Agentic AI for healthcare refers to autonomous, multi-step AI agents that perform tasks such as triage, scheduling, care coordination, and administrative automation across clinical systems. As of 2026-04-02, mainstream deployments and regulatory attention have made comparisons between vendor approaches especially relevant: Amazon’s One Medical integration is a high-profile example of a major tech company embedding agentic assistants into a primary care stack, while enterprise and specialist vendors offer distinct architectural and operational choices. Key categories and tools to watch include enterprise virtual-assistant frameworks (IBM watsonx Assistant) for no-code and developer-driven multi-agent orchestrations; infrastructure-focused platforms (Xilos) that emphasize observability and service-level visibility for agent activity; omnichannel CX/EX automation platforms (Yellow.ai) that support patient-facing chat and voice across channels; no-code/low-code end-to-end platforms (StackAI) for rapid build, deploy, and governance of agents; and hybrid managed-service models (Crescendo.ai) that combine agentic AI with human experts for outcome guarantees. Decisions between these approaches hinge on clinical safety, HIPAA/FHIR interoperability, explainability, auditability, human-in-the-loop controls, and procurement model (platform vs managed service). Trends through 2026 show growing demand for multi-agent orchestration, stronger observability and governance, and hybrid models that pair automation with human oversight to limit clinical risk. This topic helps health system leaders and vendors weigh trade-offs between scalability, safety, and operational complexity when adopting agentic AI in care delivery.