Relevant bibliographies by topics / Agentic AI

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Agentic AI'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Agentic AI"

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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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Dissertations / Theses on the topic "Agentic AI"

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Pettersson, Oscar. "Machine Learning Agents : En undersökning om Curiosity som belöningssystem för maskininlärda agenter." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17142.

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Denna rapport har använt sig av Unity-verktyget ML-Agents till att bygga upp en spelmiljö där agenter tränats med hjälp av neurala nätverk och reinforcement learning. Miljön har utmanat agenterna med labyrintliknande banor där vissa även har enkla pusselmekaniker. Agenterna har sedan använts för att göra en undersökning om hur belöningssystemet Curiosity påverkar agentens förmåga att efter träning klara av för dem nya banor. Denna undersökning gjordes genom att träna 5 olika agenter under samma förhållanden där enbart styrkan på Curiosity-incitamentet skiljde dem åt. De olika Curiosity-styrkor
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Gunnarsson, Henrik. "Actionplanering och Samarbete (APAC) mellan multipla AI-agenter." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-108864.

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This thesis covers an implementation of an artificial intelligence (AI) system for cooperation between multiple AI-agents. It was done as a part of a master thesis in Media Technology and a master thesis in Computer Engineering at Linköping University, campus Norrköping. The aim of the project was to explore modern techniques in AI and also develop a platform where this AI is implemented for the upcoming educational purposes. The idea is that students can use the system as a base to extend, learn and implement their own AI algorithms. Based on a literature study in AI systems the decision was
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Magnusson, Martin. "Beteendevarierande agenter med stokastiska tillståndsmaskiner." Thesis, University of Skövde, School of Humanities and Informatics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-3094.

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<p>Tillståndsmaskiner var en av de första teknikerna som användes för att skapa AI (Artificiell Intelligens) i dataspel och är fortfarande än av de vanligaste teknikerna. Men under senare år har flera nya tekniker, såsom ANN (Artificiella Neurala Nätverk), börjat användas för att skapa mer avancerad AI i dataspel. Många anser att tillståndsmaskiner inte kan skapa tillräckligt smarta agenter och att agenterna ofta blir förutsägbara. Generellt är tillståndsmaskiner inte lämpade att använda när man vill ha många agenter med olika beteende eftersom det ofta kräver speciell kod för varje unik agent
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Marino, Caterina. "Finte pelli per interni auto in PVC: adeguamento ai requisiti REACH." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/6544/.

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Le finte pelli sono sistemi costituiti da vari strati, il cui componente principale è il PVC. La produzione delle finte pelli richiede l’utilizzo di un elevato numero di additivi; per ognuno di essi occorre valutare la quantità da utilizzare, l’efficienza in rapporto al suo costo e, non di minore importanza, il suo grado di tossicità. Gli additivi attualmente utilizzati nella produzione delle finte pelli, che presentano problemi legati alla sicurezza sono: gli ftalati (plastificanti), l’azodicarbonammide (agente espandente) e il triossido di antimonio (agente antifiamma). Il lavoro di tesi è s
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Gehlin, Rikard. "Action Planning and Cooperation (APAC) between multiple AI-agents." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-108865.

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An architecture for actionbased planning and cooperation between multiple AI-agents based on the GOAP-architecture was developed together with a system to be used in advanced AI-courses at Linköping unversity. The architecture was implemented in this system to show the possibilities of our work.
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Franzén, Daniel. "A Modular API for Intelligent Virtual Agents." Thesis, Linköping University, Department of Science and Technology, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8508.

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<p>This report proposes a modular Application Programmer's Interface (API) for handling the mental layer of intelligent virtual agents for a wide range of application types, with the aim of reducing the work required to program a completely new AI engine, and describes its implementation. One of the key elements and major difficulties in its design is the need to make it general enough to suit most types of applications, while preserving its usefulness and keeping it both efficient and reliable. A sample application interfacing with the API is created to demonstrate its capabilities, various A
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Jusko, Ján. "Chatbot pro Smart Cities." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403204.

