Authors - Luis Anthony Hidalgo Ponce, Maricela Pinargote-Ortega Abstract - Technical support management in university environments often faces a high manual operational load due to the constant increase in digital service requests. This paper presents a multi-agent system based on Large Language Models (LLMs) designed to automate the ticket lifecycle, including classification, urgency-based prioritization, and intelligent routing. The proposed solution is built upon a modular architecture coordinated by an orchestrator agent and integrated with Retrieval-Augmented Generation (RAG) techniques to resolve frequent queries without human intervention. The system’s performance was evaluated through a controlled dataset, achieving a classification accuracy of 85.7% and a 100% effectiveness rate in user intent detection. The results demonstrate a significant reduction in response times compared to manual processes, validating the efficacy of generative artificial intelligence to optimize efficiency and user experience within university technology service desks.
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