The healthcare industry has reached an inflection point with agentic artificial intelligence, where delayed adoption can mean delayed care, compromised privacy, and erosion of clinical trust. Agentic AI operates across five interdependent layers: power requirement, infrastructure/hardware, network, data, and application. Responsible use requires understanding where each layer is vulnerable. While most conversations focus on the top three layers—network, data, and application—irresponsible use doesn’t require bad intent. For example, an LLM interpreting a patient’s pain rating of 10 on a 10-point scale as “severe pain” and writing it into a clinical summary, when the physician never used that word, can misrepresent clinical reality and expose organisations to legal risk. Responsible use requires three things: bias mitigation, observability, and a third unspecified element. Healthcare organisations must actively audit for bias across age, race, gender, and other factors, not as a compliance exercise but as a clinical imperative. Observability means every person whose work is touched by an AI agent needs visibility into what that agent is designed to do and how it’s performing.