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Journal articles on the topic 'Autonomous AI Agents'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Kumar, Apurva. "Building Autonomous AI Agents based AI Infrastructure." International Journal of Computer Trends and Technology 72, no. 11 (2024): 116–25. https://doi.org/10.14445/22312803/ijctt-v72i11p112.

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FRANKLIN, STAN. "AUTONOMOUS AGENTS AS EMBODIED AI." Cybernetics and Systems 28, no. 6 (1997): 499–520. http://dx.doi.org/10.1080/019697297126029.

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Sai Vyshnavi, Koyya Doondy. "Integration of Blockchain, Internet of Things and AI." International Journal of Research in Science and Technology 12, no. 04 (2022): 31–36. http://dx.doi.org/10.37648/ijrst.v12i04.006.

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The next phase of digital transformation will be propelled by technologies like blockchain, the Internet of Things (IoT), and artificial intelligence (AI). In this paper, we suggest that the convergence of these technologies will make possible novel forms of enterprise. Future autonomous agents will function as autonomous profit centers that have a digital twin leveraging IoT, send and receive money leveraging blockchain technology, and autonomously make decisions as independent economic agents utilizing artificial intelligence and data analytics. Further, we suggest that this convergence will

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Ziemke, Tom. "Adaptive Behavior in Autonomous Agents." Presence: Teleoperators and Virtual Environments 7, no. 6 (1998): 564–87. http://dx.doi.org/10.1162/105474698565947.

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This paper provides an overview of the bottom-up approach to artificial intelligence (AI), commonly referred to as behavior-oriented AI. The behavior-oriented approach, with its focus on the interaction between autonomous agents and their environments, is introduced by contrasting it with the traditional approach of knowledge-based AI. Different notions of autonomy are discussed, and key problems of generating adaptive and complex behavior are identified. A number of techniques for the generation of behavior are introduced and evaluated regarding their potential for realizing different aspects

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Maes, Pattie. "Modeling Adaptive Autonomous Agents." Artificial Life 1, no. 1_2 (1993): 135–62. http://dx.doi.org/10.1162/artl.1993.1.1_2.135.

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One category of research in Artificial Life is concerned with modeling and building so-called adaptive autonomous agents, which are systems that inhabit a dynamic, unpredictable environment in which they try to satisfy a set of time-dependent goals or motivations. Agents are said to be adaptive if they improve their competence at dealing with these goals based on experience. Autonomous agents constitute a new approach to the study of Artificial Intelligence (AI), which is highly inspired by biology, in particular ethology, the study of animal behavior. Research in autonomous agents has brought

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SUMANTH REDDY, SHIVA. "AI Agents: Agent GPT." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50557.

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Abstract - Agent GPT is an advanced autonomous AI system designed to simulate human-like reasoning and task execution through the deployment of AI agents. Unlike traditional large language models that respond passively to user prompts, Agent GPT can plan, iterate, and execute multi-step goals with minimal human intervention. Each agent operates based on a defined objective, breaking it down into smaller tasks, leveraging APIs, tools, or internet access to gather information, and adapting dynamically to changing conditions. The system is often built on top of language models such as GPT-4, and

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Shinde, Dr Pravin, Himali Paradkar, Poojan Vig, Sanchay Thalnerkar, and Vinay Jain. "HELIX: Autonomous AI Agent." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 4647–54. http://dx.doi.org/10.22214/ijraset.2024.61080.

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Abstract: Artificial Intelligence has transformed the way we interact with technology, introducing us to agents that can think and make choices like humans. At the heart of this evolution is our project, 'HELIX'. Through 'HELIX', we've developed AI agents specialized in a variety of tasks: from digging deep into the web for research, streamlining email communication through automation, efficiently sending out emails in bulk, to strategically identifying and generating potential business leads. By weaving together cutting-edge machine learning algorithms and advanced language models, our system

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Nguyen Thanh, Binh, Ha Xuan Son, and Diem Thi Hong Vo. "Blockchain: The Economic and Financial Institution for Autonomous AI?" Journal of Risk and Financial Management 17, no. 2 (2024): 54. http://dx.doi.org/10.3390/jrfm17020054.

