Jul 2026· World Journal of Information Technology· Vol 4, pp. 10-16· 0 citations· 24 references
TL;DR
AI Agents are driving the transformation of sci-tech intelligence analysis from "human-in-the-loop" to "human-on-the-loop," enabling intelligent and pipeline-based intelligence production processes.
Abstract
Objective: In the data-intelligent era, sci-tech intelligence analysis faces the dual challenges of information overload and insufficient depth in knowledge mining. Driven by large language models, AI Agents—as new autonomous actors capable of perception, planning, execution, and memory—are profoundly reshaping the intelligence workflow paradigm. This paper aims to systematically analyze the current applications, core capabilities, and development trends of AI Agents in sci-tech intelligence analysis, providing a reference for theoretical evolution and innovative practice in information science. Process: Using literature review and logical deduction, this paper first constructs an analytical framework encompassing four elements: "human–agent–information–technology." It then examines the core capabilities of AI Agents, from planning and memory to multi-agent collaboration. On this basis, it delves into their current applications in intelligence perception, organization, and generation. Finally, it discusses emerging trends such as human–AI collaboration paradigms and knowledge production transformation, along with potential challenges. Conclusion: AI Agents are driving the transformation of sci-tech intelligence analysis from "human-in-the-loop" to "human-on-the-loop," enabling intelligent and pipeline-based intelligence production processes. In the future, intelligence work will move toward a human–multi-agent collaborative paradigm, yet faces risks such as AI hallucinations and cognitive offloading, requiring urgent countermeasures at technical, literacy, and institutional levels.
GPT-5.5-like systems and their place represent in progressing agentic artificial intelligence (AI) in library environments. This study aims to investigate how autonomous, purpose-directed AI agents can disrupt fundamental library operations, including information access, user services, knowledge organization and research support, while meeting shifting user expectations in digital knowledge ecosystems.
This research is analytical-conceptual, based on an extensive review of recent literature regarding agentic AI and smart library systems. It reviews various theoretical models and practical applications and extracts important directions, technological features and areas of function that are important in context of conventional libraries that will be enabled for AI in future.
The results suggest that advanced models like GPT-5.5can run away with agentic AI systems. It provides substantial improvements in automation, personalization and proactive assistance in library services. Systems have the ability to perform complex operations independently, including semantic search, metadata generation, user interaction and guiding the user in the research process. The issues surrounding data privacy, good governance and the transparency of such systems remain major roadblocks.
This paper has added to existing conversation about AI and libraries by adopting an agentic lens with an emphasis on capabilities and autonomy. It provides advice for researchers and practitioners aiming to conceive future-ready, smart library systems.
Agentic Artificial Intelligence (Agentic AI) represents a major advancement in the evolution of intelligent systems by enabling autonomous planning, decision-making, and action execution. Unlike traditional AI models, which are primarily reactive and designed to respond to predefined inputs, Agentic AI systems possess capabilities such as memory, reasoning, goal-oriented planning, tool integration, and dynamic adaptation to changing environments. These characteristics allow them to perform complex, multi-step tasks with minimal human intervention, making them suitable for applications across healthcare, finance, cybersecurity, robotics, education, and enterprise automation. This paper explores the conceptual foundations, architectural principles, and practical applications of Agentic AI while examining the key differences between conventional AI and autonomous agent-based systems. It presents a taxonomy that distinguishes reactive and agentic models, discusses single-agent and multi-agent orchestration frameworks, and analyzes the role of memory, planning, perception, and external tool usage in intelligent decision-making. The study also highlights the ethical, security, governance, and accountability challenges associated with deploying autonomous AI, including issues related to transparency, privacy, bias, reliability, and human oversight. As AI systems become increasingly capable of operating independently within complex environments, establishing robust governance and regulatory frameworks is essential. This paper proposes a comprehensive framework for the design, evaluation, and responsible deployment of Agentic AI, emphasizing safety, explainability, human-in-the-loop supervision, and ethical compliance. By integrating technical innovation with effective governance strategies, the proposed approach aims to maximize the benefits of Agentic AI while minimizing potential risks. The findings contribute to the growing body of research on trustworthy autonomous systems and provide practical guidance for developing secure, reliable, and human-centered Agentic AI solutions.
Dr. Nitin S. Shrirao, Mr. Dnyaneshwar S. Jadhav, Mithun B. Patil· Recent Trends in Mathematics· 0 citations
This article examines the emerging paradigm of agentic AI for scientific discovery, traces the conceptual shift from tools to agents, lays out a six-stage workflow spanning literature synthesis to manuscript generation, and reviews practical systems in chemistry, equation discovery, materials science, and general machine learning research.
Alexander Taktakidze· Longevity Horizon· 0 citations
Against the backdrop of the deepening development of the digital economy and the ongoing integration of the digital and physical worlds, artificial intelligence has evolved from a single-function technical tool into a core strategic capability that supports organizations in building long-term competitive advantages. As a core construct that explains differences in the value transformation of AI technology, AI capability has become a hot topic of research in the fields of strategic management and information systems. However, existing research is scattered across diverse disciplinary perspectives and application scenarios, and has yet to form a unified research framework or theoretical system. Based on 33 core publications in the field of AI capabilities, this paper conducts a systematic review following the logical framework of “conceptual evolution-theoretical foundations-application scenarios-research outlook.” The study traces the evolutionary path of AI capabilities, from their origins in IT capability research to the development of a general three-dimensional construct, and further expansion into specialized technological forms and specific application scenarios; it synthesizes a theoretical framework centered on the resource-based view and dynamic capabilities theory, complemented by multiple theoretical perspectives; and summarizes the application progress and heterogeneity in value realization of AI capabilities across eight major scenarios, including green innovation, supply chain management, public governance, and business model innovation; Finally, it identifies limitations in existing research regarding research design, theoretical perspectives, scenario coverage, and risk governance, and proposes future research directions. This paper integrates and constructs a comprehensive research framework for AI capabilities, clarifies the field’s consensus and research gaps, and not only enriches the theoretical research landscape in the field of AI capabilities but also provides practical guidance for organizations of various types to systematically build AI capabilities and achieve the transformation of technological value.
Hao Jiang· International Journal of Edu...· 0 citations
By foregrounding decision intelligence in complex systems, Enactive AI expands the frontier of AI from model capability to system-aware action, opening new possibilities for scalable, governable, and socially valuable AI deployment.
Zuo-Jun Max Shen, Yuan Qu, Pujun Zhang et al.· 0 citations
An organizing framework for understanding LLM‐based agents is established, systematically deconstructing both single‐agent and multi‐agent systems into their core components, and the architectural principles and key mechanisms that underpin their intelligence are analyzed.
Yuheng Cheng, Ceyao Zhang, Zheng-Wen Zhang et al.· WIREs Data Mining and Knowle...· 1 citation
