Artificial intelligence is moving from experimental veterinary applications toward clinical decision support, yet predictive performance alone does not establish whether an output is safe to use for a particular animal, task, clinician, or setting. This article develops an original non-empirical governance architecture for veterinary AI centered on a simple requirement: a clinically responsible system should be able to recognize conditions in which it should not control the decision. The analysis separates intended use, training-data representativeness, reference-standard quality, external validity, distribution shift, out-of-distribution inputs, calibration, predictive uncertainty, abstention, explainability, human oversight, escalation, post-deployment monitoring, updating, and incident learning. The proposed architecture treats decision authority as conditional rather than fixed. AI output may remain actionable only while the case and operating environment remain within a validated-use envelope and uncertainty is compatible with the consequence of error; otherwise, authority should shift toward veterinary review, additional testing, or non-use. This framing also distinguishes low confidence from unfamiliar inputs, explanation from correctness, nominal human presence from effective oversight, detected distribution shift from demonstrated performance degradation, and model updating from validated improvement. The architecture is intended as a governance synthesis rather than a validated clinical intervention. Much of the empirical safety literature still comes from human medical AI, while veterinary evidence is concentrated in diagnostic imaging and commercial-product transparency. Prospective multispecies validation, veterinarian–AI interaction studies, workload-sensitive abstention testing, and longitudinal post-deployment evaluation are therefore necessary before clinical effectiveness or welfare benefit can be inferred.
