Agentic AI can enhance learning outcomes in these subjects, reduce cognitive load, and better prepare students for the demands of an AI-driven workforce. By utilising smart tools like GitHub Copilot, Agentic AI systems provide opportunities to improve STEM learning environments through personalised assistance, collaborative problem-solving, and skill development, all while automating repetitive tasks. This paper also discusses the

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Erukude, Sai Teja, Suhasnadh Reddy Veluru, and Viswa Chaitanya Marella. "AGENTIC AI - THE RISE OF AUTONOMOUS INTELLIGENT AGENTS IN THE ERA OF LLMS." Indian Journal of Computer Science and Engineering 16, no. 2 (2025): 9–16. https://doi.org/10.21817/indjcse/2025/v16i1/251602024.

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Agentic AI refers to AI systems that autonomously set and act towards these goals over time. The emergence of large language models (LLMs) has renewed interest in agentic architectures as LLMs are a “brain” that provides human-level reasoning capability for agents. This survey reviews the state of the agentic AI research area. We examine agentic AI’s definition and historical foundations, the theoretical underpinnings of agency, system architectures, and applications. We consider some of the leading LLM-agenting frameworks (Auto-GPT, BabyAGI, LangChain agents) and the essential components that

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Ahmed, Nisher, Md Emran Hossain, Zakir Hossain, Md Farhad Kabir, and Iffat Sania Hossain. "Understanding the Capabilities and Implications of Agentic AI in Surveillance Systems." Indonesian Journal of Advanced Research 4, no. 1 (2025): 91–110. https://doi.org/10.55927/ijar.v4i1.13682.

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Agentic AI can also mean the speeding up of new levels of surveillance systems the size of which has never been encountered before which grants automation decision making and realtime responses. Examples of stateful models of an agent are agentic AI, where an agentic AI isn't just a static function, but has capabilities to reason and learn with reference to the environment and goals. The paper explores the possible implications of embedding Agentic AI in surveillance systems, demonstrating how it could revolutionize monitoring, identification of threats, and response systems. Agentic AI: how t

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Karan, Khanna. "The Rise of Agentic AI in E-Commerce by Integrating into Customer Engagement, Hyper-personalization, And Revenue Growth." Journal of Advances in Developmental Research 15, no. 2 (2024): 1–13. https://doi.org/10.5281/zenodo.14993292.

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Agentic AI is rapidly transforming the ecommerce landscape 1. Unlike traditional AI systems that react to predefined rules or static inputs, agentic AI operates autonomously, learning from real-time data and optimizing outcomes in complex environments 1. This marks a significant leap forward 1, enabling ecommerce businesses to streamline operations, personalize customer experiences, and drive revenue growth 1. Traditional AI and automation primarily excel at analyzing data and presenting results. However, agentic AI goes a step further by combining these insights with autonomous action, allowi

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Pop, Marius Vlad, Gabriela Tonț, Flavius-Viorel Flonta, and Marius Flore. "Agentic AI in STEM Education: Enhancing Cognitive Flexibility and Workforce Readiness." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 16, no. 1 Sup1 (2025): 239. https://doi.org/10.70594/brain/16.s1/20.

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<span id="docs-internal-guid-5d638118-7fff-0289-7602-4076551a55e8"><span>This scientific article examines the transformative potential of Agentic AI in Science, Technology, Engineering, and Mathematics (STEM) education. It highlights how Agentic AI can enhance learning outcomes in these subjects, reduce cognitive load, and better prepare students for the demands of an AI-driven workforce. By utilising smart tools like GitHub Copilot, Agentic AI systems provide opportunities to improve STEM learning environments through personalised assistance, collaborative problem-solving, and ski

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Chawla, Chhavi, Siddharth Chatterjee, Sanketh Siddanna Gadadinni, Pulkit Verma, and Sourav Banerjee. "Agentic AI: The building blocks of sophisticated AI business applications." Journal of AI, Robotics & Workplace Automation 3, no. 3 (2024): 1. http://dx.doi.org/10.69554/xehz1946.

