This topic examines how enterprises assemble production-grade AI agents by pairing data platforms (notably Snowflake) with large-model providers (Anthropic/Claude vs OpenAI/Gemini) and the surrounding ecosystem of agent frameworks, marketplaces, and developer tools. The emphasis is on integrating secure, governed access to enterprise data with multi-model orchestration, retrieval-augmented generation (RAG), and observability to run agentic applications at scale. Relevance in early 2026 stems from two concurrent shifts: model providers and APIs have matured into interoperable building blocks, and enterprises increasingly demand tight data-to-model integration, governance, and cost control. That drives interest in Snowflake-hosted vectors and queryable data plus hosted model endpoints (Claude, OpenAI, Gemini) and in complementary tooling that handles prompt/version management, testing, and deployment. Key tools and roles: LangChain — open-source SDK and commercial platform for building, testing, and deploying agent workflows; LlamaIndex — focused on turning unstructured content into RAG-enabled document agents; Pezzo — centralized prompt/version control, observability and one-click deployments for production features; Cursor and Windsurf — AI-first editors/IDEs embedding agents into developer workflows; Tabby — self-hosted coding assistant option for local or hybrid deployments; Qodo — quality-first code review, test generation, and SDLC governance. Together these tools form the developer and operational layer that connects Snowflake-hosted data with model providers. Choosing between Snowflake+Claude and OpenAI/Gemini integrations requires weighing safety profiles, latency, cost, governance needs, and multi-model orchestration. The practical trend is hybrid stacks: standardized agent frameworks (LangChain/LlamaIndex) plus platform services (Pezzo, observability, IDE integrations) to deliver reproducible, auditable agent behavior across model vendors and data platforms.