Enterprise AI agent platforms encompass the stacks, marketplaces and frameworks used to build, deploy and operate autonomous or semi‑autonomous agents across business workflows. In 2026 this space centers on stateful agent engineering, multi‑agent orchestration, and tighter integration with developer and productivity tools. Key categories include agent frameworks (LangChain’s engineering platform and LangGraph state management), AI automation platforms and orchestration (IBM watsonx Assistant’s no‑code and developer-driven multi‑agent flows), and agent marketplaces and monetization layers (developer SDKs and platforms like GPTConsole that provide lifecycle, event chaining and memory services). Practical deployments increasingly surface inside IDEs and terminals (Windsurf’s AI‑native IDE, Warp’s Agentic Development Environment) and productivity suites (Microsoft 365 Copilot embedded across Office apps), while dedicated coding assistants (GitHub Copilot, Tabnine) emphasize latency, context retention and enterprise governance or self‑hosted options. Market signals such as platform consolidation (noted in the Deci.ai → NVIDIA transition) and growing commercial and research investment — including Meta‑backed systems contributing models and infrastructure — underscore both vendor competition and the need for interoperability. Enterprises evaluating platforms should weigh factors beyond model quality: state management, multi‑agent coordination, operational tooling (deploy/debug/evaluate), security/compliance, and options for private hosting or governance. The current landscape is pragmatic: toolchains are maturing toward production reliability while marketplaces and frameworks aim to reduce integration costs, but integration complexity and evaluation standards remain key adoption barriers.