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Language models (LMs) like GPT-4’s ability to predict word sequences have made tasks such as summarisation and translation significantly easier. They often struggle, however, with complex reasoning tasks that require deliberate, multi-step processes. To address these limitations, the concept of agentic artificial intelligence (agentic AI) is introduced, where LMs are organised into workflows that mimic human-like iterative reasoning. This paper explores the four pillars of agentic AI frameworks: tool use, reflection, planning and multi-agent collaboration (MAC). Tool use allows LMs to access e

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Olujimi, Peter Adebowale, Pius Adewale Owolawi, Refilwe Constance Mogase, and Etienne Van Wyk. "Agentic AI Frameworks in SMMEs: A Systematic Literature Review of Ecosystemic Interconnected Agents." AI 6, no. 6 (2025): 123. https://doi.org/10.3390/ai6060123.

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This study examines the application of agentic artificial intelligence (AI) frameworks within small, medium, and micro-enterprises (SMMEs), highlighting how interconnected autonomous agents improve operational efficiency and adaptability. Using the PRISMA 2020 framework, this study systematically identified, screened, and analyzed 66 studies, including peer-reviewed and credible gray literature, published between 2019 and 2024, to assess agentic AI frameworks in SMMEs. Recognizing the constraints faced by SMMEs, such as limited scalability, high operational demands, and restricted access to ad

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Burnstine, Andrew. "Agentic AI and the Future of Fashion: Autonomous Creativity and Intelligent Systems." Archives of Business Research 13, no. 04 (2025): 51–64. https://doi.org/10.14738/abr.1304.18608.

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The fashion industry is undergoing a profound transformation driven by rapid advancements in artificial intelligence (AI). While generative AI has demonstrated its capabilities in content creation and design ideation, the emergence of agentic AI heralds a paradigm shift towards autonomous systems capable of sophisticated perception, decision-making, and action within complex and dynamic environments. Unlike traditional AI tools that require specific inputs and human oversight for each step, agentic AI possesses the capacity for continuous learning, goal-driven behavior, and intricate decision-

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Khanna, Karan. "Proactive fraud detection: Safeguarding customers with agentic AI." International Journal of Multidisciplinary Research and Growth Evaluation 5, no. 6 (2024): 1523–31. https://doi.org/10.54660/.ijmrge.2024.5.6-1523-1531.

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Agentic AI is rapidly emerging as a transformative force in the banking industry, poised to revolutionize how customers manage their finances. Unlike traditional AI applications that focus on automating specific tasks, agentic AI systems act as autonomous agents, capable of understanding customer needs, making informed decisions, and taking proactive actions to optimize their financial well-being. This article explores the potential of agentic AI in personalized financial management, examining its key benefits, use cases, and the challenges that lie ahead.

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Firoz Mohammed Ozman. "Systematic literature review on the rise of agentic AI in enterprise operations." International Journal of Frontiers in Science and Technology Research 8, no. 2 (2025): 001–15. https://doi.org/10.53294/ijfstr.2025.8.2.0025.

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The study aims to analyze Agentic AI's impact on enterprise operations, specifically emphasizing its benefits, challenges and strategic implementation. Agentic AI has shown the capability of independently interpreting data-driven tasks and aligning them to continuously changing business conditions regardless of human intervention. The major benefit of incorporating Agentic AI has been the ability to introduce transformation within supply chain management and the efficiency of enterprise resource planning. It has been highlighted that AI-driven ERP solutions have contributed towards improving a

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Burnstine, Andrew. "Autonomous Intelligence in Fashion: A Comprehensive Analysis of Agentic AI Across the Fashion Ecosystem." Asian Business Research Journal 10, no. 4 (2025): 31–37. https://doi.org/10.55220/25766759.405.