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This paper examines how the combination of artificial intelligence (AI) and blockchain technology can enable autonomous AI agents to engage and execute economic and financial transactions. We critically examine the constraints on AI agents in achieving predefined objectives independently, especially due to their limited access to economic and financial institutions. We argue that AI’s access to these institutions is vital in enhancing its capabilities to augment human productivity. Drawing on the theory of institutional economics, we propose that blockchain provides a solution for creating dig

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Echenim, Jennifer Ifeoma. "Integration of Artificial Intelligence and Blockchain for Intelligent Autonomous Systems." International Journal of Future Engineering Innovations 2, no. 3 (2025): 31–37. https://doi.org/10.54660/ijfei.2025.2.3.31-37.

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The integration of Artificial Intelligence (AI) and Blockchain offers a transformative approach to the development of Intelligent Autonomous Systems (IAS). Autonomous systems, which include self-driving cars, drones, and robots, require advanced decision-making, real-time data processing, and secure communication. AI provides the intelligence necessary for these systems to operate autonomously, while Blockchain introduces decentralized control, transparency, and enhanced security. This paper explores the potential of combining AI and Blockchain technologies to create more secure, transparent,

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Kusuma Kumar Parimi,, A. Santhosh. "Adaptation and Learning in AI Agents." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45372.

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Customer support process has seen a transformational shift in recent years. With the rapid proliferation of Artificial Integrating (AI), the human driven customer support has leapfrogged into automated customer services & support. The field of Artificial Intelligence (AI) has seen phenomenal rise in the development of agents' capability right from autonomous decision-making and problem-solving abilities to adapt to changing environments. Their proficiency in learning from experience and adaptation greatly influences their effectiveness. They both drive improvement over time and insure resp

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Jovanović, Mlađan, and Mark Campbell. "Self-Directing AI: The Road to Fully Autonomous AI Agents." Computer 58, no. 2 (2025): 71–77. https://doi.org/10.1109/mc.2024.3507513.

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Durlik, Irmina, Tymoteusz Miller, Ewelina Kostecka, Polina Kozlovska, and Wojciech Ślączka. "Enhancing Safety in Autonomous Maritime Transportation Systems with Real-Time AI Agents." Applied Sciences 15, no. 9 (2025): 4986. https://doi.org/10.3390/app15094986.

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The maritime transportation sector is undergoing a profound shift with the emergence of autonomous vessels powered by real-time artificial intelligence (AI) agents. This article investigates the pivotal role of these agents in enhancing the safety, efficiency, and sustainability of autonomous maritime systems. Following a structured literature review, we examine the architecture of real-time AI agents, including sensor integration, communication systems, and computational infrastructure. We distinguish maritime AI agents from conventional systems by emphasizing their specialized functions, rea

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Sengupta, Abhijeet. "Securing the Autonomous Future A Comprehensive Analysis of Security Challenges and Mitigation Strategies for AI Agents." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–2. https://doi.org/10.55041/ijsrem40091.

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The proliferation of Artificial Intelligence (AI) agents, characterized by their autonomy and capacity for independent decision-making, presents both unprecedented opportunities and novel security challenges. This research paper provides a comprehensive analysis of the security landscape surrounding AI agents, examining the unique vulnerabilities stemming from their inherent characteristics and the emerging threat vectors targeting these autonomous systems. We delve into a categorized framework of potential attacks, ranging from data poisoning and adversarial manipulation to physical tampering

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Nowak, Ewa. "Can human and artificial agents share an autonomy, categorical imperative-based ethics and “moral” selfhood?" Filozofia Publiczna i Edukacja Demokratyczna 6, no. 2 (2018): 169–208. http://dx.doi.org/10.14746/fped.2017.6.2.20.

