Artificial intelligence(AI)is reshaping financial systems and services,as intelligent AI agents increasingly form the foundation of autonomous,goal-driven systems capable of reasoning,learning,and action.This review s...Artificial intelligence(AI)is reshaping financial systems and services,as intelligent AI agents increasingly form the foundation of autonomous,goal-driven systems capable of reasoning,learning,and action.This review synthesizes recent research and developments in the application of AI agents across core financial domains.Specifically,it covers the deployment of agent-based AI in algorithmic trading,fraud detection,credit risk assessment,roboadvisory,and regulatory compliance(RegTech).The review focuses on advanced agent-based methodologies,including reinforcement learning,multi-agent systems,and autonomous decision-making frameworks,particularly those leveraging large language models(LLMs),contrasting these with traditional AI or purely statistical models.Our primary goals are to consolidate current knowledge,identify significant trends and architectural approaches,review the practical efficiency and impact of current applications,and delineate key challenges and promising future research directions.The increasing sophistication of AI agents offers unprecedented opportunities for innovation in finance,yet presents complex technical,ethical,and regulatory challenges that demand careful consideration and proactive strategies.This review aims to provide a comprehensive understanding of this rapidly evolving landscape,highlighting the role of agent-based AI in the ongoing transformation of the financial industry,and is intended to serve financial institutions,regulators,investors,analysts,researchers,and other key stakeholders in the financial ecosystem.展开更多
Energy security is the association between national security and the availability of natural resources for energy consumption.Access to cheaper energy has become essential to the functioning of modern economies.Howeve...Energy security is the association between national security and the availability of natural resources for energy consumption.Access to cheaper energy has become essential to the functioning of modern economies.However,the uneven distribution of energy supplies among countries has led to significant challenges.International energy relations have contributed to the globalization of the world,leading to energy security and energy vulnerability at the same time.The first article in Science Direct for energy security appeared in 1993 with authors from USA.Gradually the number of articles and their origin increases.In 2024 we observe a boom of these publications-30(not only in Science Direct)and one planned for 2025.The recent article explores these publications and sorts them by origin and content.The content is distributed in five topics:computer technologies impact,artificial intelligence applications,environmental management and energy security,economics and policy for energy security,and emerging technologies for energy security.Since the share of AI usage is relatively small the author suggests scenarios for AI agents’application in various situations concerning environmental decisions,failures,cyber-attacks,performance,and pricing.展开更多
The integration of Green Artificial Intelligence(AI)technologies into educational systems offers a promising avenue to enhance operational efficiency while addressing sustainability challenges.Through a rigorous three...The integration of Green Artificial Intelligence(AI)technologies into educational systems offers a promising avenue to enhance operational efficiency while addressing sustainability challenges.Through a rigorous three-phase methodology combining literature review,AI agent development,and participatory workshop-based case analysis,this paper highlights the pivotal role of AI agents,as applications of Green AI technologies,in driving transformative outcomes within schools.By directly improving self-learning efficiency and reducing learning costs for students,enhancing management and service efficiency,reducing labor costs for schools,as well as minimizing resource dependence for both teachers and students,AI agents create a foundation for sustainable operations.These direct effects generate positive spillover effects,cascading into broader outcomes,including innovation performance,economic efficiency,and environmental sustainability,aligning with the United Nations Sustainable Development Goals(SDGs).By presenting a comprehensive conceptual model,this study demonstrates the pathways through which Green AI contributes to sustainable development in education and emphasizes its critical role in bridging technological innovation with sustainability.This framework provides significant theoretical insights for further empirical research while offering actionable strategies for policymakers and educators to harness Green AI for building sustainable schools with a student-centered approach.展开更多
