The aspiration toward ``self-healing'' AI---systems that detect and correct their own failures without human intervention---is now central to research on autonomous agents. We argue that this aspiration, as standardly conceived, is unrealizable. An agent that self-heals must assess its own performance against some benchmark, but assessment is itself a task that can fail. If self-healing extends to assessment failures, the requirement iterates without limit. The resulting regress forces a choice,…
Read moreThe aspiration toward ``self-healing'' AI---systems that detect and correct their own failures without human intervention---is now central to research on autonomous agents. We argue that this aspiration, as standardly conceived, is unrealizable. An agent that self-heals must assess its own performance against some benchmark, but assessment is itself a task that can fail. If self-healing extends to assessment failures, the requirement iterates without limit. The resulting regress forces a choice, since any system described as self-healing must rely on some external anchor, primitive foundation, or tolerance threshold that the system itself cannot certify. This regress is more than a conceptual puzzle, since the autonomy promised by self-healing rhetoric is, in every implemented system, quietly bounded by a stopper that the system does not control. We identify where these stoppers live in prominent agent architectures, show that the field's evaluative benchmarks systematically obscure them, and argue that the vocabulary of autonomous self-correction should be replaced by explicit, stopper-relative claims. In doing so, we aim to bridge two audiences by introducing philosophers to the technical architecture of agentic AI and offering AI researchers epistemological tools for refining the concepts that guide system design. The result is a constraint on both the design and the evaluation of agentic AI, because no finite system can be self-healing in an unqualified sense.
