Clinicians and care teams must navigate growing volumes of biomedical literature, provider information, and other healthcare data while making time-sensitive decisions. Generative AI can accelerate information synthesis, but conventional approaches can obscure where information came from and whether citations actually support the response.
This session presents a multi-agent AI architecture that separates query planning, healthcare retrieval, evidence ranking, and grounded response generation. Through a working healthcare AI prototype, the session demonstrates how specialized agents can retrieve provider and biomedical evidence, preserve provenance, rank sources, and synthesize information while maintaining human oversight. The approach explores how agentic AI can assist healthcare professionals without turning an LLM into an autonomous clinical decision-maker.