This topic compares two converging classes of healthcare AI assistants: Copilot-style personal/productivity assistants and agentic, autonomous AI agents used for clinical workflows, patient engagement and contact-center automation. Copilot models (exemplified by Microsoft 365 Copilot) are integrated across productivity apps to surface insights, speed documentation and embed AI into clinician and administrative workflows. Agentic platforms (Kore.ai, Yellow.ai) and specialist vendors (PolyAI, Observe.AI) focus on orchestrating multi-agent workflows, omnichannel conversational agents, voice-first contact-center automation, real-time agent assist and post-interaction QA. Disease-specific assistants such as iCliniq Tes illustrate narrow, data-driven coaching and monitoring use cases (for example, diabetes management) that combine device telemetry with clinical guidance. Relevance in 2026: health systems face persistent clinician burnout, growing demand for remote care and cost pressures that drive adoption of assistants for documentation, triage, coaching and customer service. At the same time, regulatory scrutiny, data governance and clinical validation requirements make platform capabilities—interoperability with EHRs, observability, model governance, audit trails, and no-code/pro-code customization—critical selection criteria. Trends include multi-agent orchestration to automate handoffs (clinical→operational), voice and multilingual support for contact centers, and hybrid Copilot experiences that augment rather than replace clinicians. When comparing options, evaluate intended role (productivity vs autonomous agent), integration depth, clinical validation and safety controls, privacy and compliance, and operational observability. This framing helps health leaders assess where Copilot-style assistants vs agentic AI are most appropriate across personal AI, health coaching, clinical documentation and customer-service scenarios.