Clinical pharmacy is on the cusp of exponential change powered by artificial intelligence agents,automation,data analytics,and robotics.Blockchain will enhance data integrity and transparency,and Augmented and Virtual...Clinical pharmacy is on the cusp of exponential change powered by artificial intelligence agents,automation,data analytics,and robotics.Blockchain will enhance data integrity and transparency,and Augmented and Virtual Reality technologies will revolutionise training,patient education,and simulation-based care planning.Clinical pharmacists need to be ready and upskill to prepare for emerging technologies.The ethical,regulatory,and educational frameworks surrounding artificial intelligence and precision medicine will require constant attention,but the potential benefits for patient outcomes are unprecedented.Clinical pharmacists are in a prime position to design a new era in precision medicine,where technology works hand in hand with humans to transform healthcare.展开更多
In response to the pain points of rapid iteration of front-end education technology,large differences in learner foundations,and a lack of practical scenarios,this paper combines generative artificial intelligence and...In response to the pain points of rapid iteration of front-end education technology,large differences in learner foundations,and a lack of practical scenarios,this paper combines generative artificial intelligence and AI agents to analyze the empowerment logic from three dimensions:knowledge ecology reconstruction,cognitive collaborative upgrading,and teaching methodology innovation.It explores its application scenarios in teaching and learning,sorts out challenges such as technology adaptation and learning dependence,and proposes paths such as building an exclusive AI ecosystem and optimizing the guidance mechanism of intelligent agents to provide support for the digital transformation of front-end education.展开更多
Governance scholarship overwhelmingly treats artificial intelligence as a tool—an object to be regulated,audited,or aligned with human values.This article argues that when organizations treat the outputs of AI agents...Governance scholarship overwhelmingly treats artificial intelligence as a tool—an object to be regulated,audited,or aligned with human values.This article argues that when organizations treat the outputs of AI agents as institutionally binding,these systems cross a threshold from instruments into institutional actors.Drawing on Goffman’s interaction order and Meyer and Rowan’s institutional isomorphism,we develop a diagnostic framework that specifies when and how AI agents acquire practical actorhood within organizations.We formalize a 2×2 Actorhood Matrix along two axes—discretion granted and institutional embedding—yielding four system types:Tool,Infrastructure,Shadow Actor,and Institutional Actor.The Actorhood Matrix is proposed as a reusable diagnostic method for identifying when AI systems cross the institutional threshold from tools to role occupants.Applying this framework to five empirical cases(Klarna’s AI customer service,GitHub Copilot Workspace,NHS AI triage,Harvey AI legal assistant,and generic FAQ chatbots)demonstrates that institutional actorhood is not a property of technical sophistication but of organizational role assignment.We identify a critical transition zone where override rates fall below ten percent and propose four testable propositions linking discretion,embedding,and temporal persistence to governance outcomes.The article concludes that governance frameworks designed for“tools”are structurally inadequate for systems that have become practical role occupants within institutional settings.展开更多
The rapid integration of high-proportion renewable energy and the escalating complexity of modern power systems have exposed critical limitations in conventional grid dispatching automation systems,particularly in the...The rapid integration of high-proportion renewable energy and the escalating complexity of modern power systems have exposed critical limitations in conventional grid dispatching automation systems,particularly in their ability to achieve real-time responsiveness,adaptive coordination,and collaborative decision-making.To address these challenges,this paper presents a novel next-generation dispatching automation framework that synergistically integrates large language models(LLMs),knowledge graphs(KGs),and AI agent technologies.The proposed system harnesses LLMs for advanced natural language processing and semantic reasoning,employs KGs to construct a structured,multidimensional knowledge representation of grid entities and their interdependencies,and deploys AI agents for autonomous decision-making and dynamic operational optimization.Collectively,these components establish a closedloop intelligent architecture encompassing“perception-cognitiondecision-execution”,enhancing system-wide situational awareness and adaptive control.This study proposes a unified integration methodology for LLMs,KGs,and AI agents in power system dispatching with modularized functional implementations designed for scalable deployment.Furthermore,it provides comprehensive multi-scenario analyses to verify operational efficacy.The proposed system offers a transformative technical pathway for advancing intelligent dispatching in next-generation power grids by resolving key bottlenecks in data interoperability,decision transparency,and dynamic adaptability.展开更多
