Enterprises have moved quickly to adopt RAG to ground LLMs in proprietary data. In practice, however, many organizations are discovering that retrieval is no longer a feature bolted onto model inference — it has become a foundational system dependency.Once AI systems are deployed to support decision-making, automate workflows or operate semi-autonomously, failures in retrieval propagate directly into business risk. Stale context, ungoverned access paths and poorly evaluated retrieval pipelines do not merely degrade answer quality; they undermine trust, compliance and operational reliability.This article reframes retrieval as infrastructure rather than application logic. It introduces a system-level model for designing retrieval platforms that support freshness, governance and evaluation [...]
Something shifted in enterprise RAG in Q1 2026. VB Pulse data spanning January through March tells a consistent story: the market stopped adding retrieval layers and started fixing the ones it already [...]
A core element of any data retrieval operation is the use of a component known as a retriever. Its job is to retrieve the relevant content for a given query. In the AI era, retrievers have been used a [...]
It has become increasingly clear in 2025 that retrieval augmented generation (RAG) isn't enough to meet the growing data requirements for agentic AI.RAG emerged in the last couple of years to bec [...]
By now, enterprises understand that retrieval augmented generation (RAG) allows applications and agents to find the best, most grounded information for queries. However, typical RAG setups could be an [...]
An enterprise AI agent answers with total confidence, but the number is wrong. Nobody catches it until someone traces it back to a stale metric definition or a document the retrieval system never pull [...]
Enterprise AI has a data problem. Despite billions in investment and increasingly capable language models, most organizations still can't answer basic analytical questions about their document re [...]
The vector database category is undergoing a shift in response to the needs of agentic AI. The retrieval-augmented generation (RAG)-to-vector database pipeline doesn't cut it anymore; agentic AI [...]
Enterprise companies are running AI agents ahead of the controls needed to manage them — and they deployed that way knowingly. That is the central finding from VentureBeat Research's June surve [...]