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Optimizing Tool Selection for LLM Workflows with Differentiable Programming
How local, learnable routers can reduce token overhead, lower costs, and bring structure back to agentic workflows.
The consequence is that most agent stacks are paying GPT-4 to do what amounts to classical control flow — tool selection — with no reuse, no abstraction, and no efficiency gains at scale. This architectural separation restores clarity to the final model call — reducing hallucinations, improving determinism, and reclaiming inference capacity for core reasoning. The result is a more modular, inspectable, and scalable architecture — one that avoids paying transformer inference costs for classical programming constructs.
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