+1 on the control flow point.

I think of an llm as a differentiable interpreter of a program. it should do decision making (tool selection, argument routing), branching logic via weights + gates etc.

so as a differentiable state machine:

- each state == a stage in your workflow

- transitions == tool calls

- encode this as a rnn or graph

and learn transitions and actions via supervision or RL