As AI tasks become increasingly varied in complexity, routing every request to a single flagship model creates massive inefficiencies. This project introduces a dynamic, context-aware routing system designed to evaluate incoming prompts and distribute workloads across a tiered network of models. By analyzing the specific requirements of each request before execution, the system ensures that lightweight tasks are handled with maximum cost-efficiency, while complex logic and reasoning challenges are directed to specialized, high-performance models. Built with a focus on fault-tolerance and asynchronous batch processing, the architecture gracefully handles API failures and edge cases. The result is a robust, highly resilient agent that maximizes overall accuracy while strictly minimizing unnecessary token spend.
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