AI agents increasingly act consequentially in the real world. This creates a problem we call \emph{consequence reception}: harm occurs, the producing system is identified, yet no continuing agent receives consequences in a way that changes future behavior. Pain, understood mechanistically as a corrective feedback signal, is foundational to canonical theories of punishment -- deterrence, rehabilitation, retribution, and incapacitation all assume a continuing locus that registers the signal and updates behavior. That, in turn, requires a body for the signal to land on: a boundary whose integrity it protects, a locus where it accumulates, consolidation that converts episodic signal into durable update, and a substrate that responds by altering future action. Current LLM agents -- software-defined composites of weights, prompts, tools, memory, and credentials, freely swapped, copied, reset, and reassembled -- satisfy none of these conditions. The two prevailing legal responses therefore fail to achieve consequence reception. The thin-identity agent-principal dyad has a body but no \emph{consequence--agency coupling}: the human bears pain for behaviors beyond their control -- Elish's \emph{moral crumple zone}. The thick-identity Arbel et al.'s \emph{Algorithmic Corporation} creates legally legible entities but does not guarantee that any AI decision architecture receives pain as a behavioral signal. Achieving consequence-agency coupling is therefore a sociotechnical infrastructural problem, not only a legal one. Until such architectures exist, high-stakes AI deployment should remain tethered to accountable human principals with meaningful control, proportional liability, and authority to constrain or terminate the agent. \emph{If some body does not receive the pain by design, some body will receive it by default.}