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The fashion industry is undergoing a paradigm shift with the emergence of agentic artificial intelligence (AI), a sophisticated class of intelligent systems exhibiting autonomous decision-making, continuous learning, and adaptive action with minimal human intervention. Moving beyond traditional AI applications in fashion focused on predictive analytics, generative tools, and supervised automation, agentic AI introduces a transformative paradigm wherein intelligent agents proactively navigate the complexities inherent in design, manufacturing, supply chain optimization, and consumer personaliza

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Awasthi, Yogesh. "Agentic AI Redefined: A New Paradigm in Artificial Intelligence." Journal of Software Engineering and Simulation 11, no. 6 (2025): 79–86. https://doi.org/10.35629/3795-11067986.

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The evolution of Artificial Intelligence (AI) from rule-based systems to deep learning has enabled significant technological advancements, but it has also raised complex questions about autonomy and agency. Agentic AI refers to AI systems capable of initiating goal-directed actions, making context-sensitive decisions, and adapting over time with minimal human oversight. This paper explores the conceptual boundaries of agentic AI and provides empirical analysis based on case studies from autonomous vehicles, intelligent tutoring systems, and AI-enabled robotics. By evaluating behavioural data a

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Timms, Alexander, Abigail Langbridge, Antonis Antonopoulos, Antonis Mygiakis, Eleni Voulgari, and Fearghal O'Donncha. "Agentic AI for Digital Twin." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29703–5. https://doi.org/10.1609/aaai.v39i28.35373.

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The complexity of the shipping industry, dynamic operational drivers, and diverse data sources present significant scalability challenges for digital twins. Agentic Large Language Models (LLMs) augmented with external tools offer a promising solution to accelerate digital twin adoption. Using pre-trained knowledge and reasoning capabilities, these LLMs autonomously select optimal tools and data streams for user-specific queries, enabling language to serve as a universal interface between digital twins and various stakeholders, from technicians to fleet managers. This interface facilitates real

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Asif Ali, Mohammad. "Efficient Underwriting Using Agentic AI." Software Engineering 12, no. 1 (2025): 1–13. https://doi.org/10.5923/j.se.20251201.01.

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Siva, Kumar Mamillapalli. "The Agentic AI Framework: Enabling Autonomous Intelligence." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 13, no. 1 (2025): 1–4. https://doi.org/10.5281/zenodo.15029753.

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Agentic AI marks a significant advancement in artificial intelligence, enabling systems to make autonomous decisions and adapt to changing circumstances. This paper provides a comprehensive overview of Agentic AI, exploring its underlying architecture, key functionalities, and diverse applications. It contrasts Agentic AI with traditional AI by highlighting its capacity for independent operation, goal setting, and environmental adaptation without continuous human oversight. The article examines real-world implementations across various sectors, such as robotics, healthcare, autonomous vehicles

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Tiwari, Alok. "Beyond Automation: The Emergence of Agentic Urban AI." Automation 6, no. 3 (2025): 29. https://doi.org/10.3390/automation6030029.

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Urban systems are transforming as artificial intelligence (AI) evolves from automation to Agentic Urban AI (AI systems with autonomous goal-setting and decision-making capabilities), which independently define and pursue urban objectives. This shift necessitates reassessing governance, planning, and ethics. Using a conceptual-methodological approach, this study integrates urban studies, AI ethics, and governance theory. Through a literature review and case studies of platforms like Alibaba’s City Brain and CityMind AI Agent, it identifies early agency indicators, such as strategic adaptation a

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Ridhima, Arora, Modi Maurya, and Bhalla Arjun. "Agentic AI: Revolutionizing eCommerce and Quick Commerce." Journal of Advances in Developmental Research 16, no. 1 (2025): 1–6. https://doi.org/10.5281/zenodo.15049815.

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Agentic AI represents a paradigm shift in artificial intelligence, characterized by autonomous decision-making capabilities and proactive behavior. This paper explores how Agentic AI can revolutionize eCommerce and quick commerce by enhancing operational efficiency, customer experience, and market responsiveness. We delve into key applications, potential benefits, challenges, and future directions, offering insights into its strategic deployment.

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Gaurav Samdani, Kabita Paul, and Flavia Saldanha. "Serverless architectures for agentic AI deployment." World Journal of Advanced Engineering Technology and Sciences 7, no. 2 (2022): 320–33. https://doi.org/10.30574/wjaets.2022.7.2.0144.

