AI agents are making significant strides in healthcare by handling tasks that are too complex for traditional automation and too time-consuming for continuous human attention, such as administrative roles that require dealing with numerous variables and exceptions. Despite their potential, the deployment of AI in healthcare faces substantial challenges, including engineering, regulatory, and ethical issues. Complex tasks often require collaborative efforts from multiple specialized agents, which raises questions about optimal design and interaction. Further complicating matters are the vast and intricate healthcare systems, characterized by obscure medical jargon, fragmented software landscapes, and interoperability issues, which make seamless integration difficult. While advances like the Fast Healthcare Interoperability Resources (FHIR) framework and improved LLM function calling offer hope for better integration, the reality is that the deployment of AI agents in healthcare requires not only technical expertise but also a deep understanding of healthcare workflows, legacy systems, and compliance norms.