Engineer shares open-source scaffold to bring AI agents safely into production

A software engineer with experience running a 25-agent production platform at Microsoft has published a lightweight open-source framework designed to make AI agents enterprise-ready. The scaffold, available on GitHub under an MIT licence, centres on three core mechanisms: a quality gate that scores agent outputs before any action is taken, a human approval gate that requires sign-off for high-stakes operations, and a provider abstraction layer that prevents vendor lock-in across models such as OpenAI, DeepSeek and Qwen. A state machine component explicitly models allowed workflow transitions, ensuring agents can only propose actions that a defined ruleset permits. The author argues that without such guardrails — covering output grading, auditable human oversight and model-agnostic design — AI agent projects remain demos rather than systems businesses can trust in production.