[Purpose/Significance] Currently, large language models (LLMs) and agents have emerged as core technical paradigms in artificial intelligence, with their integration into scientific research scenarios holding profound significance for transforming research paradigms. Traditional scientific research is facing an increasing number of challenges such as inefficient literature searches, the processing of massive amounts of data, repetitive experimental tasks, and barriers to collaborative innovation. Agents, empowered by LLMs, offer a promising solution to these bottlenecks by enabling intelligent automation and adaptive collaboration across research workflows. Beyond basic task assistance, they play a pivotal role in facilitating knowledge fusion, accelerating breakthroughs in frontier areas, and reshaping traditional research models. This study aims to clarify the core techniques and applications of agents in scientific research, highlighting their transition from auxiliary tools to integral innovation partners, which is crucial for accelerating knowledge discovery, enhancing research efficiency, and promoting the shift toward intelligent and collaborative research models. [Method/Process] Employing an objective, inductive approach, this study thoroughly explains the core technical modules of agents including planning, perception, action, and memory, as well as the operational mechanisms of multi-agent collaboration. It also integrates an analysis of agent applications throughout the entire scientific research lifecycle. This analysis covers key scenarios including literature review and idea formulation, experimental planning and design, data processing and execution, result analysis and knowledge discovery, and research report composition. By analyzing the application value and existing limitations of agents, this study proposes prospects and recommendations for the application and development of agents in scientific research scenarios. [Results/Conclusions The findings reveal that LLM-driven agents are evolving from basic task executors to active participants in scientific discovery, demonstrating significant transformative potential throughout the entire research workflow. They facilitate more efficient information processing, smarter experimental design, and deeper knowledge integration, thereby redefining traditional research patterns. However, several challenges persist, including limitations in long-range reasoning capabilities, and underdeveloped ecosystem support. There are also ethical and security concerns, such as data privacy and academic integrity. To address these, future efforts should focus on strengthening intelligent computing infrastructure for scientific data, deepening collaborative development of domain-specific agents, establishing a unified open collaboration framework with standardized interfaces, and building "human-in-the-loop" hybrid systems and multiple evaluation mechanisms. These measures will enable agents to become core partners in scientific innovation, driving the transition of research paradigms toward greater intelligence and collaboration.

[Purpose/Significance] In the era of cloud computing, the Library Services Platform (LSP) failed to become a unified solution for libraries it promised to be. Now, it faces new development bottlenecks in the era of smart libraries. Its relatively rigid architecture, isolated data models, and limited intelligence level make it difficult to meet modern users' urgent demands for access to new resource ecosystems and proactive services. This limitation stems from the fact that existing LSPs are rooted in a resource management design philosophy. They lack native support for intelligence, personalization, and ecosystem integration, which hinders their ability to serve as a core component in the construction of smart libraries. [Method/Process] The rapid development of large language model (LLM) technology is promoting libraries to transition from digital intelligent phases into a new era of intelligent services. As AI agents are increasingly emerge as a core strategy for LLM applications, this paper proposes a next-generation LSP architecture called A-LSP, which is agent-oriented. The core of A-LSP consists of a three-layer logical model. 1) Layer 1: Compatibility & Tools - MCP Marketplace, serving as the foundation of the platform, this layer bridges the agent ecosystem with the external world. It transforms existing heterogeneous library systems (including legacy LSPs) and external tools into invocable "capability units" for agents through standardized protocols. 2) Layer 2: Orchestration & Intelligence－Agent Middleware. Functioning as the platform's "operating system" and "brain," this layer handles agent lifecycle management, task planning and decomposition, state and memory maintenance, and most crucially, the coordination of multi-agent collaboration. 3) Layer 3: Application & Ecosystem - Agent Marketplace. This functional layer serves users and developers, where various reusable agents encapsulating specific business logic are published, discovered, combined, and invoked, creating a rich application ecosystem. This architecture enables the implementation of new platform strategies without replacing legacy systems, establishing a modern technological platform with endogenous intelligence, inclusive compatibility, and an open ecosystem. This agent-based library service platform can be seen as a significant upgrade to existing LSPs, it drives their transformation from resource management-centric to agent service-centric, establishing itself as the library service platform for the AI era. [Results/Conclusions] Moreover, this paper puts forward a "Five Centers" construction demand framework for future libraries, namely, the Smart Resource Center, Smart Service Center, Smart Learning Center, Smart Scholarly Communication Center, and Smart Cultural Heritage Center, to build a blueprint for the integration of library technology and business. For each center, it delineates a representative complex application scenario and analyzes the underlying multi-agent collaboration processes, thereby clearly demonstrating A-LSP's deep integration with each center's operational logic and illuminating its profound impact on future library service models.

