Enterprise AI agents are delivering confident wrong answers, and the root cause is not the model. At Snowflake Summit 26 in San Francisco, the data cloud vendor unveiled a two-layer context system: Horizon Context and Cortex Sense. The goal is to provide a governed, shared definition of business logic across retrieval stacks. This comes as hybrid retrieval intent tripled from 10.3% in January to 33.3% in March 2026 according to VB Pulse data, making semantic fragmentation an immediate production problem.
Why context is the battleground
Christian Kleinerman, EVP of Product at Snowflake, explained that business logic today is scattered across SQL, BI dashboards and agent instructions. When multiple agents query the same data, they reason over different schemas and return different answers. Snowflake's solution anchors context in the catalog and governance layer, not the agent layer. Horizon Context covers what customers explicitly declare, while Cortex Sense derives context from usage patterns automatically. Both layers feed into Cortex Search, Snowflake's RAG implementation, and are tied to the Open Semantic Interchange for portability across third-party tools.
The concrete implication for enterprises
The context problem extends beyond Snowflake. Zip, the $2.2 billion procurement platform, launched five Superagents and a native Model Context Protocol (MCP) implementation to ensure every agent action is governed, traced and auditable. Meanwhile, ZeroDrift raised $10 million for a compliance service that sits between AI models and end users, flagging non-compliant messages. The common thread is clear: autonomous AI agents cannot scale without a reliable, auditable context layer. According to IDC analyst Devin Pratt, the context layer, not the model, is the thing to watch. Enterprises that ignore semantic fragmentation risk wrong answers at scale, with immediate regulatory consequences (SOX fines up to $25 million). The lesson is that the next big enterprise investment will be in context infrastructure that makes agents trustworthy and verifiable.
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