In an analysis of 101 enterprises, the rapid build-out of AI context infrastructure is outpacing the trust enterprises place in it. Retrieval-augmented generation (RAG) has become the predominant method for feeding AI agents business context, with provider-native retrieval tools like OpenAI’s file search and Google’s Vertex AI Search surpassing traditional vector databases. Despite this, over half of enterprises report that their AI agents have confidently delivered incorrect answers due to gaps or inconsistencies in the underlying context. While a governed semantic layer is emerging as a vital fix, most organizations are still developing it, pointing to a significant ‘context gap’—where agents sound authoritative but operate on shaky foundations. Enterprises face a strategic tension: adopting provider-native bundles for convenience while preferring best-of-breed, standalone tools for control. Hybrid retrieval architectures combining embeddings with reranking and access controls are widely expected to become the industry standard by 2026. The study highlights that solving this challenge is less about increasing data volume and more about building a consistent, governed, and secure context layer—as AI agents currently outpace the maturity of this essential support infrastructure.
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