This topic examines how enterprises scale generative AI by combining cloud platforms, model providers, automation frameworks and governance tooling. As organizations move from pilots to production in 2026, priorities have shifted toward orchestrating multi-agent workflows, managing data lineage and privacy, controlling model risk, and operating cost‑efficient inference and fine‑tuning at scale. Key platform roles include hyperscaler infrastructure and managed AI services (AWS/Red Hat integrations, Google Cloud Vertex AI, Snowflake’s data‑centric capabilities) and specialized model providers (e.g., Anthropic) that supply or host LLMs. Complementary tools address adjacent needs: Kore.ai and IBM watsonx Assistant support enterprise-grade virtual agents and multi‑agent orchestration across no‑code to pro‑code stacks with observability and governance; Microsoft 365 Copilot embeds assistant capabilities across productivity apps; LangChain and similar engineering frameworks provide patterns and state management for building, testing and deploying agentic apps; Together AI and similar acceleration clouds focus on efficient GPU training, fine‑tuning and low‑latency serverless inference; Monitaur and other governance platforms centralize policy, monitoring, vendor risk and validation for regulated industries; Qagent illustrates automated, goal‑based testing for agentic web applications. Enterprises should evaluate platforms across four categories—AI automation, data platforms, security/governance, and governance tooling—balancing operational controls (observability, access controls, validation), data posture (cataloguing, lineage, secure compute), and model lifecycle (fine‑tuning, evaluation, cost/latency). With increasing regulatory scrutiny and wider adoption of multi‑agent patterns, a composable stack—cloud infrastructure + model provider + orchestration/engineering frameworks + governance layer—remains the pragmatic path to scale GenAI responsibly.