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The aim of this work is to simplify access to information for citizens of the city of Brno and at the same time to innovate the way of communication between the citizen and his city. The problem is solved by creating a conversational agent - chatbot Kroko. Using artificial intelligence and a Czech language analyzer, the agent is able to understand and respond to a certain set of textual, natural language queries. The agent is available on the Messenger platform and has a knowledge base that includes data provided by the city council. After conducting an extensive user testing on a total of 76
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Emanuelsson, Max. "SVÄRM-AI FÖR TAKTISKA BESLUT HOS GRUPPER AV FIENDER." Thesis, Högskolan i Skövde, Institutionen för kommunikation och information, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-8451.

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I detta examensarbete tittas det närmare på taktiska beslut för större grupper av autonoma agenter. Arbetet ska försöka besvara följande frågeställning: Hur effektivt kan svärm-AI användas för att utföra taktiska beslut när det appliceras till ett spel med grupper av fiender? För att kunna besvara frågeställningen skapades en applikation där fyra kombinationer av styrbeteenden och beräkningsmodeller inom tekniken ”boids” användes. Inom två av kombinationerna användes traditionella styrbeteenden inom boids, och inom de andra introducerades ett styrbeteende för att flankera spelaren för att ge b
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Pedrini, Gianmaria. "Rogueinabox: a Rogue environment for AI learning. Framework development and Agents design." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13812/.

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In this thesis we introduce Rogueinabox: a higly modular learning environment built around the videogame Rogue, the father of the roguelike genre. It offers easy ways to interact with the game and a whole framework to build, customize and run learning agents. We discuss the interest and challengies of this game for machine learning and deep learning, and discuss our initial experiments of training. We show the userfulness and convenience of Rogueinabox employing it in combination with QLearning tecniques to build an agent that explores the dungeon.
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Slater, Stuart. "E-AI : an emotion architecture for agents in games & virtual worlds." Thesis, University of Wolverhampton, 2010. http://hdl.handle.net/2436/105038.

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Characters in games and virtual worlds continue to gain improvements in both their visual appearance and more human-like behaviours with each successive generation of hardware. One area that seemingly would need to be addressed if this evolution in human-like characters is to continue is in the area of characters with emotions. To begin addressing this, the thesis focuses on answering the question “Can an emotional architecture be developed for characters in games and virtual worlds, that is built upon a foundation of formal psychology? Therefore a primary goal of the research was to both revi
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Books on the topic "Agentic AI"

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Huang, Ken, ed. Agentic AI. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-90026-6.

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Zhang, Xiucheng. Xian dai shi yong kang ai Zhong yao. Beijing ke xue ji shu chu ban she, 1999.

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Jianhua, Cheng, Li Yibin, Yuan Chunlin, and Liu Xinhong, eds. Kang ai zhi wu yao ji qi yan fang. Jiangxi ke xue ji shu chu ban she, 1998.

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1962-, Weiss Gerhard, ed. Multiagent systems: A modern approach to distributed artificial intelligence. MIT Press, 1999.

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Mukesh, Patel, Honavar Vasant, and Balakrishnan Karthik 1970-, eds. Advances in the evolutionary synthesis of intelligent agents. MIT Press, 2001.

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1960-, Cassell Justine, ed. Embodied conversational agents. MIT Press, 2000.

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Lee, R. S. T. Fuzzy-neuro approach to agent applications: From the AI perspective to modern ontology. Springer, 2006.

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CIA 2004 (2004 Erfurt, Germany). Cooperative information agents VIII: 8th international workshop, CIA 2004, Erfurt, Germany, September 27-29, 2004 ; proceedings. Springer, 2004.

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Maurice, Pagnucco, Wobcke Wayne 1963-, Zhang Chengqi, and Australian Joint Conference on Artificial Intelligence, (10th : 1997 : Perth, W.A.), eds. Agents and multi-agent systems: Formalisms, methodologies and applications : based on the AI'97 Workshop on Commonsense Reasoning, Intelligent Agents, and Distributed Artificial Intelligence, Perth, Australia, December 1, 1997. Springer, 1998.