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AI designers endeavour to improve ‘autonomy’ in artificial intelligent devices, as recent developments show. This chapter firstly argues against attributing metaphysical attitudes to AI and, simultaneously, in favor of improving autonomous AI which has been enabled to respect autonomy in human agents. This seems to be the only responsible way of making further advances in the field of autonomous social AI. Let us examine what is meant by claims such as designing our artificial alter egos and sharing moral selves with artificial humanoid devices as well as providing autonomous AI with an ethica

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Rajubhai Golani, Rajeshkumar. "The Rise of AI Agents: Transforming Enterprise Automation." International Journal of Advances in Engineering and Management 7, no. 4 (2025): 132–39. https://doi.org/10.35629/5252-0704132139.

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Artificial intelligence is undergoing a paradigm shift, evolving from rule-based systems into autonomous agents capable of end-to-end task execution and complex decision-making. This transformation represents what industry leaders call the "third wave" of enterprise automation, where AI systems become active participants in business processes. Modern AI agents combine large language models with multi-agent frameworks that enable specialized functions, self-optimization through feedback loops, and autonomous learning. These agents excel in real-time decision-making across multiple data sources,

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Manish Tripathi. "Autonomous Generative AI Agents in the Workforce: Transforming Industry Operations." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 05 (2025): 1643–48. https://doi.org/10.47392/irjaem.2025.0265.

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The advent of autonomous generative AI agents is poised to revolutionize workforce dynamics across various industries. These AI agents, capable of independently generating solutions, designing systems, and performing complex tasks, promise to optimize operations, reduce human error, and foster unprecedented levels of innovation. This paper examines the potential of autonomous generative AI agents in transforming industry operations, including their implications on labor markets, productivity, and the future of work. By exploring advancements in machine learning, robotics, and natural language

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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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Mehdi, Roopaei, Durian Hunter, and Godiska Joey. "Explainable AI in Internet of Control System Distributed at Edge-Cloud Architecture." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 3 (2021): 136–24. https://doi.org/10.35940/ijeat.C2246.0210321.

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Many current control systems are restricted to highly controlled environments. In complicated dynamic and unstructured environments such as autonomous vehicles, control systems must be able to deal with more and more complex state situations. In complex systems with large number of states, it is often too slow to use optimal planners and developing heuristic tactics for high level goals can be challenging. AI control is an attractive alternative to traditional control architectures due to their capability to approximate optimal solutions in high dimensional state spaces without requiring a hum

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Tulli, Silvia. "Explainability in Autonomous Pedagogical Agents." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13738–39. http://dx.doi.org/10.1609/aaai.v34i10.7141.

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The research presented herein addresses the topic of explainability in autonomous pedagogical agents. We will be investigating possible ways to explain the decision-making process of such pedagogical agents (which can be embodied as robots) with a focus on the effect of these explanations in concrete learning scenarios for children. The hypothesis is that the agents' explanations about their decision making will support mutual modeling and a better understanding of the learning tasks and how learners perceive them. The objective is to develop a computational model that will allow agents to exp

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Patel, Anjali, Hava Patel, and Charmi Patel. "A Survey on the Evolution of Autonomous Agents: Trends, Challenges, and Future Directions." SPU- Journal of Science, Technology and Management Research II, no. 01 (2025): 58–68. https://doi.org/10.63766/spujstmr.24.000033.

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Over the past few decades, autonomous agents have undergone tremendous evolution, moving from rule based systems to highly adaptive, learning-driven architectures. These autonomously perceivable, reasoning, and acting agents have found use in robotics, healthcare, finance, and other fields. This survey provides a comprehensive overview of the evolution of autonomous agents, highlighting key technological advancements, emerging trends, and persistent challenges. We explore the role of deep reinforcement learning, multi-agent systems, neuro symbolic AI, and edge computing in enhancing agent auto

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Roopaei*, Mehdi, Hunter Durian, and Joey Godiska. "Explainable AI in Internet of Control System Distributed at Edge-Cloud Architecture." International Journal of Engineering and Advanced Technology 10, no. 3 (2021): 136–42. http://dx.doi.org/10.35940/ijeat.c2246.0210321.

