So I was curious about how you accomplished this and took a look with the robots to figure it out.
TL;DR: the router is a massive decision tree using heuristics and regex to avoid LLM calls on unprefixed prompts.
I think this is an interesting, brute force approach to the problem, but one that will always struggle with edge cases. The other bit it will struggle with is transparency. Yes, it might be deterministic because it is a decision tree, but unless you really understand how that decision tree works under the hood and know where the pitfalls are, you're going to end up talking to the LLM a lot of the time anyhow.
Something you might want to consider is doing a fine-tune of a smol model (think something like qwen3:1.7B or even smaller like one of the gemma3n sub-1B) that will do the routing for you. You can easily build the dataset synthetically or harvest your own logs. I think this might end up covering more edge cases more smoothly without resorting to a big call to a larger model