Enterprises adopting generative AI and agentic systems now require governed AI platforms that combine policy orchestration, monitoring, vendor oversight, and technical controls. This topic covers commercial offerings and infrastructure that help organizations operationalize model risk management, privacy and security controls, explainability, and auditability to meet internal standards and regulatory obligations (e.g., EU AI Act, NIST guidance) that have matured since mid‑2020s. Key categories include AI Governance Tools (policy libraries, approval workflows, explainability and testing), AI Security Governance (access controls, data lineage, runtime monitoring and anomaly detection), and Regulatory Compliance Tools (evidence collection, vendor assessments, reporting). Representative solutions span infrastructure and purpose-built platforms: IBM watsonx Assistant and Microsoft 365 Copilot provide enterprise‑grade assistants that need embedding into governance pipelines; Claude, Mistral AI and Vertex AI supply models and managed model platforms where controls, fine‑tuning and private deployment matter; StackAI, Tektonic AI and Xilos position themselves around no‑/low‑code agent orchestration, hybrid symbolic/neural workflows, and visibility into agent activity; Monitaur focuses on insurance and other regulated verticals with policy, monitoring and vendor governance features. Practical governance now links model lifecycle controls (training/fine‑tuning, testing, deployment), continuous monitoring (performance drift, safety incidents), and vendor management (model provenance, SLAs, audit artifacts). Platforms are converging on integrations with MLOps, enterprise IAM, and compliance reporting to reduce friction between developers, risk and compliance teams. Choosing the right governed AI stack requires balancing control surface (sandboxing, explainability), operational scale, and alignment with regulatory and industry requirements.