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Adelinde, Uhrmacher, and Weyns Danny, eds. Multi-agent systems: Simulation and applications. Taylor & Francis, 2009.

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Book chapters on the topic "Agentic AI"

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Wissuchek, Christopher, and Patrick Zschech. "Challenges in Managing the Relationship Between Agentic AI Systems and Humans in Organizations." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-94193-1_1.

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Dinu, Marius-Constantin, Markus Hofmarcher, Vihang P. Patil, et al. "XAI and Strategy Extraction via Reward Redistribution." In xxAI - Beyond Explainable AI. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_10.

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AbstractIn reinforcement learning, an agent interacts with an environment from which it receives rewards, that are then used to learn a task. However, it is often unclear what strategies or concepts the agent has learned to solve the task. Thus, interpretability of the agent’s behavior is an important aspect in practical applications, next to the agent’s performance at the task itself. However, with the increasing complexity of both tasks and agents, interpreting the agent’s behavior becomes much more difficult. Therefore, developing new interpretable RL agents is of high importance. To this e
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Babin, Gilbert, Zakaria Maamar, and Brahim Chaib-draa. "Metadatabase meets distributed AI." In Cooperative Information Agents. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-62591-7_29.

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Magboo, Ma Sheila A., and Vincent Peter C. Magboo. "Explainable AI for Autism Classification in Children." In Agents and Multi-Agent Systems: Technologies and Applications 2022. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3359-2_17.

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Taulli, Tom, and Gaurav Deshmukh. "Developing Agents." In Building Generative AI Agents. Apress, 2025. https://doi.org/10.1007/979-8-8688-1134-0_5.

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Usatorre, Luis, Paula Morella, Iñigo Sedano, Sergio Clavijo, and Asier Aguayo. "AI Based Solutions for Manufacturing Mass Customization." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86489-6_19.

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Abstract This paper analyses how to solve the challenges in the implementation of Mass Customization in manufacturing using Artificial Intelligence agents/services/tools. Considering that humans alone cannot cope with mass customization due to the huge amount of information, it is required AI based solutions that help humans to take decisions. We consider that those AI based solutions must communicate with other AI based solutions in order to obtain a holistic improvement (this is the Multi Agent System concept). More in detail, this paper addresses how to solve the challenges identified when
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Williams, Benjamin, James Jackson, Jason Hall, and Christopher J. Headleand. "Modular Games AI Benchmark." In Artificial Life and Intelligent Agents. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90418-4_12.

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Wallace, Scott A., and John E. Laird. "Toward a Methodology for AI Architecture Evaluation: Comparing Soar and CLIPS." In Intelligent Agents VI. Agent Theories, Architectures, and Languages. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/10719619_9.

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Bahrami, Mohammad Reza, Bahareh Bahrami, Farima Behboodi, and Samae Pourrafie. "Teaching the Future: The Vision of AI/ChatGPT in Education." In Agents and Multi-agent Systems: Technologies and Applications 2023. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3068-5_37.

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Kladko, Sergei. "Cultural-Ethical Evaluation in the Launch of AI Education Technologies." In Agents and Multi-agent Systems: Technologies and Applications 2023. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3068-5_24.

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Conference papers on the topic "Agentic AI"

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Tong, Richard Jiarui, Haoyang Li, Sridhar Raghavan, et al. "IEEE AI Standards for Agentic Systems." In 2025 IEEE Conference on Artificial Intelligence (CAI). IEEE, 2025. https://doi.org/10.1109/cai64502.2025.00269.

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Jovanović, Sonja, Lazar Dražeta, Aleksandar Petrović, Nebojša Bačanin Džakula, and Miodrag Živković. "Ethical Concerns and Mass Agentic AI Adoption." In Sinteza 2025. Singidunum University, 2025. https://doi.org/10.15308/sinteza-2025-369-375.