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Many current control systems are restricted to highly controlled environments. In complicated dynamic and unstructured environments such as autonomous vehicles, control systems must be able to deal with more and more complex state situations. In complex systems with large number of states, it is often too slow to use optimal planners and developing heuristic tactics for high level goals can be challenging. AI control is an attractive alternative to traditional control architectures due to their capability to approximate optimal solutions in high dimensional state spaces without requiring a hum

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Wang, Jiasheng, and Aziz Nazha. "Autonomous Analysis of CIBMTR Datasets Using Artificial Intelligence Agents." Blood 144, Supplement 1 (2024): 7489. https://doi.org/10.1182/blood-2024-207380.

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Background: Analyzing complex medical data requires specialized knowledge and expertise, making it both time-consuming and resource-intensive. Large language models (LLMs), such as GPT-4, excel in tasks like coding and medical statistics. However, analyzing datasets is more intricate than interacting with a chatbot. It involves several critical steps: planning, tracking information, locating data, and developing and refining the right statistical analyses. Artificial intelligence (AI) agents represent a new trend, where each AI agent can perform specific tasks based on prior defined instructio

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Ameya Gokhale. "Autonomous AI Agents in Online Retail: The Next Leap in Programmatic Media Buying." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 2713–22. https://doi.org/10.32628/cseit25112732.

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The retail industry stands at the brink of transformation driven by autonomous AI agents that will redefine shopping experiences, optimize advertising strategies, and streamline seller onboarding. AI agents will be personalized shopping assistants, intelligent advertising optimizers, and automated seller support systems, creating a seamless and highly efficient retail ecosystem. This technological evolution will personalize consumer interactions, automate advertising campaign management, and lower entry barriers for sellers, making e-commerce more accessible and profitable for all stakeholders

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Thaker, Jwalin. "Autonomous Web3 Browsing: Leveraging Decentralized AI Agents for Personalized and Privacy-Preserving Experiences." International Scientific Journal of Engineering and Management 01, no. 01 (2022): 1–6. https://doi.org/10.55041/isjem00175.

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Abstract—This white paper explores the transformative potential of decentralized AI agents operating within the Web3 infrastructure to enhance user experiences through personalized and privacy- preserving browsing. We investigate how these AI agents can autonomously navigate the web, utilizing smart contracts for automated decision-making processes that prioritize user preferences and privacy. The paper outlines a robust technical framework that includes decentralized AI models running on blockchain networks, integration with existing Web3 protocols such as IPFS and ENS, and the implementation

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Rosenthal, Stephanie, and Reid Simmons. "Autonomous Agents: An Advanced Course on AI Integration and Deployment." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 15843–50. http://dx.doi.org/10.1609/aaai.v37i13.26881.

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A majority of the courses on autonomous systems focus on robotics, despite the growing use of autonomous agents in a wide spectrum of applications, from smart homes to intelligent traffic control. Our goal in designing a new senior-level undergraduate course is to teach the integration of a variety of AI techniques in uncertain environments, without the dependence on topics such as robotic control and localization. We chose the application of an autonomous greenhouse to frame our discussions and our student projects because of the greenhouse's self-contained nature and objective metrics for su

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Ananthakrishnan, Vasudevan, Shemeer Sulaiman Kunju, and Radhakrishnan Pachyappan. "Autonomous GenAI Agents for Legacy-to-Cloud ETL Modernization." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 1, no. 1 (2024): 274–90. https://doi.org/10.60087/jaigs.v1i1.377.

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The modernization of Extract, Transform, Load (ETL) processes from legacy systems to cloud-native architectures is critical for enhancing scalability, agility, and cost-efficiency in enterprise data management. Traditional manual modernization approaches, however, are time-intensive, error-prone, and require specialized expertise. This research introduces a novel framework leveraging autonomous Generative AI (GenAI) agents to automate the end-to-end legacy-to-cloud ETL modernization. The proposed agents autonomously analyze legacy ETL logic (e.g., SQL scripts, COBOL jobs), redesign pipelines u

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Arunraju Chinnaraju. "AI-powered consumer segmentation and targeting: A theoretical framework for precision marketing by autonomous (Agentic) AI." International Journal of Science and Research Archive 14, no. 2 (2025): 401–24. https://doi.org/10.30574/ijsra.2025.14.2.0370.

