Healthcare staff shortages increase workloads and create a need for assistive robots capable of handling routine hospital tasks. Traditional rule-based systems struggle in dynamic environments and with unforeseen requests. We present an agile hospital assistive robot that interprets natural language instructions using an AI-based task planner and keyword retrieval to generate executable task sequences. The robot adapts in real time to additional user requests, reschedules tasks using a suboptimal genetic-algorithm-based approach, and recovers from execution failures with vision-language reasoning and AI suggestions. Deployed on a Temi robot with a custom Android application, the system demonstrates effective handling of dynamic changes, improved human-robot interaction efficiency, and positive feedback from nursing staff.