Enterprise AI agent platforms are now a core part of automation and productivity strategies: they let organizations assemble LLM-powered assistants, orchestrate multi-agent workflows, and expose model-driven capabilities to business users and developers. As of early 2026, the market is defined by three approaches — platform-integrated copilots, model-vendor agent frameworks, and developer-first toolsets — each addressing integration, governance, and extensibility in different ways. Microsoft 365 Copilot represents the platform-integrated approach, embedding an assistant across Word, Excel, PowerPoint, Outlook, Teams and other Microsoft apps to streamline knowledge work and analytics. Anthropic’s Claude Agents (Claude) emphasizes configurable conversational agents and safety-first guardrails for enterprise deployments. Google’s Gemini family combines multimodal models with deep Workspace and Cloud integrations to support retrieval-augmented workflows and developer APIs. Alongside these, specialist platforms and frameworks — Kore.ai and IBM watsonx Assistant for enterprise orchestration and no-code assistants; LangChain for developer-first agent construction and testing; Cohere for private, customizable LLMs and embeddings — fill gaps around governance, observability, and private model hosting. Current trends: enterprises prioritize observability, data governance, and secure retrieval pipelines; marketplaces and agent catalogs ease reuse and compliance; no-code to pro-code tool chains allow both business owners and engineers to collaborate; and in‑IDE or testing-focused agents (Windsurf, JetBrains AI Assistant, Qagent) bring agentic workflows into developer workflows and CI. Selecting between Claude Agents, Copilot, and Gemini depends less on raw model capability than on integration points, enterprise controls, developer toolchain compatibility, and marketplace/ecosystem support.