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This paper presents directions on improving scalabilities, costs, and flexibility in serverless architectures incorporating agentic AI deployment. Using event-driven and a pay-as-you-go model, Serverless computing is shown to be an optimal way to deploy agentic AI systems due to their need for flexibility. The research objectives include the assessment of the possibilities for serverless platforms, the assessment of the effectiveness of its case applications, and the development of a solid methodology for its application in real life. The methodology uses case studies, comparative analysis, an

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Sivakumar, Shanmugasundaram. "Agentic AI in Predictive AIOps: Enhancing IT Autonomy and Performance." International Journal of Scientific Research and Management (IJSRM) 12, no. 11 (2024): 1631–38. http://dx.doi.org/10.18535/ijsrm/v12i11.ec01.

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The integration of Agentic Artificial Intelligence (AI) within Predictive AIOps (Artificial Intelligence for IT Operations) is revolutionizing the management of IT systems, significantly enhancing IT autonomy and performance (Smith & Johnson, 2023). This article explores the potential of Agentic AI to empower AIOps platforms in proactively predicting, identifying, and resolving system issues. By leveraging predictive analytics and machine learning, AIOps not only enhances operational efficiency but also minimizes downtime and supports autonomous decision-making in complex IT environments (

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Ushaa, Eswaran, J. Suman, Jaishree, et al. "Transforming disaster response: The role of agentic AI in crisis management." i-manager's Journal on Structural Engineering 13, no. 2 (2024): 48. https://doi.org/10.26634/jste.13.2.21675.

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One revolutionary step in redefining disaster response procedures is the use of agentic AI in crisis management. Conventional methods of disaster management mostly depend on human judgement, which is frequently sluggish, prone to mistakes, and overpowered by the complexity of ever-changing emergency situations. A new paradigm for handling such difficulties is provided by agentic AI, which is distinguished by its capacity for autonomous decisionmaking, adaptive learning, and real-time data processing. This paper examines how agentic AI can be incorporated into disaster response systems, emphasi

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Gautam Ulhas Parab. "Agentic AI in Data Analytics: Transforming Autonomous Insights and Decision-Making." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 1752–59. https://doi.org/10.32628/cseit241061220.

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This article explores the transformative impact of Agentic AI systems on modern data analytics, examining how these autonomous agents are revolutionizing organizational decision-making processes. The article demonstrates how Agentic AI enables unprecedented automation and accuracy in data analysis across various sectors through detailed analysis of implementation strategies, architectural considerations, and real-world applications. The article encompasses key domains, including retail, financial services, and manufacturing, highlighting how these systems reduce manual intervention while impro

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Lee, Hsien-Hsin S. "Rise of the Agentic AI Workforce." IEEE Micro 45, no. 1 (2025): 4–5. https://doi.org/10.1109/mm.2025.3535912.

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ARSLAN, AYSE. "Exploring Agentic AI and Recursive Reasoning." International Journal of Applied Science and Research 08, no. 03 (2025): 51–61. https://doi.org/10.56293/ijasr.2025.6505.

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Larichev, Vlad, Jennifer Masek, Prashant Chouhan, and Daniel Spiess. "Generative AI and Agentic Architecture in Engineering and Manufacturing." Zeitschrift für wirtschaftlichen Fabrikbetrieb 120, s1 (2025): 17–24. https://doi.org/10.1515/zwf-2024-0166.

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Abstract The integration of Generative AI (GenAI) and Agentic Architecture offers potential for scalability, automation, and improved decision-making in engineering and manufacturing. These technologies contribute to efficiency and process optimization but face challenges such as data fragmentation and interoperability. This paper examines the role of Agentic Architecture in addressing these issues, presenting scalable AI solutions, practical use cases, and strategic considerations for sustainable AI-driven innovation in industrial applications.

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Ilango, Kessavane. "A Study of Reducing HR Redundancy Processes with Agentic AI." Journal of Advances in Developmental Research 16, no. 1 (2025): 1–10. https://doi.org/10.5281/zenodo.14993242.

