Anyone else finding that “agent said it succeeded” ≠ "it actually did the right thing"?

Teams deploy AI agents for things like refunds, purchase orders, CRM updates, and support resolutions. The agents often report “done” and the API call returns 200, but later someone discovers: The refund amount was wrong The customer wasn’t actually eligible A duplicate order went through The ERP never updated Policy was quietly violated So the technical action succeeded, but the business outcome was wrong. I’m trying to understand how common this actually is in production. If you’re running agents (or automated workflows) that take real actions: Do you (or someone on your team) still manually check a meaningful percentage of them? Have you had cases where the agent reported success but the actual result was incorrect or incomplete? How do you currently catch these? Manual reconciliation? Spot checks? Alerts from finance/ops? What’s the real cost when one slips through (time, money, customer impact)? Not looking for theoretical answers or “AI needs more guardrails.” Just real operational experience from people who’ve dealt with this. Curious how painful this actually is right now versus something teams just accept as the cost of automation. Thanks.