Building a Vertical AI Agent for Library Services: Enhancing Discovery, Empowering Librarians

The Modern Academic Library

Academic libraries have evolved far beyond book repositories. Today's libraries provide:

• Access to millions of digital resources across databases and publishers

• Research consultation and information literacy instruction

• Specialized collections and archives

• Study spaces and collaborative learning environments

• Technology lending and digital scholarship support

• Institutional repository and research data services

This breadth creates both opportunity and challenge. Students and researchers often don't know what's available or how to find it. Librarians can help—but there are never enough librarians to provide individualized support to everyone who needs it.

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What a Library Agent Does

A vertical AI agent for library services extends librarian expertise to every patron interaction while freeing librarians for the complex work that requires human judgment.

Intelligent Discovery

Finding the right resources is harder than it looks. Students often:

• Don't know which databases to search

• Use keyword searches when controlled vocabulary would work better

• Miss relevant resources in adjacent disciplines

• Can't distinguish authoritative sources from questionable ones

An agent can:

Guide Search Strategy: "You're researching climate policy—have you tried GreenFile in addition to JSTOR? Here's how to structure a search that captures both scientific and policy literature."

Recommend Resources: Based on the patron's discipline, level, and research question, suggest specific databases, journals, and resource types.

Translate Queries: Convert natural language questions into effective database searches, then explain the translation so patrons learn.

Evaluate Sources: Help students understand how to assess authority, currency, and relevance—not by doing it for them, but by teaching evaluation frameworks.

Collection Intelligence

Library collections require constant curation. An agent can:

Analyze Usage Patterns: Which resources are heavily used? Which are underutilized relative to cost? Where are there gaps based on curriculum needs?

Recommend Acquisitions: Based on faculty research, course reading lists, and peer library holdings, suggest acquisitions priorities.

Identify Access Issues: Monitor for broken links, authentication problems, and access gaps. Surface issues before patrons report them.

Support Renewal Decisions: When subscription renewals come up, provide data on usage, cost-per-use, and alternatives.

Research Consultation Support

Before in-depth research consultations, the agent can:

Gather Context: What is the patron researching? What have they already tried? What level of expertise do they have?

Prepare Recommendations: Draft a starting list of resources and search strategies for the librarian to refine.

Follow Up: After consultations, check in with patrons about whether suggested resources were helpful.

24/7 Reference

Not every question needs a librarian, but every question deserves an answer:

Answer Routine Questions: Library hours, database access instructions, citation formatting, interlibrary loan procedures.

Route Complex Questions: When queries require librarian expertise, the agent captures context and connects patrons with appropriate staff.

Guide Self-Service: Walk patrons through self-service processes rather than just providing links.

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Memory Architecture

Library agents require specialized knowledge structures:

Collection Memory

Comprehensive understanding of what the library provides—databases, journals, special collections, digital archives—with details on coverage, access methods, and use cases.

Patron Memory

For each user, understanding of discipline, level (undergraduate, graduate, faculty), previous interactions, and research interests. This enables personalization while respecting privacy.

Research Methodology Memory

How do different disciplines conduct research? What sources do historians use versus chemists versus sociologists? This disciplinary knowledge shapes resource recommendations.

Institutional Context Memory

What courses are being taught? What research is happening? What are current collection priorities? This context ensures relevance.

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Platform Integrations

Library services span multiple systems:

Integrated Library System (ILS)

The catalog, circulation, and collection management foundation. The agent needs to search holdings, understand availability, and potentially initiate requests.

Discovery Layer

If your library uses a discovery layer (Primo, Summon, EDS), the agent should integrate to search across resources.

Database and Publisher Platforms

Access to licensed databases and ability to search them on patron behalf, with appropriate authentication.

Link Resolver

Understanding of how your institution routes patrons to full text through the link resolver.

Institutional Repository

Awareness of locally-produced research and ability to search and recommend repository content.

Course Reserves

Understanding of what materials are on reserve for which courses.

Interlibrary Loan

Ability to check ILL availability and initiate requests.

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Patron Experience

For library users, the agent should feel like having a knowledgeable guide always available:

Natural Conversation: "I'm writing a paper on vaccine hesitancy" should be understood without requiring database-specific syntax.

Appropriate Depth: Undergraduates need different guidance than doctoral researchers. The agent adapts.

Learning Orientation: Not just finding resources, but helping patrons understand how to find resources themselves.

Seamless Escalation: When human expertise is needed, the transition to a librarian should be smooth, with context preserved.

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Librarian Experience

For librarians, the agent should enhance professional practice:

Consultation Preparation: Before appointments, have patron context and preliminary recommendations ready.

Usage Intelligence: Understand which resources are being used and how, informing collection development.

Outreach Targeting: Identify researchers who might benefit from specific library services or new acquisitions.

Time Recovery: By handling routine questions, free time for instruction, consultation, and collection work that requires professional expertise.

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Building on the Right Foundation

Library systems contain sensitive information about patron research interests. The platform foundation matters.

Data Privacy

What patrons search, read, and check out is protected by library ethics and often by law. An AI platform must:

• Keep patron data under institutional control

• Not share search patterns with vendors or third parties

• Enable compliance with state library confidentiality laws

• Support transparent data practices

LLM Flexibility

Information retrieval and natural language understanding continue to evolve. An LLM-agnostic platform allows:

• Using specialized models for search and discovery

• Upgrading as capabilities improve

• Controlling costs appropriately

• Maintaining vendor independence

Integration Flexibility

Every library has different systems, licensed resources, and local customizations. The platform must accommodate your specific environment.

Code Ownership

When your team builds custom search interfaces, recommendation logic, or integration code, that intellectual property should belong to your institution.

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Implementation Approach

Library agent implementation should enhance existing services:

Phase 1: Reference Support

Deploy an agent that handles routine reference questions—hours, access, basic how-to. This demonstrates value and builds patron comfort.

Phase 2: Discovery Enhancement

Integrate with discovery systems to provide search assistance and resource recommendations.

Phase 3: Collection Intelligence

Use agent analysis capabilities to inform collection development decisions.

Phase 4: Research Partnership

Extend to proactive research support, identifying researchers who might benefit from library services and resources.

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Working Together

Effective implementation pairs platform capability with library expertise:

Forward-deployed engineers who understand both technology and library science, working alongside librarians.

Librarian involvement in defining how the agent should interact with patrons and what library values it should embody.

Iterative development that starts with specific services and expands based on feedback.

Privacy review at each stage to ensure patron confidentiality.

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The Opportunity

Libraries have always been about connecting people with information. AI agents extend that mission—enabling personalized discovery assistance at any hour, for any patron, without replacing the expertise and judgment that professional librarians provide.

The libraries that develop these capabilities will better serve their communities. The key is building on foundations that respect library values and keep the institution in control.

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*Universities exploring library AI should prioritize platforms that protect patron privacy, integrate with diverse library systems, and provide implementation partnerships that understand library culture. The goal is to extend librarian expertise—not to replace the human connection that makes library service valuable.*