Most AI agents seen online are mere demos that fail to reach production due to unaddressed critical engineering challenges. The true difficulty lies not in calling AI models, but in preventing harmful agent actions. Shipping an AI agent requires more than a chat interface; it demands robust quality gates, human approval processes, and model vendor flexibility. The author has developed a small open-source harness to address these issues.The quality gate ensures agent outputs meet a predefined standard before execution. An approval gate is crucial for authorizing significant actions like financial transactions, preventing unauthorized changes. A model-agnostic design allows easy switching between AI vendors, avoiding vendor lock-in and managing costs. Business workflows should be modeled as explicit state machines, not open-ended chat loops, for better control.The harness integrates these components, demonstrating a production-ready flow in minimal code. This approach prioritizes safety and reliability, distinguishing functional systems from mere demonstrations. Building these guardrails first is essential for creating AI agents that businesses can trust. Businesses often desire autonomy with control, which this structured approach provides. The author, with experience at Microsoft, offers this scaffolding to aid developers.