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Consumer segmentation and targeting are essential for precision marketing, allowing businesses to deliver personalized experiences. The article explores the transformative role of autonomous AI agents in enhancing consumer segmentation and targeting within the data-driven marketing landscape. The proposed framework integrates machine learning (ML), natural language processing (NLP), and predictive analytics to continuously optimize segmentation models, enabling real-time targeting and hyper-personalization without human oversight. Autonomous agents dynamically manage segmentation by leveraging

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Buzu, Irina. "The Metaverse and Legal Status of Intelligent Agents." Studii Juridice Universitare, no. 2 (November 2024): 153–69. http://dx.doi.org/10.54481/sju.2023.2.11.

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Recent advances in artificial intelligence (AI) and the expansion of the metaverse have brought legal challenges to AI avatars, which are becoming increasingly sophisticated and autonomous. These avatars, capable of complex interactions, raise questions about their legal status and liability framework that require a paradigm shift. The article explores the legal implications of AI avatars, proposing new legal approaches to protect the rights and interests of parties involved in the metaverse.

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Fuchs, Andrew, Andrea Passarella, and Marco Conti. "Compensating for Sensing Failures via Delegation in Human–AI Hybrid Systems." Sensors 23, no. 7 (2023): 3409. http://dx.doi.org/10.3390/s23073409.

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Given the increasing prevalence of intelligent systems capable of autonomous actions or augmenting human activities, it is important to consider scenarios in which the human, autonomous system, or both can exhibit failures as a result of one of several contributing factors (e.g., perception). Failures for either humans or autonomous agents can lead to simply a reduced performance level, or a failure can lead to something as severe as injury or death. For our topic, we consider the hybrid human–AI teaming case where a managing agent is tasked with identifying when to perform a delegated assignm

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Vamsi Krishna Kumar Karanam. "Autonomous agents in the cloud: Advancing application management with agentic AI." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 4291–300. https://doi.org/10.30574/wjarr.2025.26.2.2122.

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Autonomous agents in cloud computing represent a transformative evolution beyond traditional automation approaches, enabling self-directed management of complex application environments. This article explores the architectural framework, implementation patterns, and operational benefits of Agentic AI in cloud-based application management. Unlike conventional automation systems constrained by static rules and predetermined workflows, autonomous agents leverage advanced machine learning techniques to perceive environmental conditions, learn from interactions, and take independent actions aligned

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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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Praneeth, Vadlapati. "Agent-Supervisor: Supervising Actions of Autonomous AI Agents to Ensure Ethical Compliance." International Journal on Science and Technology 14, no. 4 (2023): 1–9. https://doi.org/10.5281/zenodo.14288330.

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The rapid adoption of Artificial Intelligence (AI) agents in decision-making involves autonomous selection of tools and execution of actions. User interactions with agents create concerns regarding the autonomous selection of inappropriate tools and the oversharing of unnecessary or sensitive data of the users with APIs, which causes concerns regarding privacy. The selection of malicious tools causes further concerns related to user safety. This paper proposes a comprehensive framework to evaluate actions performed by AI agents through a Large Language Model (LLM), which acts as a supervisory

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Omeiza, Daniel, Pratik Somaiya, Jo-Ann Pattison, et al. "S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous Driving." Proceedings of the AAAI Symposium Series 4, no. 1 (2024): 89–96. http://dx.doi.org/10.1609/aaaiss.v4i1.31776.

