This topic examines the leading enterprise platforms and ecosystems for large language models (LLMs) and AI agents as of early 2026, focusing on operational fit, agent orchestration, marketplaces, and governance. Enterprises now choose between managed LLM clouds (Claude Cloud, Azure OpenAI, Google Vertex AI, AWS Bedrock, Snowflake‑Anthropic) that combine model access, data connectivity, and compliance controls, and a growing set of agent frameworks and marketplaces that simplify multi-agent workflows. Key trends include multimodal model APIs, tighter data-platform integrations, on‑demand fine‑tuning and retrieval-augmented retrieval, and acceleration layers for low-latency inference. Tool categories and representative capabilities: managed LLM platforms—Claude (Anthropic) and cloud providers’ offerings provide hosted conversational and developer assistants for research, code, and analysis; Vertex AI, Azure OpenAI and Bedrock provide model catalogs, deployment, and MLOps integrations; Snowflake‑Anthropic highlights data-platform model access patterns. Agent frameworks and marketplaces—LangChain and commercial derivatives standardize agent building blocks and observability; MindStudio offers no‑code/low‑code visual agent design for business users; agent marketplaces enable discovery and reuse of prebuilt skills. Infrastructure and acceleration—Together AI and similar clouds focus on efficient training, fine‑tuning and serverless inference for custom or open models. Security and governance are central: enterprises require audit trails, access controls, model provenance, evaluation pipelines, and controls for data residency and hallucination mitigation. Evaluating platforms in 2026 means balancing developer flexibility, integration with enterprise data, operational costs, and the maturity of agent orchestration and governance features rather than raw model performance alone.