Enterprise AI agents and orchestration platforms coordinate large language models, tool integrations, and business data to automate workflows, augment knowledge work, and run customer-facing assistants. This topic covers the platforms, marketplaces, and frameworks that organizations use to build, deploy, monitor and govern multi-agent systems—from data-proximate agents to voice-first contact center bots and low-code automation flows. By 2026 the focus has shifted from point LLM performance to orchestration: enterprises prioritize data residency, latency, access controls, and composability. Examples include Snowflake paired with Anthropic’s Claude agents to keep reasoning close to governed data, emerging orchestration offerings such as OpenAI’s Sora-style approaches for coordinating model calls and tool use, and cloud-native stacks like Unicorne on AWS that emphasize integration with cloud infrastructure. Key categories and tools: Claude (Anthropic) provides conversational and developer assistants for research, writing, code and analysis; IBM watsonx Assistant enables no-code and developer-driven enterprise assistants and multi-agent flows; Observe.AI and PolyAI focus on voice and contact-center intelligence; Yellow.ai targets CX/EX automation across channels; Google Gemini supplies multimodal models and APIs; Together AI offers scalable training, fine-tuning and inference infrastructure. Practical decision factors include integration with enterprise data platforms, security and compliance, latency and inference economics, marketplace access to prebuilt agents and connectors, and low-code orchestration for non-developers. The market is maturing toward modular agent frameworks and agent marketplaces that make it easier to compose specialized skills (voice, retrieval, RAG, task automation) while maintaining governance and observability—critical considerations for production deployments in 2026.