This topic covers self‑hosted AI sandboxes and agent platforms that enterprises use to develop, evaluate and run agentic applications while keeping data, models and telemetry on premises or in trusted clouds. By 2026, increased regulatory scrutiny, data‑sovereignty requirements, cost pressure and the need for reproducible model behaviour have pushed organizations toward local‑first and edge deployments, stronger observability and lifecycle controls for agentic systems. Key categories include agent frameworks (for building and testing agents), AI tool marketplaces (for packaging and distributing components), AI data platforms (for observability and memory), decentralized AI infrastructure (for edge and multi‑party hosting), and AI automation platforms (for orchestration and governance). Representative tools: LangChain (open‑source engineering frameworks and LangGraph for stateful agent construction and testing); Xilos (enterprise infrastructure emphasizing 100% visibility into connected services and agent activity); Kore.ai (enterprise multi‑agent orchestration with governance and observability); Tabby and Cursor (developer‑centric, self‑hosted coding assistants and AI‑first editors that embed agents across IDE, CLI and web workflows); Stable Code (compact, edge‑ready code LLMs tuned for private, fast code completion); GPTConsole (SDK, API, CLI and web tooling plus event chaining and memory for production agent lifecycle and monetization). Enterprises choosing self‑hosted sandboxes seek repeatable evaluation, audit trails, toolchain modularity and reduced vendor lock‑in. Current trends emphasize instruction‑tuned smaller models at the edge, robust observability and governance for multi‑agent flows, and marketplaces/SDKs that accelerate safe production deployment without relinquishing control.