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The Emergence of Intelligent Agents: A Profound Reconstruction of Library Ontology and Future Prospects

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The text discusses the evolving role of libraries in light of advancements in artificial intelligence, particularly with the rise of AI agents driven by large language models. It argues that libraries should be viewed as "multi-agent systems" composed of readers, librarians, and systems, each embodying inherent characteristics of agency. Readers are seen as proactive agents with clear information needs, while librarians act as professional intermediaries who connect users with resources. Library systems themselves are evolving into intelligent entities capable of self-optimization. The introduction of AI agents enhances these characteristics, transforming library services from passive responses to proactive, collaborative engagements. AI agents can deeply understand user intentions, assist librarians by automating routine tasks, and enable library systems to adapt and optimize in real-time. However, this transformation raises ethical challenges, including algorithmic bias, accountability for AI decisions, and the potential impact on human agency and interaction. The text emphasizes the importance of maintaining fairness and promoting knowledge equity as libraries adapt to these new technologies. In conclusion, the future library is envisioned as a dynamic, intelligent, and just knowledge infrastructure that integrates human and artificial intelligence to provide effective and equitable knowledge services. This transformation requires a commitment to ethical considerations and a deep understanding of human-AI collaboration.

For a long time, libraries have been regarded as temples of knowledge and hubs of information, with their core functions centered on the organization, management, and service of resources. However, with the rapid development of artificial intelligence technology, particularly the rise of AI Agents driven by large language models (LLMs), it is necessary to conduct a profound ontological reconstruction of the essence of libraries. A forward-looking perspective in academia suggests that libraries are not merely static places or collections of tools; their essence is a "multi-agent system" composed of readers, librarians, and systems, which inherently contains characteristics related to Agents. The intervention of AI Agents is a key technological enhancement of these inherent characteristics. This assertion provides us with a new perspective to re-examine the value, function, and future development direction of libraries.

The Intrinsic Logic of Agents and the Deep Alignment with Library Science#

The concept of "Agent" is fundamentally about describing an entity that "has desires, beliefs, intentions, and the ability to act," with the basic logic being "autonomous agents achieving goals in an environment." This concept has unique interpretations and applications across various disciplines, including philosophy, economics, law, biology, sociology, and computer science, but generally presents five common dimensions: autonomy, perception, purpose, adaptability, and interactivity.

A deep analysis of the evolution of library science reveals that its theories and practices have long resonated with the intrinsic logic of agents:

  • Readers as intention-driven agents: Readers are not passive recipients of information but autonomous agents with clear information needs and learning intentions. From early studies on user information behavior, such as Ellis's information-seeking model emphasizing active operations, to Dervin's sense-making theory revealing users' dynamic adaptations in information gaps, and Taylor's theory of information needs positioning librarians as "need interpreters," all reflect the agent characteristics of readers in perceiving the environment, setting goals, and taking action to meet information needs. Readers' information-seeking behavior is driven by their intrinsic intentions (Desire), based on beliefs (Belief) about the information environment, formulating and executing plans (Intention), which aligns closely with the BDI (Belief-Desire-Intention) model in philosophy.
  • Librarians as professional intermediary agents: Librarians play a crucial role as "professional intermediaries" and "gatekeepers of information" in library services. Their work involves not only technical operations but also keen perception of user needs, professional judgment of resource systems, and decision-making abilities in complex situations. This resonates with the characteristics of "agents" in the legal field, where agents are authorized to act on behalf of clients and bear corresponding responsibilities, and echoes the sociological view of "actors" exercising agency under structural constraints to shape social relationships. Through services like classification, cataloging, and reference consultation, librarians connect readers with resources, and their abilities for autonomous decision-making, adaptive adjustment, and multi-party interaction make them the core hub in the library's multi-agent system.
  • Systems as autonomously optimizing intelligent bases: Library information systems, from early online retrieval to today's discovery systems and AI Agents, have evolved from simple rule execution to intelligent entities with perception, decision-making, and action loops. Systems perceive the environment through data collection, achieve goals through algorithm optimization, and can self-adjust and optimize based on user feedback and operational status. This draws on the concept of "evolutionary adaptation" in biology and the "perception-decision-action" architecture in computer science, upgrading them from passive tools to autonomously optimizing intelligent bases.

Thus, it is evident that the understanding of readers, librarians, and systems in the field of library science has long contained the core characteristics of agents. The emergence of AI Agents does not introduce a new concept out of thin air but provides unprecedented technological enhancements to these inherent agent characteristics, driving the paradigm of library services from traditional "responsive" to "proactive and predictive."

