AI systems are moving quickly from assistants to decision engines. They summarize documents, route customer support, score transactions, trigger automations, and increasingly participate in workflows that affect money, compliance, operations, and public services. But there is a structural problem in most AI systems today: they are not built to produce verifiable records of what actually ran. Most teams rely on logs, traces, dashboards, and database entries. Those are useful for debugging and ...