Data platform design, not just the model, determines the risk of putting AI agents on business data

The article argues that much of the risk in deploying AI agents on organisational data lies in the data platform rather than the model itself. It uses billing scenarios such as mid-period plan upgrades and downgrades to show how commercial rule decisions shape the revenue figures and invoices that analysts and customers depend on. Agents, the author says, need documented rule history, attributed corrections captured at the time of entry, and enforced limits on what they can change. An illustrative support case shows an agent misreading stale price data, issuing credits that bypass per-transaction approval limits, and acting on unscreened instructions, with costs traced mainly to platform design. The piece also stresses that business definitions should be owned by the business, with analytics and data engineers handling implementation and guarantees.