JMIR Publications

ABSTRACT

Digital therapeutics are digital technology-based interventions designed to treat, manage, or prevent medical or behavioral conditions. Early products delivered fixed educational, behavioral, or monitoring content. Newer products increasingly resemble adaptive software ecosystems that incorporate large language models, multimodal real-time data, memory, retrieval, tool use, and multi-agent coordination. Such systems change in two distinct ways. The first is planned: a team updates the model, data, configuration, or permissions. Regulatory mechanisms such as predetermined change control plans govern these updates. The second is not planned. An agentic system can act differently at runtime with nothing newly deployed, because retrieval surfaces different context, memory accumulates, or agents select different tools. These changes cannot be enumerated in advance, and neither clinical stage models nor software governance frameworks specify how to develop, evaluate, or retire products that exhibit unplanned behavior. We propose an Agile Stage Model for minimally viable development and governance of digital therapeutics, including those built on agentic artificial intelligence. The model draws on the NIH Stage Model, software-as-a-medical device principles, Agile Science and its derivatives, the artificial intelligence agent literature, intelligent delegation, and recent work on agentic artificial intelligence for executing clinical trials. It extends the Agile Analytic Engine, an architecture that connects population evidence to a local treatment-to-target decision contract specifying target, baseline, action, reassessment interval, and response rule, and then to a governed chain of minimally viable experiments. The model comprises six stages: minimum viable specification (Stage 0), minimum viable product and internal validation (Stage 1), first-in-human sensor-action testing (Stage 2), local real-world minimally viable experiment (Stage 3), population effectiveness and cross-site learning (Stage 4), and lifecycle stewardship and deimplementation (Stage 5). The stages are learning gates rather than a fixed sequence, and teams may advance, repeat, revise, pause, or stop. At each stage the team asks a single question: what is the smallest valid experiment that can inform the next decision? For agentic systems, every stage adds explicit requirements for bounded authority, human oversight, permissioning, verification, and monitoring that separates degradation in performance from change in system behavior. One rule governs the model: standardize only what protects patient safety and the integrity of evidence and let everything else adapt to context. Oversight should scale with the severity, irreversibility, and reach of potential harm rather than being uniform across products. The model is conceptual and requires prospective evaluation.