Enterprise AI agents often provide confident but incorrect answers due to missing or inconsistent business context, a problem affecting 57% of organizations. This issue stems from the prevalent reliance on document retrieval for context, where ease of ingestion is prioritized over accuracy. A common solution is a governed context layer, a shared model of business data meanings that agents can consistently reference. Currently, 75% of enterprises lack such a layer, though 58% are actively building or have implemented one.Companies already experiencing these "confident-wrong" AI failures are more likely to be adopting this fix, while those unaffected show less urgency. Major data and AI platform vendors are developing various architectural approaches for this context layer, yet no single standard has emerged. Analysts agree that agents require governed, current, and low-latency context beyond just more tokens or better models. The challenge lies in integrating disparate tools for retrieval, memory, and access control, which leads to operational complexity.For enterprises, retrieval alone is insufficient to close the context gap; the budget is shifting towards semantic context layers. The market is fragmented, meaning integration, rather than picking a single vendor, will be necessary for some time. The decision to adopt these context platforms is happening this year, primarily driven by companies that have already faced AI agent inaccuracies. While agents are already in use, the underlying context infrastructure is still under construction, and vendors for these solutions are being selected now.