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In modern Human Resources (HR) management, the necessity to streamline administrative processes and eliminate redundancy has become paramount. This study examines the potential of agentic artificial intelligence (AI) to revolutionize HR operations, with a particular focus on reducing redundant tasks. By employing agentic AI, HR departments can achieve higher efficiency, accuracy, and employee satisfaction. This research investigates various AI models and their applications in automating repetitive tasks such as resume screening, employee onboarding, and performance evaluations. The findings su

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Pavan Kumar Bollineni. "Revolutionizing Financial Management: The Role of Agentic AI in SAP Finance." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 473–82. https://doi.org/10.32996/jcsts.2025.7.2.49.

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The integration of Agentic AI into SAP Finance represents a transformative advancement in enterprise financial management, combining autonomous decision-making capabilities with sophisticated data analytics to revolutionize traditional financial processes. This comprehensive article explores how Agentic AI is reshaping SAP Finance through enhanced automation of routine financial tasks, deployment of advanced predictive analytics for forecasting and risk assessment, and the provision of real-time financial intelligence that enables dynamic decision-making. By examining the technical architectur

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Pomozova, Natalia B., and Nikolay V. Litvak. "Artificial Intelligence Ethics as a Realm of International Discursive Competition." Russia in Global Affairs 23, no. 2 (2025): 58–70. https://doi.org/10.31278/1810-6374-2025-23-2-58-70.

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It might seem that the global threat posed by the hybrid non-agentic/agentic nature of AI would encourage governments to jointly regulate it. Most states’ acts related to AI development include sections on ethical regulations, which reveal differing normative approaches that make discussions about AI ethics an important element of interstate discursive competition. Russia’s significant lag behind the U.S., China, and the EU does not preclude its possible emergence as a discursive competitor.

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Wali, Girish, and Praveen Sivathapandi. "Suspicious Transaction Detection In Bank Transactions Using Agentic AI." Cuestiones de Fisioterapia 54, no. 2 (2025): 4827–36. https://doi.org/10.48047/eed97w67.

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Banking fraud has become a serious issue, with financial institutions struggling to detect suspicioustransactions effectively. Traditional fraud detection methods often fail due to evolving fraudulent techniques.This paper explores the use of Agentic AI to identify suspicious bank transactions with greater accuracy andefficiency. Agentic AI, which operates with more autonomy and adaptability than traditional AI models, cananalyze transaction patterns, detect anomalies, and make intelligent decisions in real time. The studyimplements an AI-driven detection model using machine learning technique

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Vamsi Krishna Kumar Karanam. "From Automation to Autonomy: Exploring Agentic AI in IT Service Management." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 3009–19. https://doi.org/10.30574/wjaets.2025.15.2.0871.

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Agentic Artificial Intelligence represents a transformative paradigm in Information Technology Service Management (ITSM), fundamentally redefining operational capabilities through autonomous decision-making and action execution. This article explores the evolution from traditional automation toward agentic autonomy in ITSM environments, examining both theoretical foundations and practical applications. The transition from human-driven workflows to autonomous systems capable of contextual awareness, adaptive learning, and independent action execution marks a significant advancement beyond conve

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Satav, Ashay. "Enterprise API & Platform Strategy in the era of Agentic AI." Journal of Computer Science and Technology Studies 7, no. 1 (2025): 380–85. https://doi.org/10.32996/jcsts.2025.7.1.28.

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This research paper investigates the critical importance of robust API and platform strategies for enterprises adapting to the proliferation of agentic AI, wherein AI systems autonomously execute tasks with limited human intervention. It addresses the imperative of facilitating seamless communication among AI agents, enterprise data systems, and external applications. The research examines the architectural and performance considerations essential for organizations to maintain competitiveness in this rapidly growing technological landscape of agentic AI projected to expand from $5.1 billion in

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Twinkle Joshi. "Architecting Agentic AI for Modern Software Testing: Capabilities, Foundations, and a Proposed Scalable Multi-Agent System for Automated Test Generation." Journal of Information Systems Engineering and Management 10, no. 52s (2025): 625–38. https://doi.org/10.52783/jisem.v10i52s.10768.