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As artificial intelligence (AI) technology advances, ensuring the robustness and safety of AI-driven systems has become paramount. However, varying perceptions of robustness among AI developers create misaligned evaluation metrics, complicating the assessment and certification of safety-critical and complex AI systems such as autonomous driving (AD) agents. To address this challenge, we introduce Simulation-Based Robustness Assessment Framework (S-RAF) for autonomous driving. S-RAF leverages the CARLA Driving simulator to rigorously assess AD agents across diverse conditions, including faulty

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Chetan Manda. "The PTSA Framework: An Enterprise Architecture for Autonomous AI Agents." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 2125–36. https://doi.org/10.32628/cseit2410612395.

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The PTSA (Personality, Task, Skill, Accountability) Framework represents a significant advancement in developing autonomous AI agents for enterprise applications, addressing fundamental challenges in reliability, scalability, and operational effectiveness. This article introduces a comprehensive architectural approach that integrates four essential components: personality modeling for consistent interaction patterns, task orchestration for complex workflow management, skill integration for tool utilization, and accountability mechanisms for performance tracking. Personality modeling in the PTS

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Kota, Sunil Karthik. "The Evolution of AI Agents: From Rule‐Based Systems to Autonomous Intelligence – A Comprehensive Review." Journal of Artificial Intelligence & Cloud Computing 4, no. 2 (2025): 1–5. https://doi.org/10.47363/jaicc/2025(4)433.

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Artificial Intelligence (AI) agents have evolved from early rule‐based systems to today’s sophisticated autonomous systems. This comprehensive review examines the historical development, technical advancements, and emerging trends in AI agent research.

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Kosaraju, Deekshitha. "AI and Multi-Agent Systems: Collaboration and Competition in Autonomous Environments." International Journal of Research and Review 10, no. 12 (2024): 883–88. https://doi.org/10.52403/ijrr.20231288.

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Cutting edge technology in intelligence involves multi agent systems (MAS) which allow autonomous agents to interact in shared environments by either working together or competing to achieve common or individual goals. This study delves into the aspects of cooperation and rivalry in MAS and illustrates their application in practical situations, like autonomous vehicles, robot’s interactions, and financial settings. In addition to that we explore the obstacles like coordination, learning and communication that come up while creating MAS frameworks and how sophisticated algorithms like deep rein

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Arunraju Chinnaraju. "AI-driven strategic decision-making on innovation: Scalable, ethical approaches and ai agents for startups." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 2219–48. https://doi.org/10.30574/wjarr.2025.25.2.0575.

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The integration of Artificial Intelligence (AI) into strategic decision-making is transforming business landscapes, offering startups unprecedented opportunities to scale, optimize operations, and drive innovation. While AI adoption is well-documented in large enterprises, startups often face unique challenges, including limited financial and technical resources, ethical concerns, and the need for adaptable frameworks. This article bridges the gap by presenting a scalable AI adoption model tailored for startups, outlining resource-efficient strategies, and emphasizing ethical governance to ens

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Chinnaraju, Arunraju. "AI-powered consumer segmentation and targeting: A theoretical framework for precision marketing by autonomous (Agentic) AI." International Journal of Science and Research Archive 14, no. 2 (2025): 401–24. https://doi.org/10.30574/ijsra.2025.14.2.0370.

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<strong>This is the author-archived version of the article titled</strong><em>“AI-Powered Consumer Segmentation and Targeting: A Theoretical Framework for Precision Marketing by Autonomous (Agentic) AI”</em>,published in <em>International Journal of Science and Research Archive</em>, Vol. 14, Issue 2, 2025. The official published version is available at: https://doi.org/10.30574/ijsra.2025.14.2.0370 URL: https://journalijsra.com/sites/default/files/fulltext_pdf/IJSRA-2025-0370.pdf This version has been deposited on Zenodo to enhance academic accessibility and ensure indexing throug

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Ashutosh Rana. "Implementing Assistive and Autonomous Agents: A Technical Framework for Business Transformation with Google Cloud." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1294–304. https://doi.org/10.32628/cseit25112462.

