<![CDATA[Students frequently rely on direct messaging to verify the presence of lecturers and staff on campus, a practice that often results in delayed responses due to the recipients' busy schedules. This study aims to design, implement, and evaluate an automated attendance system based on an AI agent utilizing face recognition technology and n8n as a centralized workflow automation platform. The research employs a Research and Development (R&D) approach with the Agile development method. Real-time face detection and recognition are performed from CCTV camera feeds using a Python module that integrates the InsightFace and MediaPipe algorithms. Identified attendance data is automatically stored in Google Sheets, subsequently processed by n8n to deliver information to users via a WhatsApp chatbot powered by the Gemini 2.5 Flash model. Testing conducted on 419 samples yielded an accuracy of 86.16%, with 275 True Negative values demonstrating the system's capability in filtering unregistered faces. The overall average system latency was 15.9 seconds, with a chatbot automation response time of only 9.3 seconds. This research demonstrates that the integration of workflow automation and AI agents is effective in improving the efficiency of academic attendance information access.]]>
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