Graph workflows are useless if there are edges without cost or effor attribues and termination states without reward. Such workflow can be vibecoded in one-shot anyone who knows about graphs.

I thought some time ago about expressing SDLC as combination of harness tools, finite state machines with weights (efforts) on edges and rewards on some states. The goal - model queue of tasks as sub-workflows and implement the most valuable. I tried to design approach which will allow to maximize reward with minimal costs and risks via Markov Decision Process. Hypotetically i can implement it. But it all ends up being really hard to wrap your head around because of the state machines, custom runtime for scripts, the agent loop control, supervisor's control mechanisms. Then you have to tune the weighting models. The model can be similar to models from reinforcement learing. Even if I pull it off, I'd have to be damn lucky for even one other person to figure it out. And the project will have from 3 to 5 stars on GitHub because almost nobody will understand what is it and how to use.