Enterprise teams keep watching the same thing happen. An AI agent demos beautifully, goes to production, and stalls: it runs for a short stretch, then needs a human to top up its context and check its output, and the promised efficiency drains into supervision. The agent did the work; you did the watching. It’s one reason so many agent pilots never turn into production systems.The pitch on the other side of that wall is the one every team wants to believe: an agent that runs a long job on its own, overnight if it has to, and leaves a person to validate only the last 10%. Whether that is achievable turns on a problem the orchestration conversation mostly skips. When AI firm Chroma tested 18 leading models, every one lost accuracy as its input grew, a property of how attention works, not a [...]
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 [...]
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 [...]
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 [...]
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 [...]
New VB Pulse data shows Microsoft and OpenAI leading enterprise agent orchestration, but Anthropic’s first measurable foothold points to a larger fight over who controls the infrastructure where AI [...]
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 [...]
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 [...]