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Portugal, Ivens Da Silva, Paulo Alencar, and Donald Cowan. "An Agentic AI-based Multi-Agent Framework for Recommender Systems." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825765.

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ElMessiry, Adel, and Magdi ElMessiry. "AGENTIC APPROACH TO AI-ASSISTED EDUCATION USING AIVP." In 17th International Conference on Education and New Learning Technologies. IATED, 2025. https://doi.org/10.21125/edulearn.2025.1941.

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P, Asha Rani K., and Gowrishankar S. "Agentic AI for Pathogen-Based Plant Disease Detection." In 2025 6th International Conference on Artificial Intelligence, Robotics and Control (AIRC). IEEE, 2025. https://doi.org/10.1109/airc64931.2025.11077524.

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K, Manjunath Kamath, Samata Mehta.S, Akshaya H. L, Shilpashree N, Girish Jadhav, and Abhijit Mitra. "Neuromorphic-Driven Agentic AI for Autonomous Decision-Making Systems." In 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2024. https://doi.org/10.1109/icmnwc63764.2024.10872131.

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Peci, Fatos, Enver Hamiti, and Ishtiaq Khan. "Agentic AI with Chatops for Large Scale Network Operations." In 2025 IEEE Conference on Artificial Intelligence (CAI). IEEE, 2025. https://doi.org/10.1109/cai64502.2025.00242.

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Paulose, Renjith, Vinod Neelanath, and Milgy George. "Domain Agnostic Agentic AI: Enabling Autonomous Automation with SmartGenie CoPilot." In 2025 Emerging Technologies for Intelligent Systems (ETIS). IEEE, 2025. https://doi.org/10.1109/etis64005.2025.10961403.

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Viradia, Vijaykumar, Vineet KR Mishra, and Siddharth Parakh. "Agentic AI for Medicare Annual Wellness Visits: Improving Preventive Care." In 2025 6th International Conference on Bio-engineering for Smart Technologies (BioSMART). IEEE, 2025. https://doi.org/10.1109/biosmart66413.2025.11046123.

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., Chirag, and Sarab Nihal Singh Nagra. "A Multi-Agentic AI Framework for Autonomous and Collaborative Data Science Workflows." In 2025 First Global Conference on AI Research and Emerging Developments. Ganitara Research Foundation, 2025. https://doi.org/10.63169/gcared2025.p31.

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Reports on the topic "Agentic AI"

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Fariborzi, Hadi, and Piers Steel. Beyond ChatGPT: Agentic AI as 24/7 Research Assistants. Instats Inc., 2025. https://doi.org/10.61700/gzihjl4e74w841498.

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This advanced one-day workshop, presented by professors Piers Steel and Hadi Fariborzi, delves into the transformative power of agentic AI in academic research, equipping participants to automate data collection, analysis, and real-time dashboard updates. Through hands-on sessions, attendees will explore both qualitative and quantitative AI-driven methods, freeing them to focus on higher-level insights.
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Rinuado, Christina, William Leonard, Christopher Morey, Theresa Coumbe, Jaylen Hopson, and Robert Hilborn. Artificial intelligence (AI)–enabled wargaming agent training. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48419.

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Fiscal Year 2021 (FY21) work from the Engineer Research and Development Center Institute for Systems Engineering Research lever-aged deep reinforcement learning to develop intelligent systems (red team agents) capable of exhibiting credible behavior within a military course of action wargaming maritime framework infrastructure. Building from the FY21 research, this research effort sought to explore options to improve upon the wargaming framework infrastructure and to investigate opportunities to improve artificial intelligence (AI) agent behavior. Wargaming framework infrastructure enhancement
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Rinaudo, Christina, William Leonard, Jaylen Hopson, Christopher Morey, Robert Hilborn, and Theresa Coumbe. Enabling understanding of artificial intelligence (AI) agent wargaming decisions through visualizations. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48418.