As the global digital economy becomes deeply integrated,digital trade has emerged as a core engine of international trade growth.However,differing value orientations and interests have led to the emergence of diverse ...As the global digital economy becomes deeply integrated,digital trade has emerged as a core engine of international trade growth.However,differing value orientations and interests have led to the emergence of diverse regulatory frameworks with distinct emphases.This fragmentation ofregional regulatory systems conflicts with the large-scale cross-border flow of digital elements,thereby hindering the globalization of digital trade.From the perspective of regulatory adaptation,this article systematically analyzes how AI agents empower technological solutions to overcome digital trade barriers.It further reveals their unique opportunities in rule interpretation,dynamic compliance,and collaborative governance,and proposes a three-stage strategic pathway:“technology adaptation rule transformationecological collaboration.”This study finds that AI agents can effectively reduce the cost of regulatory adaptation and provide precise compliance solutions for digital trade entities through semantic parsing,real-time learning,and cross-domain collaboration.It offers a theoretical framework for technological pathways to overcoming digital trade barriers and provides strategic insights for China's participation in global digital governance.展开更多
AI Agent技术凭借环境感知、自主决策与协同执行能力,为财务共享服务中心的智能化升级提供了新型技术方案。财务共享中心作为集约化财务管理载体,在业务规模扩张与数据量激增的背景下,面临着风险识别滞后与资金结算效率不足等挑战,AI Ag...AI Agent技术凭借环境感知、自主决策与协同执行能力,为财务共享服务中心的智能化升级提供了新型技术方案。财务共享中心作为集约化财务管理载体,在业务规模扩张与数据量激增的背景下,面临着风险识别滞后与资金结算效率不足等挑战,AI Agent通过构建智能识别预警系统、动态评估管控体系、自动化应急处置机制及全程追踪管理框架,实现了交易异常、信用风险、资金异常与合规风险的精准防控。展开更多
AI Agent的“iPhone时刻”尚未到来2023年,AutoGPT的横空出世让科技界为之沸腾。这个能够自主分解任务、调用工具并迭代执行的AI框架,被许多人视为通用人工智能(AGI)的雏形。它在GitHub上的星标数短时间内便突破15万,社交媒体上也充斥着...AI Agent的“iPhone时刻”尚未到来2023年,AutoGPT的横空出世让科技界为之沸腾。这个能够自主分解任务、调用工具并迭代执行的AI框架,被许多人视为通用人工智能(AGI)的雏形。它在GitHub上的星标数短时间内便突破15万,社交媒体上也充斥着“AI取代人类工作”的惊叹。然而,这股热潮很快退去。用户发现,AutoGPT虽然概念惊艳,但在实际使用中常常陷入“思考循环”,不仅消耗大量API Token,产出却十分有限;而且配置复杂,对非技术用户来说门槛极高,缺乏稳定的输出质量控制机制,难以融入真实的工作流程。展开更多
Artificial intelligence has experienced a significant boom with the emergence of agentic AI,where autonomous agents are increasingly replacing human intervention,enabling systems to perceive,reason,and act independent...Artificial intelligence has experienced a significant boom with the emergence of agentic AI,where autonomous agents are increasingly replacing human intervention,enabling systems to perceive,reason,and act independently to achieve specific goals.Despite its transformative potential,comprehensive information on agentic AI remains scarce in the literature.This paper provides the first comprehensive review of agentic AI,focusing on its evolution and three core aspects:patterns,types,and environments.The evolution of agentic AI is traced through five phases to the current era of multi-modal and collaborative agents,driven by advancements in reinforcement learning,neural networks,and large language models(LLMs).Five key patterns:tool use,reflection,ReAct,planning,and multi-agent collaboration(MAC)define how agentic AI systems interact and process tasks.These systems are categorized into seven categories,each tailored for specific operational styles and autonomy in decision making.The environments in which these agents operate are classified as static,dynamic,fully observable,partially observable,deterministic,stochastic,single-agent,and multiagent,emphasizing the impact of environmental complexity on agent behavior.Agentic AI has revolutionized systems through autonomous decision making and resource optimization,yet challenges persist in aligning AI with human values,ensuring adaptability,and addressing ethical constraints.Future research focuses on multidomain agents,human–AI collaboration,and self-improving systems.This work provides researchers,practitioners,and policymakers with a structured approach to understanding and advancing the rapidly evolving landscape of agentic AI systems.展开更多
基金supported by the Ministry of Education and Science of the Republic of North Macedonia through the project“Utilizing AI and National Large Language Models to Advance Macedonian Language Capabilties”。
文摘Artificial intelligence(AI)is reshaping financial systems and services,as intelligent AI agents increasingly form the foundation of autonomous,goal-driven systems capable of reasoning,learning,and action.This review synthesizes recent research and developments in the application of AI agents across core financial domains.Specifically,it covers the deployment of agent-based AI in algorithmic trading,fraud detection,credit risk assessment,roboadvisory,and regulatory compliance(RegTech).The review focuses on advanced agent-based methodologies,including reinforcement learning,multi-agent systems,and autonomous decision-making frameworks,particularly those leveraging large language models(LLMs),contrasting these with traditional AI or purely statistical models.Our primary goals are to consolidate current knowledge,identify significant trends and architectural approaches,review the practical efficiency and impact of current applications,and delineate key challenges and promising future research directions.The increasing sophistication of AI agents offers unprecedented opportunities for innovation in finance,yet presents complex technical,ethical,and regulatory challenges that demand careful consideration and proactive strategies.This review aims to provide a comprehensive understanding of this rapidly evolving landscape,highlighting the role of agent-based AI in the ongoing transformation of the financial industry,and is intended to serve financial institutions,regulators,investors,analysts,researchers,and other key stakeholders in the financial ecosystem.
