This topic covers multi-agent AI frameworks and platforms designed to automate and support healthcare and clinical workflows — from multidisciplinary decision support to documentation, patient triage, and contact‑center interactions. As of 2026, momentum has shifted toward production-grade orchestration: developer-first SDKs for building agent chains, no-code/low-code platforms for enterprise adoption, and secure integrations into clinician collaboration tools like Microsoft Teams. Key components include agent frameworks such as LangChain (developer SDKs and a commercial platform for building, observing, and deploying LLM-powered agents) and AutoGen-style integrations for coordinating multiple specialized agents; enterprise assistants such as IBM watsonx Assistant that enable no-code and developer-driven multi-agent orchestrations; and platforms like StackAI that lower the barrier with end-to-end governance. Supporting services focus on prompt/version management and observability (Pezzo), data curation for efficient model training (DatologyAI), document-centric Q&A for literature and records (PDF.ai), and conversational LLMs (Anthropic’s Claude family) for analysis and summarization. Notion and Observe.AI surface knowledge and real‑time conversational intelligence into workflows, while TrustedMDT and Teams-based agents represent the operational patterns that bring multi-agent outputs into clinician multidisciplinary-team workflows and meeting/chat contexts. Practical priorities in 2026 include EHR and Teams integration, traceability and governance, on‑prem or enterprise LLM deployment for PHI protection, RAG and vector-store pipelines for clinical knowledge, and observability to audit agent decisions. Together these tools and platforms form a stack for building clinically focused, governed multi-agent systems that emphasize safety, interoperability, and measurable workflow impact rather than one-off experiments.