Enterprise AI agents platforms bring together large language models, orchestration layers, connectors and developer tooling to run multi-step, goal-directed workflows on behalf of users. As of 2025-12-10 this space centers on production concerns — secure access to enterprise data (notably Snowflake integrations), observability, governance, multi-model support, and developer ergonomics — rather than purely experimental demos. Key platform types include AI automation platforms and no-code builders (IBM watsonx Assistant, Microsoft 365 Copilot, Anakin.ai), engineering frameworks for agent development and testing (LangChain, LangGraph), agent-focused IDEs and runtime platforms (Windsurf, GPTConsole, AutoGPT), and domain-specialized agent solutions (Observe.AI for contact centers, Tabnine for code assistance). Major model families — Anthropic’s Claude, Google Gemini and OpenAI models via operator-style tooling — are commonly used as underlying copilots or reasoning engines, with operators and connectors enabling deployment, scaling and data governance. Marketplaces for agents and tools are emerging to package reusable skills, chains and adapters, while enterprise integrations (Snowflake, Microsoft 365, CRM and contact-center systems) let agents perform retrieval-augmented reasoning on governed data. Current trends emphasize production readiness: lifecycle management, evaluation and safety tooling, private or hybrid hosting, and role-based access to data and capabilities. For IT and product teams evaluating this category, the choice narrows to orchestration and integration needs (connectors, observability, governance), developer experience (SDKs, debugging and testing), and the model/runtime mix required for domain tasks.