文摘Energy security is the association between national security and the availability of natural resources for energy consumption.Access to cheaper energy has become essential to the functioning of modern economies.However,the uneven distribution of energy supplies among countries has led to significant challenges.International energy relations have contributed to the globalization of the world,leading to energy security and energy vulnerability at the same time.The first article in Science Direct for energy security appeared in 1993 with authors from USA.Gradually the number of articles and their origin increases.In 2024 we observe a boom of these publications-30(not only in Science Direct)and one planned for 2025.The recent article explores these publications and sorts them by origin and content.The content is distributed in five topics:computer technologies impact,artificial intelligence applications,environmental management and energy security,economics and policy for energy security,and emerging technologies for energy security.Since the share of AI usage is relatively small the author suggests scenarios for AI agents’application in various situations concerning environmental decisions,failures,cyber-attacks,performance,and pricing.
基金2024 Academic Research of Zhejiang Technical Institute of Economics:“Spillover Effects of Multimodal AI Agents on Green School Development”(Project No.:X2024038)2024-2025 Research and Creative Project,Department of Culture and Tourism:“The Application of Digital Information Technology in Safety Early Warning and Supervision of Cultural Relics in Zhejiang,China”(Project No.:2024KYY045)2024 General Research Project of Zhejiang Provincial Department of Education:“Empirical Research on Low-Carbon Economy Driving the Development of New Quality Productivity:A Case Study of Zhejiang Province”(Project No.:Y202456145)。
文摘The integration of Green Artificial Intelligence(AI)technologies into educational systems offers a promising avenue to enhance operational efficiency while addressing sustainability challenges.Through a rigorous three-phase methodology combining literature review,AI agent development,and participatory workshop-based case analysis,this paper highlights the pivotal role of AI agents,as applications of Green AI technologies,in driving transformative outcomes within schools.By directly improving self-learning efficiency and reducing learning costs for students,enhancing management and service efficiency,reducing labor costs for schools,as well as minimizing resource dependence for both teachers and students,AI agents create a foundation for sustainable operations.These direct effects generate positive spillover effects,cascading into broader outcomes,including innovation performance,economic efficiency,and environmental sustainability,aligning with the United Nations Sustainable Development Goals(SDGs).By presenting a comprehensive conceptual model,this study demonstrates the pathways through which Green AI contributes to sustainable development in education and emphasizes its critical role in bridging technological innovation with sustainability.This framework provides significant theoretical insights for further empirical research while offering actionable strategies for policymakers and educators to harness Green AI for building sustainable schools with a student-centered approach.
文摘Clinical pharmacy is on the cusp of exponential change powered by artificial intelligence agents,automation,data analytics,and robotics.Blockchain will enhance data integrity and transparency,and Augmented and Virtual Reality technologies will revolutionise training,patient education,and simulation-based care planning.Clinical pharmacists need to be ready and upskill to prepare for emerging technologies.The ethical,regulatory,and educational frameworks surrounding artificial intelligence and precision medicine will require constant attention,but the potential benefits for patient outcomes are unprecedented.Clinical pharmacists are in a prime position to design a new era in precision medicine,where technology works hand in hand with humans to transform healthcare.
基金funded by two 2024 Ministry of Education supply-demand docking employment and education projects(Grant No.2024101679202,Grant No.2024121116066)2024“Innovation Strong Institute Project of Guangdong Polytechnic Institute”(Grant No.2024CQ-29)2022 Guangdong Province Undergraduate Online Open Course Guidance Committee Research Project(Grant No.2022ZXKC612).
文摘In response to the pain points of rapid iteration of front-end education technology,large differences in learner foundations,and a lack of practical scenarios,this paper combines generative artificial intelligence and AI agents to analyze the empowerment logic from three dimensions:knowledge ecology reconstruction,cognitive collaborative upgrading,and teaching methodology innovation.It explores its application scenarios in teaching and learning,sorts out challenges such as technology adaptation and learning dependence,and proposes paths such as building an exclusive AI ecosystem and optimizing the guidance mechanism of intelligent agents to provide support for the digital transformation of front-end education.
