Multi‑agent AI platforms for healthcare assemble specialized agents—triage, scheduling, clinical documentation, and analytics—into coordinated workflows that support clinical planning and operational decisions. This topic covers marketplaces and frameworks for composing agents, no‑code/low‑code design tools for rapid deployment, clinical documentation and decision‑support integrations, and data platforms that prepare and govern training data for clinical models. Demand for multi‑agent solutions has grown as health systems seek to automate routine tasks (discharge planning, care coordination, documentation) while preserving auditability, interoperability with EHRs, and regulatory compliance. Enterprise offerings (IBM watsonx Assistant, Kore.ai) emphasize multi‑agent orchestration plus governance and observability; developer frameworks (LangChain) provide SDKs and testing workflows for building, chaining, and deploying reliable agents; visual platforms (MindStudio) enable clinicians and analysts to design and iterate agent workflows with less engineering overhead; and AI data platforms (DatologyAI) focus on converting raw clinical data into model‑ready, curated datasets that reduce size and improve quality for domain models. Key practical considerations include EHR integration, role‑based access and logging, validation and continuous monitoring of agent outputs, and data curation to limit hallucination and bias. Successful deployments blend developer tools for custom logic, no‑code tools for operational agility, and robust data pipelines to train and retrain focused models. As of early 2026, organizations evaluating these platforms should prioritize traceability, interoperability, and measurable clinical safety controls over feature count—choosing combinations of agent frameworks, documentation tools, marketplaces, and data platforms that together meet clinical governance and workflow needs.