Designing an AI agent to route meeting agendas with limited context requires a pragmatic approach focusing on structured input and user interaction. Initially, the agent would leverage user-defined keywords or template selection to categorize the meeting type or core topics. Furthermore, it could employ lightweight topic modeling or keyword extraction from the agenda items themselves to infer purpose. Given the context limitations, establishing predefined routing rules based on common agenda patterns or participant roles is crucial for initial suggestions. A critical component involves a feedback mechanism, allowing users to correct or refine the suggested routing, thereby incrementally improving the agent's accuracy over time. This iterative process, combining explicit user input with simple classification, enables the agent to adapt and provide increasingly relevant routing despite an initial lack of deep organizational knowledge. More details: https://www.khonphutorn.com/go.php?https://infoguide.com.ua/