Artificial intelligence is increasingly embedded within medication-related decision support, prescription review, alerting, prioritization, pharmacovigilance, and operational workflows. Deployment, however, does not stabilize the data environment, clinical task, professional response, patient population, organizational context, or relationship between model output and medication decisions. Postdeployment governance must therefore extend beyond periodic technical performance checks. This article develops a proposed postdeployment-governance architecture for artificial intelligence used within medication-use systems. The architecture defines the governed object as the complete AI-enabled arrangement, including its model, data pipelines, interfaces, users, workflow position, organizational policies, external dependencies, and intended-use boundaries. It connects versioned baseline evidence with multidomain surveillance of performance, drift, equity, human–AI interaction, workflow consequences, medication incidents, pharmacovigilance signals, and patient experience. Detected signals enter structured triage and investigation before proportionate decisions concerning continued use, intensified monitoring, workflow correction, model modification, revalidation, restriction, suspension, rollback, or withdrawal. Accountability functions assign responsibility for evidence review, decision authority, communication, alternative workflow activation, and learning closure. Validation would require longitudinal and multisite assessment of technical, clinical, sociotechnical, safety, equity, organizational, and patient-relevant outcomes. The architecture does not establish universal thresholds, causal attribution rules, regulatory acceptability, or improved medication outcomes. Its original contribution is an integrated and testable governance structure that treats deployment as the beginning of continuing institutional responsibility rather than the endpoint of model development.