A VentureBeat Pulse survey of 101 enterprises finds that the infrastructure supplying business context to AI agents is being built faster than it can be trusted. Retrieval-augmented generation (RAG) has become the default context source, and provider-native retrieval (notably OpenAI file search and Google Vertex AI Search) has quietly overtaken dedicated vector databases. At the same time, a majority of enterprises have observed agents produce confident but incorrect answers attributable to missing or inconsistent context.
Key findings
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A defining failure: confident but wrong answers
- 57% of surveyed enterprises reported that in the past six months an AI agent gave a confident but incorrect answer they traced to missing or inconsistent business context (wrong metrics, stale definitions, missing documents). More than half of those said it happened more than once.
- Only 28% reported no such failure; the remainder either do not run agents on enterprise data or do not trace root cause closely enough to know.
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RAG is the primary context source
- For 38% of organizations, RAG over documents or a vector index is the main way agents understand business data — nearly double the share for a governed semantic layer or ontology (21%). Mixed approaches (14%), live system queries (10%), and long-context loading (6%) account for the rest; only 2% rely on the model’s general knowledge alone.
- Fine-tuning is notably absent from primary selection: businesses inject context at runtime rather than through model weight customization.
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Provider-native retrieval leads production usage
- OpenAI file search (40%) and Google Vertex AI Search (38%) are used more in production than any single dedicated vector database. Among specialists, Elasticsearch/OpenSearch (20%) and pgvector (12%) appear most often; pure-play vector databases such as Weaviate, Qdrant, Pinecone, and Milvus each occupy single-digit to low-double-digit shares. 13% said they run no production RAG.
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Stated intent vs. reality: many want best-of-breed but use provider bundles
- Despite provider-native dominance in practice, 36% of enterprises say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack. 21% plan consolidation, 21% expect a mix, and 9% intend to build their own layer.
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The market is converging on hybrid retrieval
- 34% expect hybrid retrieval — embeddings combined with reranking and access controls — to dominate production RAG systems by the end of 2026, versus 11% who expect vector-only retrieval to prevail. Another 17% said they don’t know, and 14% expect to move beyond a dedicated vector layer toward tool-first or long-context retrieval.
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A governed semantic/context layer is being built, but is not yet widely in production
- 58% either run a governed semantic layer in production (25%) or are piloting/building one (34%); an additional 17% are actively evaluating. In total, about three-quarters are engaged with the idea, but more are building than have shipped.
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Selection and monitoring priorities
- Enterprises pick retrieval systems on operability: ease of data ingestion (36%), latency/performance (32%), and operational simplicity (29%) lead selection criteria, ahead of retrieval accuracy and access control (23% each).
- Once running, organizations most frequently track response correctness (42%) and security/access control (38%), above latency (28%), operational stability (27%), and answer relevance (23%). Overall satisfaction averages 4.0 on a five-point scale.
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A retrieval reshuffle is likely
- 57% plan to switch or add a retrieval provider within 12 months (26% within the next quarter). While provider-native options (OpenAI 22%, Vertex AI Search 21%) top evaluation lists, open-source vector specialists such as Qdrant (14%) and Milvus (13%) are attracting greater interest than their current usage suggests.
Practical implications
The survey paints a clear picture: enterprises are wiring agents to business data quickly, and retrieval is the main conduit of that context. When retrieval is thin or inconsistent, the errors produced are the agent’s apparent authority. The industry response — governed semantic layers, hybrid retrieval with reranking and access controls — is under construction but not yet widespread in production. The tension between the convenience of provider-native bundles and the desire to retain best-of-breed independence will influence how the retrieval market evolves.
Methodology and limitations
The survey targeted organizations with more than 100 employees (n=101) in a single Q2 2026 (June) wave. The sample skews to the mid-market (31% 251–1,000 employees; 31% 101–250 employees) and includes managers, individual contributors, VPs/directors and C-suite respondents, with strong purchasing representation (46% final decision-makers). This is a self-selected, non-probability sample and should be read as a directional signal rather than a precise population measurement.
Conclusion
A context gap exists where AI agents answer confidently while the underlying context layer is not yet fully trustworthy. Simply increasing retrieval volume or index size will not solve the issue: enterprises need governed, consistent, access-aware context. Whether organizations complete the semantic layer and adopt hybrid architectures before confident-but-wrong failures affect material decisions remains an open question for future research.
