AI Health Agents & Clinical Assistant Platforms covers the class of systems that use large language and multimodal models, agentic automation, and no-code/low-code tooling to assist clinicians, care teams, and patients. These platforms power conversational triage, virtual clinical assistants, EHR-integrated workflow automation, and personalized health coaching. As of 2026-04-01, adoption is shaped by several converging trends: more capable multimodal models (e.g., Google Gemini) for text, image and signal understanding; conversationally focused assistants (Anthropic’s Claude family) for synthesis and documentation; enterprise-grade assistant frameworks (IBM watsonx Assistant) that pair no-code builders with developer controls; no-code/low-code agent orchestration and governance platforms (StackAI) for deployment and oversight; and agentic systems (Adept/ACT-1) that can operate inside software interfaces to automate multi-step clinical workflows. Relevance and timeliness: clinical deployments increasingly prioritize integration with electronic health records, workflow automation, and tightly scoped human-in-the-loop safety nets, while regulatory scrutiny, data-protection requirements, and clinical validation demands have intensified. Effective solutions therefore combine model capability with role-based governance, auditability, and EHR interoperability. Key use cases include documentation support, automated prior authorization, patient messaging and coaching, remote monitoring summaries, and developer/clinical co-design through no-code builders. Risks and operational considerations—privacy, bias, clinical validation, and change management—remain central to procurement and deployment decisions. This topic synthesizes current tool categories and practical constraints so health systems, vendors, and evaluators can compare architectures, functional roles, and governance approaches when choosing or building clinical AI assistants and personal AI health coaches.