When Miro’s data team pointed AI agents directly at its Snowflake environment, the agents got the wrong answer more than 65% of the time. The problem wasn’t the model — it was context. With more than 10,000 tables and no semantic layer to guide routing, the agents had no way to know which data assets matched which business questions.DataHub is releasing a context intelligence layer Thursday that mines existing SQL query history to build a semantic index — and exposes it to agents via MCP, LangChain, Google’s Agent Development Kit and CrewAI. The company calls it Context Intelligence, and it’s built on the same query-log infrastructure DataHub has used for lineage tracking in production deployments worldwide.The company was founded by the team that built DataHub as an open 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 [...]
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 [...]
Our LLM API bill was growing 30% month-over-month. Traffic was increasing, but not that fast. When I analyzed our query logs, I found the real problem: Users ask the same questions in different ways.& [...]
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 [...]
Enterprise AI agents today face a fundamental timing problem: They can't easily act on critical business events because they aren't always aware of them in real-time.The challenge is infrast [...]
A company builds a governed context layer specifically to stop its AI agents from confidently giving wrong answers. Once that layer is live, the company is more than twice as likely to report the fail [...]
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 [...]
Building a context layer between enterprise data stores and AI agents is bespoke work, with no standard service to automate or maintain the graphs over time. Amazon is making a direct play to change t [...]
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 [...]