Enterprise RAG: Why context is failing AI agents

A new analysis of enterprise RAG implementations reveals that provider-native tools like OpenAI’s file search and Google’s Vertex AI Search dominate the market, but many developers still prefer standalone vector databases to avoid vendor lock-in. The core failure point is the lack of a governed semantic layer, leading to inconsistent data and LLM hallucinations in production. The trend is shifting toward hybrid retrieval to improve enterprise-grade reliability.

Enterprise RAG: Why context is failing AI agents I've been looking into why so many RAG Retrieval-Augmented Generation implementations fail in production, and this data explains a lot. We're seeing a weird split in how companies handle their AI workflow. On one hand, provider-native tools like OpenAI’s file search and Google’s Vertex AI Search are actually dominating the market. On the other hand, a huge chunk of devs still want "best-of-breed" standalone vector databases to avoid vendor lock-in. The real struggle seems to be the lack of a governed semantic layer. A lot of teams are rushing to build these to fix the inconsistency issues, but most aren't actually in production yet. It feels like everyone is just slapping a vector DB on top of their docs and hoping for the best, then wondering why the LLM agent hallucinates when it hits a gap in the data. For those of us trying to build a real-world deployment, the trend is clearly moving toward hybrid retrieval. It seems like relying on a single method just isn't cutting it for enterprise-grade reliability. If you're setting this up from scratch, it feels like the focus needs to shift from "how do I retrieve this?" to "how do I ensure the retrieved data is actually the truth?" I'm curious if anyone has actually successfully deployed a governed semantic layer that stopped these "confident hallucinations." What does your stack look like? Next AI Infrastructure: The Compute Gap Problem → /en/threads/2161/