<![CDATA[The rapid advancement of Artificial Intelligence (AI), particularly Large Language Models (LLMs), has accelerated the adoption of AI Agents in various information service systems. However, the quality of responses generated by AI Agents depends not only on language models but also on the structure and management of the underlying knowledge source. Most previous studies have focused on AI Agent implementation and API integration, while limited attention has been given to the design of relational knowledge base databases. This study aims to design a relational knowledge base database that supports AI Agents in information service systems. The research adopts the Database Life Cycle (DBLC) methodology, including data requirement analysis, entity identification, conceptual database design, normalization, logical database design, physical database implementation, and database testing. The proposed database consists of several core entities, including information categories, knowledge repository, metadata, users, administrators, conversation history, user feedback, and AI processing logs. The resulting schema satisfies Third Normal Form (3NF), reducing data redundancy while improving consistency, scalability, and maintainability. Furthermore, the proposed database supports AI Agent integration by providing structured retrieval mechanisms for contextual information before being processed by Large Language Models. The proposed relational database architecture contributes as a scalable foundation for developing AI Agent-based information service systems across various application domains.]]>
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