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This article explores the implementation framework for assistive and autonomous agents utilizing Google Cloud's infrastructure, focusing on their transformative impact on business operations and decision-making processes. The article examines the distinct characteristics and applications of both agent types, highlighting how assistive agents enhance human capabilities through real-time analysis and decision support, while autonomous agents operate independently using sophisticated self-learning algorithms. The article delves into the technical infrastructure requirements, including data proces

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Chinnaraju, Arunraju. "AI-driven strategic decision-making on innovation: Scalable, ethical approaches and ai agents for startups." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 2219–48. https://doi.org/10.30574/wjarr.2025.25.2.0575.

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<strong>This is the author-archived version of the article titled</strong><em>“AI-Driven Strategic Decision-Making on Innovation: Scalable, Ethical Approaches, and AI Agents for Startups”</em>,published in <em>World Journal of Advanced Research and Reviews</em>, Vol. 25, Issue 2, 2025. The official published version is available at: https://doi.org/10.30574/wjarr.2025.25.2.0575 URL: https://journalwjarr.com/sites/default/files/fulltext_pdf/WJARR-2025-0575.pdf This version has been deposited on Zenodo to enhance scholarly visibility and facilitate indexing in research databases such

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Soumen Chakraborty. "From DataOps to AIOps: How autonomous agents are revolutionizing data engineering." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 1403–14. https://doi.org/10.30574/wjaets.2025.15.2.0650.

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This comprehensive article examines the paradigm shift from traditional DataOps to AI-powered DataOps (AIOps), highlighting how autonomous agents are fundamentally transforming data engineering practices. The evolution represents not merely a technological upgrade but a complete reimagining of data pipeline management—moving from human-centered operations to self-learning, autonomous systems. The article explores the core pillars of AIOps: automated observability that contextually understands metrics beyond simple collection, predictive issue resolution that anticipates and prevents problems b

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Shadrach C Matthew, Sanjay Siddharthan R, and Elavarasan R. "Adaptive Neuro-Symbolic Systems for Real Time Ethical Decision-Making in Autonomous Agents." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 04 (2025): 1571–76. https://doi.org/10.47392/irjaem.2025.0254.

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With the rapid emergence of autonomous systems, appropriate robust frameworks that could make ethical decisions in real time are needed. The adaptive neuro-symbolic approach to decision making by autonomous agents is thus presented here, integrating the advantages of symbolic ability like conventional AI with the adaptability imparted through neural networks. This proposed system enables symbolic reasoning by the AI along with learning from data, thus ensuring transparency and adaptability in decisions. This system, with deep learning models integrated with symbolic representations, would have

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Langley, Pat. "Agents of Exploration and Discovery." AI Magazine 42, no. 4 (2022): 72–82. http://dx.doi.org/10.1609/aimag.v42i4.15089.

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Autonomous agents have many applications in familiar situations, but they also have great potential to help us understand novel settings. In this paper, I propose a new challenge for the AI research community: developing embodied systems that not only explore new environments but also characterize them in scientific terms. Illustrative examples include autonomous rovers on planetary surfaces and unmanned vehicles on undersea missions. I review two relevant paradigms: robotic agents that explore unknown areas and computational systems that discover scientific models. In each case, I specify the

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Langley, Pat. "Agents of Exploration and Discovery." AI Magazine 42, no. 4 (2022): 72–82. http://dx.doi.org/10.1609/aaai.12021.

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Autonomous agents have many applications in familiar situations, but they also have great potential to help us understand novel settings. In this paper, I propose a new challenge for the AI research community: developing embodied systems that not only explore new environments but also characterize them in scientific terms. Illustrative examples include autonomous rovers on planetary surfaces and unmanned vehicles on undersea missions. I review two relevant paradigms: robotic agents that explore unknown areas and computational systems that discover scientific models. In each case, I specify the

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Khanfir, Mondher, and Sana Karray. "Navigating the AI Frontier." Journal of Ethics in Higher Education, no. 4 (July 19, 2024): 123–42. http://dx.doi.org/10.26034/fr.jehe.2024.5965.

