VentureBeat’s Pulse Research surveyed 101 enterprises (all with more than 100 employees) in Q2 2026 about the infrastructure that feeds business context to AI agents. The central finding is a “context gap”: agents that answer confidently while running on data their owners do not fully trust. A majority (57%) reported that in the prior six months their agents produced confident but wrong answers traced to missing or inconsistent context, and more than half of those said it happened more than once.
Retrieval-augmented generation is the default context source, serving as the primary approach for 38% of respondents. Provider-native retrieval has overtaken dedicated vector databases in practice, led by OpenAI’s file search (40%) and Google’s Vertex AI Search (38%). Enterprises expect hybrid retrieval to dominate by the end of 2026 (34%). A governed semantic layer is emerging as the proposed fix, with 58% running or building one, though most is not yet in production.
Why it matters
Stated preference and actual usage diverge: a plurality (36%) say they intend to keep best-of-breed standalone tools, while a majority (57%) plan to switch or add a provider within the year. The report frames the risk as agents wearing an authority their underlying context does not yet earn.
Who should care
Enterprise teams building or buying RAG and context infrastructure, and those evaluating provider-native versus best-of-breed retrieval systems.