Organizations need to account for security, reporting, data access and regulatory obligations from the beginning. They need to be able to trace what AI agents are doing, understand why they’re making those decisions and govern those actions appropriately.

The foundation also has to be flexible enough to evolve as AI capabilities change. When governance systems are constructed around a single model, vendor or use case, it becomes a manual, time-consuming process to update or shift. And when organizations move too slowly to adapt, gaps emerge that can cause reputational, financial or operational harm.

As AI agents are increasingly introduced to workflows, it’s a constant balancing act to safeguard what the systems can access, the actions they can take and when a human must intervene. In low-risk processes, for example, AI agents may be able to execute entirely without human intervention. Conversely, a critical function like insurance underwriting must still rely on humans making the final judgement.