The hardest part of applied reinforcement learning is often not the algorithm.
The harder first step is creating an environment that behaves enough like reality to teach useful behavior. If the environment is shallow, inconsistent, or disconnected from real constraints, the agent learns from a toy world instead of a useful one.
Cutting Table RL is intentionally starting with simulator state, job models, machine lifecycle, and believable structured data before adding agents. The current simulator includes sample jobs, a job queue, a state summary, a wait action, and a load_next_job action.
That may sound small, but it is the correct foundation. The system first needs to understand what a job is, what machine state means, how time advances, and how work enters the machine.
Agents and reinforcement learning will come later, after the simulator has meaningful behavior to act on and consequences worth measuring.
The practical lesson is simple: build the world first, then teach agents inside it.