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The progression of software testing has evolved from manual processes to automated systems. However, the emergence of Agentic AI-driven testing represents the next transformative leap. These intelligent agents autonomously generate, execute, and optimize tests, redefining the quality assurance (QA) landscape. Agentic AI—defined by its capacity to independently perceive, plan, execute, and learn—has emerged as a transformative force in software testing. This article examines the impact of Agentic AI on the software testing lifecycle, highlighting its core capabilities, such as dynamic test gene

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Ahmed, Nisher, Md Emran Hossain, Zakir Hossain, Md Farhad Kabir, and Iffat Sania Hossain. "Assessing the Potential and Ethical Implications of Agentic AI in Surveillance Technology." Formosa Journal of Multidisciplinary Research 4, no. 4 (2025): 1841–58. https://doi.org/10.55927/fjmr.v4i4.167.

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Agentic AI creates a system rather than a tool, an autonomous entity interacting with the world as humans do. By functionalizing agentic AI into surveillance technologies, we can increase surveillance systems' efficiency, accuracy, and scope, enabling them to monitor vast expanses of public space or issue, for example, a summary of dialogue from thousands of social media posts. However, it also interrogates the ethical dimensions of these systems, including the possible loss of privacy, accountability, and bias in decision making. AI surveillance technology is in use everywhere, but creating a

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Tomar, Manish, Vasudevan Ananthakrishnan, and Muthuraman Saminathan. "Agentic AI-Powered Data Quality Guardians for Regulated Industries." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 3, no. 1 (2024): 507–31. https://doi.org/10.60087/jaigs.v3i1.378.

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In regulated industries such as healthcare, finance, and pharmaceuticals, ensuring data quality is not merely a matter of efficiency but of compliance, trust, and safety. This paper introduces the concept of Agentic AI-Powered Data Quality Guardians—autonomous, intelligent agents designed to proactively monitor, assess, and enhance data quality across complex and evolving systems. Leveraging advancements in agentic artificial intelligence (AI), these digital guardians operate with minimal human oversight, employing reasoning, learning, and self-correction to maintain data integrity in real tim

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Viswanathan, Ganesh. "AI Agentic Scriptless Automation in Software Testing." International Journal of Computer Trends and Technology 72, no. 9 (2024): 120–25. http://dx.doi.org/10.14445/22312803/ijctt-v72i9p118.

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Karunanayake, Nalan. "Next-generation agentic AI for transforming healthcare." Informatics and Health 2, no. 2 (2025): 73–83. https://doi.org/10.1016/j.infoh.2025.03.001.

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Oesch, Sean, Jack Hutchins, Phillipe Austria, and Amul Chaulagain. "Agentic AI and the Cyber Arms Race." Computer 58, no. 5 (2025): 82–85. https://doi.org/10.1109/mc.2025.3544116.

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Kundu, Subhasis. "Collaborative Agentic AI for Global Resource Management: Optimizing Sustainability and Efficiency Across Industries." International Journal of Multidisciplinary Research and Growth Evaluation. 5, no. 2 (2024): 1023–27. https://doi.org/10.54660/.ijmrge.2024.5.2.1023-1027.

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This study explores the potential of collaborative agentic AI systems to improve global resource management across multiple industries. It introduces an innovative framework that leverages advanced AI technologies to optimize resource distribution, enhance sustainability, and increase operational efficiency. The research examines the application of machine learning algorithms, predictive analytics, and autonomous decision-making in sectors such as agriculture, energy, manufacturing, and logistics. Findings highlight significant improvements in resource utilization, waste reduction, and environ

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Lubis, Muharman, Hendra Halim, Fitrah Khairi, et al. "Workshop on the Emergence of Agentic AI in Financial Technology and Entrepreneurship Development." Jurnal Pengabdian Bakti Akademisi 2, no. 2 (2025): 81–93. https://doi.org/10.24815/jpba.v2i2.45908.