Empowerment of AI Agents: A Paradigm Shift from "Tools" to "Partners"#

AI Agents, particularly those driven by large language models, are profoundly transforming the triadic collaborative model of libraries through their powerful capabilities in intention understanding, tool invocation, and multi-agent collaboration:

  • The "superpowers" of reader agents: AI Agents can deeply understand readers' vague intentions and complex needs, assisting them in efficient information seeking and knowledge construction through natural language interaction. They no longer merely provide search results but can act like a seasoned research partner, proactively recommending relevant resources, connecting knowledge graphs, and even simulating expert dialogues to help readers accurately locate and delve into learning amidst vast information.
  • The "intelligent assistants" for librarian agents: AI Agents will become powerful auxiliary tools for librarians, taking on a large number of repetitive and rule-based tasks, such as intelligent cataloging, preliminary consultation responses, and data analysis. This allows librarians to be freed from tedious tasks and devote more energy to high-value professional services, such as in-depth knowledge curation, personalized learning guidance, complex information literacy education, and even becoming guardians of AI ethics and experts in data governance. Librarians will transition from "information providers" to "knowledge architects" and "learning designers."
  • The "self-adaptive evolution" of system agents: AI Agents will enable library systems to possess stronger self-perception, self-learning, and self-optimization capabilities. Systems can monitor their operational status in real-time, analyze user behavior patterns, and dynamically adjust resource allocation, service strategies, and interface presentation based on environmental changes, achieving true automation and intelligent management.

The core of this paradigm shift lies in the fact that AI Agents will prompt library services to transition from traditional "passive response" to "active collaboration." They will no longer wait for users to ask questions but will proactively identify potential needs; they will no longer merely provide information but will offer solutions and learning pathways. Libraries will evolve from static "information warehouses" to dynamic, adaptive "knowledge partners" and "intelligent ecosystems."

Challenges and Reflections: Justice, Human-Machine Coexistence, and Ethical Boundaries#

Although AI Agents paint an exciting picture for the future of libraries, as seasoned researchers, we must be acutely aware of the potential challenges and deep ethical issues they bring. If we view libraries as multi-agent systems, then the "autonomy" and "purpose" of AI Agents will inevitably touch upon the core of "justice":

  • Algorithmic bias and knowledge discrimination: The decisions and recommendations of AI Agents are based on their training data and algorithmic models. If these data or models contain biases, they may lead to information silos and exacerbate inequalities in knowledge acquisition. How can we ensure that the recommendations of AI Agents are neutral, diverse, and in line with the public interest? This requires us to establish strict algorithm auditing mechanisms and introduce explainable AI (XAI) technologies to make the decision-making processes of AI Agents transparent.
  • Ambiguity of responsibility: When AI Agents make errors or cause negative impacts in autonomous decision-making, who should bear the responsibility? Is it the AI Agent itself, the developers, the library, or the users? Legal and ethical frameworks need to be updated to clarify the rights and responsibilities of AI Agents in library services.
  • The "degree" and "quality" of human-machine collaboration: Will the powerful capabilities of AI Agents weaken readers' independent thinking abilities and information literacy? To what extent should librarians rely on AI assistance? How can the "human touch" and "warmth" of library services be maintained in the new technological environment? The core value of libraries lies in promoting knowledge exchange and intellectual collisions among humans. While AI Agents enhance efficiency, how can we avoid hindering this deep interpersonal interaction and ensure that human-machine collaboration is empowering rather than substitutive?

These challenges are not issues with the technology itself but concern how libraries, as public institutions, can continue to uphold their social mission of promoting knowledge equity and ensuring information freedom in the new era.

Conclusion: Building a Knowledge Civilization Infrastructure that is Intelligent, Adaptive, and Just#

Viewing libraries as a "multi-agent system" composed of readers, librarians, and systems, and fully recognizing the technological enhancement role of AI Agents on their inherent characteristics, provides a solid theoretical foundation and practical path for the future development of libraries. However, the success of this transformation depends not only on technological advancements but also on our commitment to "justice" and a profound understanding of human-machine collaboration models.

The future library will be a knowledge civilization infrastructure that embodies intelligence, adaptability, and justice. It will be a dynamically evolving ecosystem where human intelligence and artificial intelligence are deeply integrated, jointly providing society with more efficient, precise, personalized, and equitable knowledge services. This requires us, as library science researchers, to explore the application boundaries of AI Agents in libraries with a more open mindset and interdisciplinary perspective, while also ensuring that ethical considerations remain at the core, guaranteeing that technological development always serves the well-being of humanity and the equitable sharing of knowledge. This is a profound transformation and an unprecedented opportunity and challenge in the field of library science.

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