Companies are building governed context layers to prevent AI agents from giving confidently wrong answers, yet failure rates are paradoxically increasing. A July 2026 survey revealed 68% of enterprises traced such failures to missing or inconsistent business context, with 37% experiencing recurring issues. The failure rate is climbing, even as more enterprises implement governed layers. The method of providing context significantly impacts accuracy; retrieval over documents is common but imperfect, and many enterprises lack structured approaches. Businesses prioritize access control when acquiring retrieval systems, rather than retrieval accuracy, which directly addresses confident wrong answers. While enterprises value correctness by measuring response accuracy, their purchasing decisions do not align. A governed context layer, a shared model of business data, aims to fix this by making failures visible. Enterprises actively building or running such layers report higher recurring failures, indicating they are better at detecting problems, not that the layers cause them. This visibility is crucial for identifying long-standing data governance issues amplified by AI agents. Larger enterprises report more failures, suggesting better instrumentation and scrutiny. Retrieval alone is insufficient to close the context gap, particularly for inconsistent definitions across systems. The budget is flowing into building these layers, but actual production deployment lags, revealing a gap between spending and problem resolution. A clean failure record is a red flag, indicating a lack of checks rather than robust governance. Most enterprises plan to use a multi-vendor approach for their context layers, retaining control over this critical AI decision-making component.