An agent can find a product, fill a cart, or prepare a subscription change in seconds. The risk begins when it can submit the order, move money, change an account, or trigger a bulk operation without a reliable control outside the model.<\/p>\r\n\r\n\r\n\r\n
To prevent unsafe AI agent actions<\/strong>, design the workflow so the agent can propose work while a separate policy layer verifies authority, scope, and approval before execution. A spending cap helps limit exposure, but it cannot tell whether the customer intended the purchase, whether the same request already ran, or whether the selected item is correct.<\/p>\r\n\r\n\r\n\r\n This guide covers a practical action-risk map, transaction confirmations, permission boundaries, approval routing, duplicate protection, testing, and incident response. The right controls depend on the action and business policy. A fixed dollar threshold or universal \u201chuman approval for every refund\u201d rule is not appropriate for every organization.<\/p>\r\n\r\n\r\n\r\n Treat the model as a request composer. Let deterministic application logic decide whether an action is allowed to execute.<\/p>\r\n<\/blockquote>\r\n\r\n\r\n\r\n\r\n