This topic covers the evolution of enterprise AI agent platforms and marketplaces—focusing on Anthropic’s Claude family, OpenAI’s GPT‑Enterprise, Google’s Gemini, and data integrations with Snowflake—and how they shape agent development, deployment, and governance. By 2026, organizations are moving from isolated LLM usage to production agent architectures that combine multimodal models, retrieval from enterprise data stores, orchestration frameworks, and curated marketplaces of prebuilt agents. Key tool patterns include: conversational and developer assistants (Claude) for research, writing and analysis; multimodal, API‑driven models (Gemini) accessible via Google AI Studio and Vertex AI; enterprise‑grade OpenAI offerings (GPT‑Enterprise) focused on security, SLAs and fine‑tuning; and Snowflake integrations that provide secure vector search, Snowpark compute and governed data access for retrieval‑augmented agents. Complementary platforms and frameworks span no‑code/low‑code agent builders (StackAI), developer SDKs and orchestration libraries (LangChain), enterprise virtual agent products (IBM watsonx Assistant), and integrated workspaces with automation (Notion). Current trends reflected across these tools include tighter data‑to‑model pipelines (Snowflake→agent), multi‑model orchestration, marketplaces for reusable agents and connectors, and stronger emphasis on observability, testing and compliance. Practically, enterprises select combinations—developer frameworks for custom agents, no‑code platforms for rapid rollout, and Snowflake or other data clouds for secure context—while relying on provider features for governance, access controls, and performance. Understanding tradeoffs between control, speed to production, and data security is central when comparing platforms, agent frameworks, and marketplaces for enterprise automation.