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 different answers depending on which agent, tool or system asks the question. Revenue means one thing in a business intelligence (BI) dashboard, something slightly different in a SQL table and something else again in an agent instruction. The retrieval infrastructure build-out of the past two years produced faster and cheaper vector search. It did not produce a shared definition of what the data means.At Snowflake Summit 26 in San Francisco, the data cloud vendor is taking a broad swing at that problem, with announcements spanning a Kafka-compatible managed streaming service called Data Stream, adaptive compute improvements, expanded Apache Iceberg interoperability and updates to its Cowork and CoCo agent and coding products. Running underneath all of it is a context layer: Horizon Context and Cortex Sense, a two-layer system designed to give agents a governed, shared definition of business logic across retrieval stacks. The context problem is why it matters: VentureBeat’s VB Pulse Q1 2026 data, drawn from a survey of organizations with 100 or more employees, shows hybrid retrieval intent tripling from 10.3% in January to 33.3% in March, the fastest-growing strategic position in the dataset.”There are a lot of tools out there that you can ask questions, you get a very confident answer, but whether it’s correct or not is different,” said Christian Kleinerman, EVP of Product at Snowflake.From fragmented business logic to a governed context layerThe problem Horizon Context targets is specific. Business logic today is distributed across SQL, BI dashboards and agent ins …