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The rapid development of Agentic Artificial Intelligence (AI) and blockchain technology has transformed the global financial and entrepreneurial landscape, posing both opportunities and challenges for young entrepreneurs. This community service activity aimed to enhance the digital literacy and innovation capacity of 100 student entrepreneurs from Universitas Syiah Kuala by introducing practical applications of Agentic AI in financial technology and business development. The activity was conducted in the form of an online workshop, utilizing a service-learning approach, and facilitated by an e

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Samdani, Gaurav, Ganesh viswanathan, and Abirami Dasu Jegadeesh. "HUMAN-AI COLLABORATION: BALANCING AGENTIC AI AND AUTONOMY IN HYBRID SYSTEMS." International Journal on Cloud Computing: Services and Architecture 15, no. 1 (2025): 01–15. https://doi.org/10.5121/ijccsa.2025.15101.

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In this article, the author explores the tension between the human factor and artificial intelligence as a symbiosis of two effective approaches to solving multifaceted, realistic tasks. Considering the premises of human-AI cooperation, it identifies how combined structures can improve these processes as decision making, scalability and flexibility in spheres including healthcare, auto transport industry as well as education. The discussion combines theories and case studies to explain how hybrid systems may retain transparent, fair, and ethical procedures while achieving operational performan

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Sakhare, Akash, Pruthviraj Chavan, Zaid Nandaniwala, Akanksha Patne, and Mrunalinee Desai. "A Step Forward to AGI: Integrating Agentic AI and Generative AI for Human-Like Intelligence." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (2025): 221–30. https://doi.org/10.22214/ijraset.2025.66831.

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Abstract: Artificial general intelligence (AGI) is the ultimate goal of artificial intelligence, which should be able to mimic human-like cognitive abilities in a variety of tasks. The developing junction of generative AI—creative, content-generating systems—and agentic AI—autonomous, goal-directed systems—is one of the possible routes to AGI. We discuss a comparative study of these two paradigms, focusing on their different approaches, underlying difficulties, and potential synergies. This research proposes a new angle in the light of AGI future, regarding how the autonomy and problem-solving

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Gaurav Samdani, Kabita Paul, and Flavia Saldanha. "Agentic AI in the Age of Hyper-Automation." World Journal of Advanced Engineering Technology and Sciences 8, no. 1 (2023): 416–27. https://doi.org/10.30574/wjaets.2023.8.1.0042.

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This article discusses the changes occurring in the background of hyper-automation concerning Agentic AI. The growth of self-governing and decision-making AI systems is addressed in the discussion of AI as an embodiment of agency, which empowers industries to operate with the least human interference or assistance. The analysis of Agentic AI in hyper-automated ecosystems corresponds to the following objectives: the role and application of AI, the main issues that appear when implementing the solution, and the ethical aspects. Case examples from logistics, manufacturing, and energy employ both

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Venus Garg. "Designing the Mind: How Agentic Frameworks Are Shaping the Future of AI Behavior." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 182–93. https://doi.org/10.32996/jcsts.2025.7.5.24.

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Agentic frameworks represent a paradigm shift in artificial intelligence, transitioning from reactive systems to autonomous entities capable of perceiving environments, reasoning about complex situations, planning actions, and executing decisions aligned with specific goals. These architectures integrate multiple specialized components—perception modules, world modeling capabilities, goal management systems, planning mechanisms, and action execution frameworks—working in concert to enable proactive behavior in dynamic environments. While offering transformative potential across domains includi

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Gaurav Samdani, Yawal Dixit, and Ganesh Vishwanathan. "Leveraging LangGraph and AutoGen for Agentic AI Frameworks." World Journal of Advanced Engineering Technology and Sciences 8, no. 2 (2023): 402–11. https://doi.org/10.30574/wjaets.2023.8.2.0068.