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The rapid digital transformation driven by Artificial Intelligence (AI) is reshaping innovation and operational efficiency across industries. As autonomous AI systems are becoming prevalent, they significantly influence traditional business models, societal norms, and legal frameworks. AI technologies are evolving beyond mere tools to become independent economic agents capable of generating assets, making decisions, commercializing products and services, and being accountable for their actions. This evolution requires a reassessment of traditional concepts of corporate and moral personhood, pa

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Geoghegan, Bernard Dionysius. "Agents of History." Interaction Studies 9, no. 3 (2008): 403–14. http://dx.doi.org/10.1075/is.9.3.03geo.

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World War II research into cryptography and computing produced methods, instruments and research communities that informed early research into artificial intelligence (AI) and semi-autonomous computing. Alan Turing and Claude Shannon in particular adapted this research into early theories and demonstrations of AI based on computers’ abilities to track, predict and compete with opponents. This formed a loosely bound collection of techniques, paradigms, and practices I call crypto-intelligence. Subsequent researchers such as Joseph Weizenbaum adapted crypto-intelligence but also reproduced aspec

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Karim, Md Monjurul, Dong Hoang Van, Sangeen Khan, Qiang Qu, and Yaroslav Kholodov. "AI Agents Meet Blockchain: A Survey on Secure and Scalable Collaboration for Multi-Agents." Future Internet 17, no. 2 (2025): 57. https://doi.org/10.3390/fi17020057.

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In recent years, the interplay between AI agents and blockchain has enabled secure and scalable collaboration among multi-agent systems, promoting unprecedented levels of autonomy and interoperability. AI agents play a vital role in facilitating complex decision making and improving operational efficiency in blockchain systems. This collaborative synergy is particularly evident in how multi-agent systems collectively tackle complex tasks to ensure seamless integration within these frameworks. While significant efforts have been made to integrate AI agents and blockchain, most studies overlook

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Vikas Reddy Penubelli. "Autonomous CRM agents: Architecting intelligent assistants for scalable, human-like customer engagement." Global Journal of Engineering and Technology Advances 23, no. 3 (2025): 209–15. https://doi.org/10.30574/gjeta.2025.23.3.0196.

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The evolution of customer relationship management (CRM) platforms is entering a new era with the integration of generative AI and autonomous agent architectures. This article explores the transformation of CRM systems, from traditional models to AI-powered platforms that leverage large language models for automating customer engagement. We discuss the architectural primitives, design patterns, and agent-enablement infrastructures that support autonomous CRM agents. Security, compliance, and transparency in these systems are also addressed. Furthermore, we explore the skills and platform capabi

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Amanda Taylor. "Artificial Intelligence Agent Frameworks in Financial Stability: Innovations, Challenges, Applications." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 2553–61. https://doi.org/10.30574/wjaets.2025.15.3.1191.

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Artificial Intelligence (AI) agents are revolutionizing industries by enabling autonomous decision-making, task execution, multi-agent collaboration. This paper provides a comprehensive review of AI agent frameworks, focusing on their architectures, applications, challenges in financial services. We conduct a comparative analysis of leading frameworks, including LangGraph, CrewAI, AutoGen, evaluating their strengths, limitations, suitability for complex financial tasks such as trading, risk assessment, investment analysis. The integration of AI agents in financial markets presents both opportu

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Milinkovic, Igor. "The Moral and Legal Status of Artificial Intelligence (Present Dilemmas and Future Challenges)." Law and Business 1, no. 1 (2021): 29–36. http://dx.doi.org/10.2478/law-2021-0004.

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Abstract The rapid development of artificial intelligence (AI) systems raises dilemmas regarding their moral and legal status. Can artificial intelligence possess moral status (significance)? And under what conditions? Can one speak of the dignity of artificial intelligence as the basis of its moral status? According to some authors, if there are entities who have the capacities on which the dignity of human beings is based, they would also possess intrinsic dignity. If dignity is not an exclusive feature of human beings, such status also could be recognised by artificial intelligence entities

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