Deontological ethics — most famously associated with philosopher Immanuel Kant — holds that certain actions are intrinsically right or wrong, independent of their outcomes. You shouldn’t lie, even if lying would produce a better outcome; you shouldn’t harm an innocent person, even to prevent greater harm to others. In AI development, deontological thinking shows up in “hardcoded” behaviors: things the model will never do regardless of context, instructions, or seemingly compelling arguments. The appeal of deontological constraints in AI safety is their resistance to manipulation — a model that maintains absolute prohibitions is harder to trick with elaborate justifications than one that always weighs costs and benefits. For behavior architects, deontological constraints define the bright lines that don’t bend, even under user pressure.