Keeping a human approval gate between LLM agent proposals and system writes

An AI agent can prepare a consequential change without being permitted to carry it out, which lets organizations automate work while keeping a person accountable. The article uses a supplier onboarding example, where the agent validates data and drafts an ERP record but cannot create the supplier or release payments. It recommends a state machine with a persisted, versioned proposal that reviewers approve exactly, along with separate execution endpoints that accept only valid approval tokens. Approval records should capture identity, payload hash, decision and expiry, and executors need idempotency keys and reconciliation. The author suggests testing rejection, expiry, modification, duplicate and outage paths, and piloting one low-volume workflow with measured outcomes.