Enterprise AI faces a growing infrastructure challenge as companies deploy more AI agents without adequate governance systems. Gartner predicts a significant increase in AI agents per company, while most organizations feel unprepared for this growth. This gap has created a demand for infrastructure that manages agents across different models and platforms. Xpander.ai, founded by former AWS engineers, aims to fill this need with its vendor-neutral AI agent platform. The platform provides a control plane for building, running, and governing agents, addressing concerns about centralized governance, isolated workflows, and vendor lock-in. Xpander's Universal Harness offers a model- and framework-agnostic runtime for executing agents as portable workloads across various environments and models. Competitors like LangChain, CrewAI, and Temporal offer similar functionalities, while major cloud providers are also expanding into this infrastructure layer. Xpander differentiates itself by treating agent frameworks as replaceable components. The company argues that building this operational infrastructure internally is costly and time-consuming. Xpander's platform aims to simplify agent deployment by packaging essential runtime services. Their platform handles tasks like sandboxed execution, persistent memory, and recovery from failures. This infrastructure burden has hindered controlled agent deployment for government and financial institutions. Xpander encourages enterprises to retain AI models while using their platform for orchestration and governance.