This topic covers the tools and patterns enterprises use to fine‑tune, deploy, and orchestrate large language models (LLMs) and multi‑agent workflows—often combining managed models from Anthropic and OpenAI with enterprise data platforms like Snowflake. With demand for private, task‑specific models, teams are balancing fine‑tuning, retrieval‑augmented generation, and prompt/instruction tuning while maintaining governance, observability, and cost control. Key tool categories include AI Automation Platforms and Agent Frameworks (LangChain, Kore.ai, StackAI, Adept) for building multi‑step agents and orchestrations; AI Data Platforms (Snowflake) for secure data access, feature engineering, and embeddings storage; LLM infrastructure and fine‑tuning providers (Together AI, Cohere, Anthropic, OpenAI) for training, serverless inference, and model customization; developer tooling and in‑IDE assistants (Tabby, Windsurf/Codeium, JetBrains AI Assistant) for engineering productivity; and GenAI Test Automation (Qagent) to validate agent behavior and regression test end‑to‑end workflows. Key trends as of 2026 include multi‑model strategies (mixing Anthropic/OpenAI and specialized open models), tighter Snowflake integrations for embedding pipelines and data governance, and growth in no‑code/low‑code platforms that let business teams compose agents with enterprise controls. Observability, role‑based governance, data residency, and automated testing have become critical for production safety and compliance. Choosing tools depends on priorities: extensible SDKs and observability (LangChain, Kore.ai), turnkey enterprise assistants (IBM watsonx Assistant, Microsoft 365 Copilot), scalable fine‑tuning/inference (Together AI, Cohere), and testing/orchestration to ensure reliable multi‑agent automation (StackAI, Qagent, Adept). Together these components form the stack enterprises use to operationalize LLMs responsibly and at scale.