[Purpose/Significance] Every transformation and development in scientific and technological (S&T) documentation and information services has revolved around the application of advanced information technologies. Currently, cutting-edge AI technologies such as large-scale models and agents are driving a new wave of paradigm shifts in scientific research. Information institutions should consider how the paradigm of S&T documentation and information services should evolve to lay a strategic foundation for the development of the "15th Five-Year Plan" development. [Method/Process] This study uses objective induction and theoretical reasoning methods. It starts with the three driving modes of AI empowering scientific research and combines them with the essence of information work. The study concludes and summarizes that AI empowers S&T documentation and information services in two main areas: information infrastructure (data production, information organization, and knowledge representation) and information generation (intelligence computation). Agents integrated with large-scale modelling technologies demonstrate exceptional, even scientist-level, data understanding capabilities, suggesting that they are already capable of enabling information generation. [Results/Conclusions] Building and deploying DIS agents is an inevitable choice for information institutions as they prepare for the "15th Five-Year Plan". Driven by DIS agents, S&T documentation and information services will achieve higher levels of automation and intelligence, freeing information professionals from tedious basic data processing tasks and allowing them to focus on generating high-value information and supporting decision making. In the ecosystem of S&T documentation and information services driven by DIS agents, clusters of agents form the core and work together both internally and externally: Internally, DIS agents achieve a high level of automation in four core functions: data production, information organization, knowledge representation, and intelligence computation through the integration of planning tools, basic data and infrastructure resources. Externally, through interactions between agents, information experts, and specific intelligence scenarios, a new working paradigm emerges: "human and multi-agent collaboration". In the future, when planning and designing the implementation of DIS agents, it is essential to focus on both the technical adaptability at the current R&D stage and the potential security risks in future application stages. This ensures the efficient and secure use of DIS agents in S&T documentation and information services.

[Purpose/Significance] Through interdisciplinary comparison, the core connotation, common core and field differentiation of the Agent concept are revealed, the Agent-related concepts and theories contained in library science are revealed, and the innovative value of AI Agent driven by large language models to the core services of libraries is analyzed to promote the transformation of knowledge services to intelligent and collaborative paradigms. Understanding the interdisciplinary nature of Agents will help library science, information science and other related disciplines to better design and apply AI technologies and achieve the core mission of connecting humans and knowledge in a more efficient, accurate and humane way. It will also enable library science to more accurately integrate the essence of the six major disciplines and transform the traditional three-subject relationship of readers, librarians and systems into a new collaborative paradigm. [Method/Process] Using interdisciplinary literature analysis, the definition, theoretical evolution and application characteristics of Agent in six major disciplines of philosophy, economics, law, biology, sociology and computer science are sorted out, the concepts and theories related to Agent contained in library science are explored, and the commonalities and differentiation of Agent in the five-dimensional characteristics of autonomy, perception, purpose, adaptability and interactivity of each discipline are compared. The theoretical essence of the six major disciplines is mapped to the three-dimensional subject in the practical field of library science, and the Agent role coordination mechanism of readers, librarians and systems is analyzed. Readers are entities with intentions and autonomous consciousness, and they will actively initiate information search behaviors based on information needs such as learning knowledge and solving problems and will also adjust their strategies according to environmental changes such as technical tools and social culture, reflecting agent-like adaptability. Librarians serve as service intermediaries and information gatekeepers, connecting readers with resources through technical services such as classification and cataloging, and helping users clarify their information needs and improve their information literacy through reader services such as reference consultation. The library's information systems will also simulate human agent capabilities through algorithms or technologies. Automated search engines or crawlers will collect data according to preset rules, and personalized information recommendations will be made based on user behavior, driving the library's management and services towards automation and intelligence. [Results/Conclusions] The commonality of interdisciplinary agents revolves around the realization of goals by autonomous actors in the environment. The five-dimensional characteristics constitute an interdisciplinary consensus, and the differences are due to the core issues of the disciplines. The essence of a library is a multi-agent system. The reader agent integrates philosophical intentionality and economic game strategy, reflecting demand-driven adaptive retrieval. The librarian agent inherits the legal agency rights and responsibilities and the sociological structural initiative, becoming a professional intermediary between resources and users. The system agent draws on the biological evolutionary adaptation and computer perception closed loop, and advances to an intelligent base for autonomous optimization. AI Agent is a technical enhancement of the inherent agent characteristics of library science. It realizes automation, personalization and intelligent service upgrades through large language models, realizes intention understanding, tool calling, and multi-agent collaboration, and drives the three-element subject from passive response to active collaboration. The three-element agent framework for library science is created, which clarifies the collaborative agent roles of readers, librarians, and systems, and reveals the deep logic of AI Agent driven by large language models and library science. An interdisciplinary comparative study of the Agent concepts reveals that its essence is a practical vehicle for achieving autonomous decision-making in a specific environment. Philosophy gives it depth of consciousness, economics models strategic games, law defines the boundaries of rights and responsibilities, biology reveals evolutionary logic, sociology anchors structural interactions, and computer science ultimately achieves a closed-loop technology. Library science constructs a ternary collaborative intelligent ecosystem that transforms abstract autonomy into a practical paradigm of knowledge connection through the dynamic collaboration of readers, librarians, and systems.