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The process to develop options for military planning course of action (COA) development and analysis relies on human subject matter expertise. Analyzing COAs requires examining several factors and understanding complex interactions and dependencies associated with actions, reactions, proposed counteractions, and multiple reasonable outcomes. In Fiscal Year 2021, the Institute for Systems Engineering Research team completed efforts resulting in a wargaming maritime framework capable of training an artificial intelligence (AI) agent with deep reinforcement learning (DRL) techniques within a mari
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Toner, Helen, John Bansemer, Kyle Crichton, et al. Through the Chat Window and Into the Real World: Preparing for AI Agents. Center for Security and Emerging Technology, 2024. http://dx.doi.org/10.51593/20240034.

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Computer scientists have long sought to build systems that can actively and autonomously carry out complicated goals in the real world—commonly referred to as artificial intelligence "agents." Recently, significant progress in large language models has fueled new optimism about the prospect of building sophisticated AI agents. This CSET-led workshop report synthesizes findings from a May 2024 workshop on this topic, including what constitutes an AI agent, how the technology is improving, what risks agents exacerbate, and intervention points that could help.
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Shukla, Indu, Rajeev Agrawal, Kelly Ervin, and Jonathan Boone. AI on digital twin of facility captured by reality scans. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47850.

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The power of artificial intelligence (AI) coupled with optimization algorithms can be linked to data-rich digital twin models to perform predictive analysis to make better informed decisions about installation operations and quality of life for the warfighters. In the current research, we developed AI connected lifecycle building information models through the creation of a data informed smart digital twin of one of US Army Corps of Engineers (USACE) buildings as our test case. Digital twin (DT) technology involves creating a virtual representation of a physical entity. Digital twin is created
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Weidmann, Ben, Yixian Xu, and David Deming. Measuring Human Leadership Skills with AI Agents. National Bureau of Economic Research, 2025. https://doi.org/10.3386/w33662.

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Lohn, Andrew, Anna Knack, Ant Burke, and Krystal Jackson. Autonomous Cyber Defense. Center for Security and Emerging Technology, 2023. http://dx.doi.org/10.51593/2022ca007.

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The current AI-for-cybersecurity paradigm focuses on detection using automated tools, but it has largely neglected holistic autonomous cyber defense systems — ones that can act without human tasking. That is poised to change as tools are proliferating for training reinforcement learning-based AI agents to provide broader autonomous cybersecurity capabilities. The resulting agents are still rudimentary and publications are few, but the current barriers are surmountable and effective agents would be a substantial boon to society.
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Bozzo Hauri, Sebastián. The New Frontier of Civil Liability: Artificial Intelligence, Autonomy, and Consumer Protection. Carver University; Universidad Autónoma de Chile, 2025. https://doi.org/10.32457/bozzo2202599.

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Technological evolution has entered a phase that challenges the very foundations of private law. The emergence of systems based on artificial intelligence (AI)—particularly in their most recent form, so-called AI agents—compels a reassessment of the traditional framework of civil liability, especially in the field of consumer law. The trajectory of AI has followed a path marked by three distinct waves. The first wave was predictive AI, trained on historical data to anticipate future behavior, as seen in recommendation engines and segmentation models. The second wave introduced generative AI—su
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Brown, Jesse Daniel, Adrian Raudaschl, Prashant Kumar, Donald Johnson, and Tsukanov Vladislav Aleksandrovich. Anchoring Global Security: Autonomous Shipping with Mind Reading AI, GPT-core and MAMBA- core Agents, RAG-Fusion, AI Communities, Hive- AI, and the Human Psyche. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.nl5249y9.1.

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Brown, Jesse Daniel, Adrian Raudaschl, Prashant Kumar, Donald Johnson, and Tsukanov Vladislav Aleksandrovich. Anchoring Global Security: Autonomous Shipping with Mind Reading AI, GPT-core and MAMBA- core Agents, RAG-Fusion, AI Communities, Hive- AI, and the Human Psyche. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.nl5249y9.

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