Instead of asking, “Which doctor should I see?”, the system helps a person navigate preventive health information over time. The agent retrieves trustworthy evidence, considers the user's stated goals/context, identifies relevant preventive-care information, explains the evidence, and directs the user toward appropriate professional care rather than attempting diagnosis.

Preventive healthcare increasingly begins outside the clinic, where individuals search for information about screenings, wellness, risk factors, and when to seek professional care. Generative AI can personalize these interactions, but unsupported recommendations and unclear evidence provenance create significant trust challenges.
This session presents an evidence-grounded agentic AI approach for personalized preventive health navigation. Specialized agents decompose health and wellness questions, retrieve authoritative biomedical evidence, rank sources by relevance and authority, and generate transparent responses linked to their supporting evidence. The framework emphasizes prevention, education, appropriate escalation to professional care, and preservation of human decision-making rather than autonomous diagnosis.
Using practical preventive-health scenarios, the session demonstrates how agentic AI can move beyond one-time generative answers toward longitudinal, evidence-aware health navigation while maintaining provenance, uncertainty, and safety boundaries.