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 become the default approach for connecting LLMs to external knowledge. The pattern is straightforward: chunk documents, embed them into vectors, store them in a database, and retrieve the most similar passages when queries arrive. This works adequately for one-off questions over static documents. But the architecture breaks down when AI agents need to operate across multiple sessions, maintain context over time, or distinguish what they've observed from what they believe.A new open source memory architecture called Hindsight tackles this challenge by organizing AI agent memory into four sepa [...]
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 [...]
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 [...]
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 [...]
Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits.MeMo, a [...]
Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits.MeMo, a [...]
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 [...]
RAG isn't always fast enough or intelligent enough for modern agentic AI workflows. As teams move from short-lived chatbots to long-running, tool-heavy agents embedded in production systems, thos [...]
When an enterprise LLM retrieves a product name, technical specification, or standard contract clause, it's using expensive GPU computation designed for complex reasoning — just to access stati [...]