Presented by Zeta GlobalThe gap between what AI promises and what it delivers is not subtle. The same model can produce precise, useful output in one system and generic, irrelevant results in another. The issue is not the model. It's the context.Most enterprise systems were not built for how AI operates. Data is scattered across tools. Identity is inconsistent. Signals arrive late or not at all. Systems record events but fail to connect them into a continuous view.AI depends on that continuity. Without it, the model fills in the gaps so the result looks polished but lacks relevance. This is where most teams get stuck.A better model does not fix fragmented, stale, or commoditized data. Gartner estimates organizations lose an average of $12.9 million annually due to poor data quality. A [...]
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 [...]
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 [...]
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 [...]
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 [...]
A new framework from Stanford University and SambaNova addresses a critical challenge in building robust AI agents: context engineering. Called Agentic Context Engineering (ACE), the framework automat [...]
One employee at Vercel adopted an AI tool. One employee at that AI vendor got hit with an infostealer. That combination created a walk-in path to Vercel’s production environments through an OAuth gr [...]
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 [...]