This topic covers multi‑agent AI platforms used to coordinate the complex workflows of multidisciplinary tumor boards and cancer treatment planning: ingesting imaging and pathology reports, extracting structured clinical data, running predictive and guideline‑based reasoning, generating treatment options for clinician review, and producing auditable documentation. As of 2026‑01‑30 this is timely because production‑grade LLMs, agent frameworks, and scalable model infrastructure have matured, while regulators and health systems emphasize provenance, explainability, and EHR interoperability. Key tool types and examples: agent frameworks (LangChain) give developer teams composable SDKs to build and observe LLM‑powered agents; enterprise assistants (IBM watsonx Assistant) enable no‑code and developer workflows for orchestrating multi‑agent automations; no/low‑code visual platforms (MindStudio) accelerate design, testing, and deployment of agent flows with enterprise controls; model infrastructure (Together AI) supplies training, fine‑tuning, and scalable inference for domain models; conversational LLMs (Anthropic’s Claude family) provide the reasoning and summarization layer; document‑centric tools (PDF.ai, ChatPDF) convert reports into queryable knowledge; and knowledge/documentation platforms (Notion) consolidate decisions, protocols, and meeting notes. Comparing TrustedMDT (specialized multidisciplinary tumor‑board orchestration) to these alternatives focuses less on marketing and more on clinical requirements: data provenance and lineage, integration with EHR/PACS, human‑in‑the‑loop controls, model validation and fine‑tuning, observability/traceability, and deployment scale. Organizations should evaluate platforms by how they combine agent orchestration, secure data handling, model governance, and clinical documentation workflows rather than by single‑metric performance.