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 requires a different approach that incorporates context. VentureBeat's Q1 2026 Pulse survey underscores this trend: Every standalone vector database is losing adoption share, while hybrid retrieval intent has tripled to 33.3%, the fastest-growing strategic position in the dataset.Vector database pioneer Pinecone recognizes this and is pivoting to meet the specific needs of agentic AI.The company today announced Nexus, which it positions as a knowledge engine rather than an improvement on retrieval. Nexus introduces a context compiler that converts raw enterprise data into persistent, tas [...]
Redis built its name as the caching layer that kept web applications from collapsing under load. The problem it is targeting now has the same structure but is harder to solve: production AI agents fai [...]
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 rogue AI agent at Meta passed every identity check and still exposed sensitive data to unauthorized employees in March. Two weeks later, Mercor, a $10 billion AI startup, confirmed a supply-chain br [...]
Enterprise AI agents have a new production failure mode, and it is not the model. As enterprises move from single-layer RAG to hybrid retrieval architectures, the same underlying data produces differe [...]
For the first time on a major AI platform release, security shipped at launch — not bolted on 18 months later. At Nvidia GTC this week, five security vendors announced protection for Nvidia's a [...]
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 [...]
Every new AI agent your team deploys starts from scratch: no memory of how the business works, where data lives, or what rules apply. And as agentic coding tools spin up applications faster than anyon [...]