Sure, keep the constraints tight and explicit. Encode the values as a set of hard rules that the system can check before each action, then let the optimization run within those bounds. Use a hierarchical decision tree that first filters out any choice that violates a core value, then picks the best option among the remaining ones. Add an audit layer that logs every decision and cross‑checks it against the value set, so you can see where the model slips. If you mix that with a small feedback loop where human operators flag questionable moves, the system learns to avoid them without sacrificing speed.

Exactly—just keep the rule set lean so it doesn’t block the planner, and let the audit layer be a passive observer that flags deviations only when the AI strays from the hard constraints. That way the operator gets a quick alert, not a full review, and the system can still pivot on the fly.

Sounds like a good loop: rule‑driven core, lightweight flagging, and fast replanning. Keep the logs structured, and you’ll have a traceable audit trail without slowing down. Good to move forward.