AI agents as the potential next operational layer in reproductive medicine

Background Artificial intelligence in reproductive medicine has so far been the domain of targeted, task-specific systems – tools for embryo assessment, sperm analysis, ovarian stimulation support, and outcome prediction. A separate and fast-growing healthcare literature now distinguishes these conventional tools from agentic systems, which retain context, plan, chain actions together, draw on external tools, and coordinate multi-step work under human supervision. Objective This research aims to determine whether agentic AI constitutes the next operational layer in reproductive medicine, distinct from earlier predictive applications, and to outline its plausible near-term role, its main risks, and the research questions it raises. Approach This article presents a critical perspective and narrative synthesis drawn from the published reproductive medicine and healthcare literature. It addresses how the agentic AI concept should be defined, how the evidence should be read, how plausible translation actually is, and how such systems should be governed – without pooled quantitative estimates. Key Observations Reproductive medicine is an unusually informative medical test case. IVF care is longitudinal and data-rich; it draws on several disciplines; it depends heavily on communication; and it is operationally fragmented. The most plausible near-term role for AI agents is supervised orchestration across four domains: evidence generation and scientific publishing, clinical workflow coordination, patient-facing communication, and bedside decision support – possibly organized as multimodal, multi-agent systems. So far, though, almost no evidence specific to reproductive medicine shows that agentic systems improve live birth, cumulative live birth, safety, or patient-centered outcomes. The wider healthcare evidence is still mostly conceptual work, simulations, and early pilots. Conclusions For now, AI agents are best treated as supervised collaborators rather than autonomous clinical actors. Adopting them responsibly will require clear definitions, prospective workflow studies using clinically meaningful endpoints, close attention to human factors and operational realities, transparent governance, and a shared, layered framework for validation.