PANSLA AI

Version 7 is an evidence-driven AI routing agent built around one simple principle: no model should be treated as universally best. Each task is first classified locally by `nemotron-3-nano:4b` into one of eight categories. As soon as the category is known, the task is routed to the model already proven to perform best for that category. The system does not wait for the entire batch; classification of the next task can overlap with execution of the previous one. The routing policy is built offline by measuring each allowed model’s accuracy, token usage, latency, reasoning overhead, and output reliability. Accuracy is the first gate. Among models that pass, the most token-efficient option is selected. At runtime, this reduces model selection to a fast, deterministic lookup rather than another expensive AI decision. The result is a simple, auditable, and scalable system that combines local intelligence with specialized remote models while minimizing judged Fireworks token usage.