文摘Governance scholarship overwhelmingly treats artificial intelligence as a tool—an object to be regulated,audited,or aligned with human values.This article argues that when organizations treat the outputs of AI agents as institutionally binding,these systems cross a threshold from instruments into institutional actors.Drawing on Goffman’s interaction order and Meyer and Rowan’s institutional isomorphism,we develop a diagnostic framework that specifies when and how AI agents acquire practical actorhood within organizations.We formalize a 2×2 Actorhood Matrix along two axes—discretion granted and institutional embedding—yielding four system types:Tool,Infrastructure,Shadow Actor,and Institutional Actor.The Actorhood Matrix is proposed as a reusable diagnostic method for identifying when AI systems cross the institutional threshold from tools to role occupants.Applying this framework to five empirical cases(Klarna’s AI customer service,GitHub Copilot Workspace,NHS AI triage,Harvey AI legal assistant,and generic FAQ chatbots)demonstrates that institutional actorhood is not a property of technical sophistication but of organizational role assignment.We identify a critical transition zone where override rates fall below ten percent and propose four testable propositions linking discretion,embedding,and temporal persistence to governance outcomes.The article concludes that governance frameworks designed for“tools”are structurally inadequate for systems that have become practical role occupants within institutional settings.
文摘The rapid integration of high-proportion renewable energy and the escalating complexity of modern power systems have exposed critical limitations in conventional grid dispatching automation systems,particularly in their ability to achieve real-time responsiveness,adaptive coordination,and collaborative decision-making.To address these challenges,this paper presents a novel next-generation dispatching automation framework that synergistically integrates large language models(LLMs),knowledge graphs(KGs),and AI agent technologies.The proposed system harnesses LLMs for advanced natural language processing and semantic reasoning,employs KGs to construct a structured,multidimensional knowledge representation of grid entities and their interdependencies,and deploys AI agents for autonomous decision-making and dynamic operational optimization.Collectively,these components establish a closedloop intelligent architecture encompassing“perception-cognitiondecision-execution”,enhancing system-wide situational awareness and adaptive control.This study proposes a unified integration methodology for LLMs,KGs,and AI agents in power system dispatching with modularized functional implementations designed for scalable deployment.Furthermore,it provides comprehensive multi-scenario analyses to verify operational efficacy.The proposed system offers a transformative technical pathway for advancing intelligent dispatching in next-generation power grids by resolving key bottlenecks in data interoperability,decision transparency,and dynamic adaptability.
文摘As the global digital economy becomes deeply integrated,digital trade has emerged as a core engine of international trade growth.However,differing value orientations and interests have led to the emergence of diverse regulatory frameworks with distinct emphases.This fragmentation ofregional regulatory systems conflicts with the large-scale cross-border flow of digital elements,thereby hindering the globalization of digital trade.From the perspective of regulatory adaptation,this article systematically analyzes how AI agents empower technological solutions to overcome digital trade barriers.It further reveals their unique opportunities in rule interpretation,dynamic compliance,and collaborative governance,and proposes a three-stage strategic pathway:“technology adaptation rule transformationecological collaboration.”This study finds that AI agents can effectively reduce the cost of regulatory adaptation and provide precise compliance solutions for digital trade entities through semantic parsing,real-time learning,and cross-domain collaboration.It offers a theoretical framework for technological pathways to overcoming digital trade barriers and provides strategic insights for China's participation in global digital governance.
文摘Artificial intelligence has experienced a significant boom with the emergence of agentic AI,where autonomous agents are increasingly replacing human intervention,enabling systems to perceive,reason,and act independently to achieve specific goals.Despite its transformative potential,comprehensive information on agentic AI remains scarce in the literature.This paper provides the first comprehensive review of agentic AI,focusing on its evolution and three core aspects:patterns,types,and environments.The evolution of agentic AI is traced through five phases to the current era of multi-modal and collaborative agents,driven by advancements in reinforcement learning,neural networks,and large language models(LLMs).Five key patterns:tool use,reflection,ReAct,planning,and multi-agent collaboration(MAC)define how agentic AI systems interact and process tasks.These systems are categorized into seven categories,each tailored for specific operational styles and autonomy in decision making.The environments in which these agents operate are classified as static,dynamic,fully observable,partially observable,deterministic,stochastic,single-agent,and multiagent,emphasizing the impact of environmental complexity on agent behavior.Agentic AI has revolutionized systems through autonomous decision making and resource optimization,yet challenges persist in aligning AI with human values,ensuring adaptability,and addressing ethical constraints.Future research focuses on multidomain agents,human–AI collaboration,and self-improving systems.This work provides researchers,practitioners,and policymakers with a structured approach to understanding and advancing the rapidly evolving landscape of agentic AI systems.
