{"id":9191,"date":"2026-07-12T19:53:32","date_gmt":"2026-07-12T11:53:32","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=9191"},"modified":"2026-07-12T20:51:23","modified_gmt":"2026-07-12T12:51:23","slug":"jase-202610-33-039","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202610-33-039","title":{"rendered":"Intelligent Agents for Enterprise Knowledge Management Construction and Application Based on Large Language Models"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 33<\/a><\/p>\n<\/div>\n\n\n\n

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Shuo Tian, Hongmei Li, and Yufei Chen<\/i><\/a><\/p>\n\n\n\n

Department of Economics, Qinhuangdao Vocational and Technical College, Qinhuangdao Hebei, 066000<\/p>\n<\/div>\n\n\n\n

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Received: March 27, 2026
Accepted: May 27, 2026
Publication Date: July 12, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Architectural of Proposed Framework of the Intelligent Agent<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202610_33.039<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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This paper presents an intelligent agent for enterprise knowledge management, combining Large Language Models (LLMs), retrieval-augmented generation (RAG), and semantic embeddings to address challenges in unstructured communication data. The system integrates preprocessing, embedding generation, retrieval, summarization, question answering, and recommendations using the Enron Email Dataset. Evaluation results show high BERTScore (0.9275) for summarization with good semantic coherence despite low lexical similarity (low ROUGEscores). Retrieval performance includes Precision @k = 0.65, Recall @k = 0.9286, and nDCG@k =0.9544, with strong contextual relevance. The model also demonstrates robust hallucination mitigation, with low hallucination rates (0.10) and high factual consistency. Scalability tests show a mean latency of 1.96 seconds and memory usage of 7.55 GB. Overall, the agent proves to be an effective solution for reducing information overload, enhancing decision-making, and preserving organizational memory<\/p>\n\n\n\n

Keywords: Enterprise Knowledge Management, Retrieval-Augmented Generation (RAG), Semantic Embeddings, Summarization, Hallucination Mitigation<\/em><\/p>\n\n\n\n

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