How do you design an AI agent to route meeting agendas with low latency targets?

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To design an AI agent for low-latency meeting agenda routing, the focus must be on hyper-efficient data ingress and streamlined decision pathways. Initial processing leverages Natural Language Processing (NLP) to rapidly parse agenda text, extracting key entities like topics, participants, and resource requirements. A lightweight, pre-trained classification model then quickly matches the parsed agenda to the most suitable department or individual based on established routing rules and relevance scores. To achieve low latency, the system utilizes in-memory data structures for quick lookups, optimized inference engines to minimize prediction time, and asynchronous message queues to prevent bottlenecks. Furthermore, strategic implementation of edge computing can bring processing closer to the data source, while pre-computed routing paths for common scenarios further reduce decision time. Continuous model monitoring and incremental learning from routing feedback ensure sustained performance and adaptation to new agenda patterns.