Most online AI agents are demos because they can’t reach production, mainly due to a lack of robust engineering. The author introduces an open-source scaffold designed to address this gap, focusing on three crucial, unglamorous engineering aspects. The first is a quality gate to score agent outputs and prevent sub-standard results from being released. This gate acts as a crucial check before any action is taken. The second is an approval gate, which pauses the agent for human review on sensitive actions like financial transactions. This ensures human oversight and provides an audit trail for compliance. The third is a model-agnostic provider, allowing easy switching between different LLM vendors. This prevents vendor lock-in and offers cost flexibility. The scaffold also emphasizes modeling business processes as state machines, not just chat loops. This explicit modeling enforces valid transitions and controlled autonomy. The author provides a practical example of an expedited order workflow demonstrating these features. The scaffold is a small, readable harness that prioritizes the pattern over a complex framework. Its MIT license encourages adoption and contributions. The author, a seasoned AI agent engineer, offers consulting on building production-ready agent systems. The core message is that robust guardrails are essential for transforming AI agent demos into trustworthy production systems.