Abstract

Abstract
The massive industrialization of products in a factory environment requires testing the product at a stage before its exportation to the sales market. For example, the end-of-line tests at Continental Advanced Antenna contribute to the validation of an antenna's functionality, a product manufactured by this organization. In addition, the storage of information from the testing process allows the data manipulation through automated machine learning algorithms in search of a beneficial contribution. Studies in this area (automatic learning/machine learning) lead to the search and development of tools designed with objectives such as preventing anomalies in the production line, predictive maintenance, product quality assurance, forecast demand, forecasting safety problems, increasing resources, proactive maintenance, resource scalability, reduced production time, and anomaly detection, isolation, and correction. Once applied to the manufacturing environment, these advantages make the EOL system more productive, reliable, and less time-consuming. This way, a tool is proposed that allows the visualization and previous detection of trends associated with faults in the antenna testing system. Furthermore, it focuses on predicting failures at Continental's EOL.

Abstract
This study examines the potential applications of large language models (LLMs) and intelligent agents in educational environments, with a particular focus on their role in enhancing the quality of teaching and learning processes. It provides a comprehensive overview of LLMs, emphasizing their capabilities in natural language analysis and generation. Furthermore, the study examines the potential for collaboration between LLMs and intelligent agents. While LLMs offer a foundation for AI capabilities, intelligent agents utilize these technologies to perform autonomous and context-aware actions within educational systems. A comparative analysis of various intelligent agent platforms, including Autogen, Langra, Crew AI, LM Studio, and Olama, constitutes a central component of this research. This study addresses the criteria that informed the selection of Crew AI for a case study, with a particular focus on its adaptability, ease of integration, and task execution capabilities in comparison to the other platforms. The research includes an analysis of the platform's performance in a controlled educational environment, highlighting the advantages of Crew AI in system functionality. These results demonstrate the necessity for a strategic and well-structured approach to integrating LLMs and intelligent agents, as their successful implementation can foster new competencies, enhance stakeholder engagement, and offer innovative teaching and learning experiences.

Abstract
Higher Education Institutions (HEIs) face increasing pressure to transform fragmented information environments into cohesive, data-driven ecosystems that support strategic and operational decision-making. This study proposes a comprehensive framework for implementing Business Intelligence (BI) in HEIs, evolving from a validated roadmap to an integrated ecosystem perspective. Grounded in the Design Science Research methodology, the work combines a systematic literature review, the design of a flexible BI architecture, and an in-depth case study at the University of Tr & aacute;s-os-Montes and Alto Douro (UTAD). The framework addresses critical factors such as strategic alignment, data governance, and system interoperability, and demonstrates how dashboards and analytics can enhance institutional intelligence and evidence-based management. Results from the UTAD case confirm the framework's capacity to overcome technical and organisational barriers, enabling the transition from isolated systems to intelligent, interconnected data infrastructures. This research contributes to the literature by bridging theoretical guidelines and practical implementation, providing a scalable reference model to guide BI-driven digital transformation in higher education. It also demonstrates the tangible institutional value of integrated BI ecosystems in supporting more informed, timely, and efficient decision-making.