AI agents keep giving confident wrong answers. The context layer is enterprise AI's next production problem.

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Enterprise AI agents are plagued by a new production failure mode: confidently providing incorrect answers. This issue arises when different agents, tools, or systems query the same underlying data, yielding varying responses due to disparate interpretations of business logic. As enterprises transition from single-layer Retrieval-Augmented Generation (RAG) to hybrid retrieval architectures, the retrieval infrastructure built over the past two years has enabled faster and cheaper vector search, but not a unified definition of data meaning.

Snowflake, a leading data cloud vendor, is tackling this problem head-on with the introduction of a context layer, comprising Horizon Context and Cortex Sense. This two-layer system aims to provide agents with a governed, shared definition of business logic across retrieval stacks. Christian Kleinerman, EVP of Product at Snowflake, highlighted the issue: '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.' The context layer problem is significant, with VentureBeat's VB Pulse Q1 2026 data showing a tripling of hybrid retrieval intent from 10.3% in January to 33.3% in March.

Snowflake's solution involves two layers: Horizon Context, a customer-managed layer built on Snowflake's acquisition of Select Star, and Cortex Sense, a platform-derived layer that automatically builds and enriches context from customer data and usage patterns. Horizon Context pulls metadata from various sources into the Horizon Catalog, ensuring that every agent, BI tool, and external system draws from the same governed definition. Semantic View Autopilot creates and refines semantic views over time, extending curated business logic without manual effort.

Cortex Sense, on the other hand, improves the default experience before explicit curation. Kleinerman emphasized the distinction between the two layers: 'Think of Horizon Context as everything that is explicit and declared by customers, and Cortex Sense is anything that is implicit and derived by us.' The context layer market is increasingly crowded, with Microsoft, Redis, and Pinecone also targeting the problem. Analysts, however, view Snowflake's approach positively.

Devin Pratt, research director at IDC, noted that Snowflake is headed in the right direction, and the context layer, not the model, is the key to watch. Mike Leone, VP and principal analyst at Moor Insights and Strategy, agreed, praising Snowflake's architectural approach to splitting context into two buckets. For enterprises evaluating context layers, the bar is set high.

Governance, lineage, portability, and accuracy are essential for a trustworthy context layer. As Leone cautioned, 'Most vendors selling a drop-in fix are overpromising,' and enterprises must be cautious when evaluating solutions.