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This research examines how LangGraph and AutoGen improve Agentic AI models by enabling improved autonomous functioning in dynamic environments. Researchers examine LangGraph's language-based system and AutoGen's generative model as independently working tools for agent autonomous performance in intricate situations. Our study uses quality-benchmarking data and test simulations to examine modeling effects on AI agents' behavior and decision-making. The study shows that LangGraph boosts language understanding effectiveness while AutoGen improves the system's ability to adjust decisions swiftly i

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Gajjar, Vyoma. "AGENTIC GOVERNANCE: A FRAMEWORK FOR AUTONOMOUS DECISION-MAKING SYSTEMS." International Journal of Engineering Applied Sciences and Technology 09, no. 04 (2024): 73–75. http://dx.doi.org/10.33564/ijeast.2024.v09i04.008.

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The proliferation of fake news has become a significant concern in recent years, with far-reaching consequences for individuals, communities, and society. Artificial intelligence (AI) has the potential to play a crucial role in detecting and mitigating the spread of fake news. However, the use of AI in fake news detection also raises important governance considerations. In this paper, we propose a novel approach to AI governance in fake news detection, including a framework for responsible AI governance, a new algorithm for fake news detection, and a comprehensive evaluation of the proposed ap

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Keshav Agrawal. "The Future of E Commerce with Agentic AI: Personalized, Task-Oriented AI for a smarter shopping experience." Open Access Research Journal of Engineering and Technology 8, no. 2 (2025): 103–13. https://doi.org/10.53022/oarjet.2025.8.2.0052.

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Agentic AI represents a paradigm shift in e-commerce, moving from basic recommendation algorithms to intelligent, task-oriented assistants with deep understanding of specific domains. Unlike traditional systems that rely on statistical correlations, agentic AI employs cognitive architectures capable of understanding contextual factors and long-term customer goals. The emergence of specialized agents focused on particular life domains - parenting, meal planning, home maintenance - enables personalized shopping experiences that adapt to individual lifestyles. These agents utilize sophisticated l

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Sreeram Reddy Thoom. "Understanding Agentic Frameworks in AI Development: A Technical Analysis." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 518–27. https://doi.org/10.32628/cseit25111249.

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This technical article examines the evolution and implementation of agentic frameworks in artificial intelligence development, focusing on their transformative impact across multiple industries. The article explores the fundamental architectural components, implementation methodologies, and practical applications of these frameworks in manufacturing, financial services, and healthcare sectors. By investigating the core components, including perception systems and decision architectures, alongside the Belief-Desire-Intention model and advanced learning mechanisms, this article provides comprehe

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Zou, James, and Eric J. Topol. "The rise of agentic AI teammates in medicine." Lancet 405, no. 10477 (2025): 457. https://doi.org/10.1016/s0140-6736(25)00202-8.

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Naseer R, Srujan K M, Deepthi A S, Divyashree C H, and Goutham M. "Framework for Cloud Data Security Using Agentic AI." International Journal of Science and Research Archive 15, no. 1 (2025): 1730–35. https://doi.org/10.30574/ijsra.2025.15.1.1255.

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Cloud platforms are ever more vulnerable to advanced cyber threats, which demand intelligent and self-reliant security systems. We introduce CloudShield, a prototype Agentic AI system for mimicking live cloud data protection in Azure platforms. The system mimics round-the-clock log collection in Azure-type protocols, employs the Isolation Forest algorithm to identify outliers, and responds automatically to attacks such as brute-force attacks and malware. Logs are locally stored in a SQLite database, encrypted for secure storage, and can be deployed entirely self-contained without dependencies.

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Gordon, Ronald D. "Domination by Agentic AI: Lament from the Future." Global Journal of Arts, Humanities and Social Sciences 13, no. 6 (2025): 1–20. https://doi.org/10.37745/gjahss.2013/vol13n6120.

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We humans have long been separated from ourselves (the inner alignment problem), and from meaningful high-quality relations with others and our own natural surroundings (the outer alignment problem). A subset of superintelligence technocrats and their wealthy investors have been driving even deeper wedges into these pre-existing divides. Their ambitiousness has gone unrestrained, as potentially life-threatening decisions made by the few then affect all the rest of humanity. Man-in-power finds it difficult to control himself, so it’s no wonder he is not always in control of his risky technologi

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