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Agent context layers: Enterprises governing their AI data are catching twice as many bad answers as the ones who aren't
Across 101 enterprises, the context feeding AI agents is failing often and repeatedly. Sixty-eight percent have traced a confident but wrong agent answer to missing or inconsistent business context in [...]
Enterprise AI agents are only as reliable as the messiest documents behind them
Enterprise AI has largely been built around context engineering. Teams connect enterprise systems, generate chunks and embeddings, build retrieval pipelines, and assemble the context needed by individ [...]
The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix
Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the default context sourc [...]
The retrieval rebuild: Why hybrid retrieval intent tripled as enterprise RAG programs hit the scale wall
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 [...]
GAM takes aim at “context rot”: A dual-agent memory architecture that outperforms long-context LLMs
For all their superhuman power, today’s AI models suffer from a surprisingly human flaw: They forget. Give an AI assistant a sprawling conversation, a multi-step reasoning task or a project spanning [...]
Databricks' Instructed Retriever beats traditional RAG data retrieval by 70% — enterprise metadata was the missing link
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 [...]
Why Google’s File Search could displace DIY RAG stacks in the enterprise
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 [...]
Context architecture is replacing RAG as agentic AI pushes enterprise retrieval to its limits
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 [...]
The RAG era is ending for agentic AI — a new compilation-stage knowledge layer is what comes next
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 [...]