Enterprise AI agents face a significant challenge beyond the AI models themselves: inconsistent answers arising from fragmented business logic across different systems. As organizations shift from basic retrieval architectures to hybrid retrieval methods, varying interpretations of data emerge depending on the querying tool or agent. Revenue figures, for example, differ between BI dashboards, SQL tables, and agent instructions due to differing semantic understandings.
At the Snowflake Summit 26, Snowflake introduced solutions aiming to unify business logic via a two-layer context system—Horizon Context and Cortex Sense. Horizon Context consolidates customer-declared metadata from various sources to create a shared, governed business logic catalog, while Cortex Sense automatically enriches context based on data usage patterns, enhancing default agent behavior.
This approach addresses the growing demand for hybrid retrieval strategies, as confirmed by recent market data showing a rapid increase in adoption. Snowflake’s system emphasizes interoperability and governance to ensure consistent, auditable, and portable semantic definitions across platforms.
Other vendors like Microsoft, Redis, and Pinecone are also developing context layers, highlighting the industry’s recognition that trustworthy AI agents depend as much on their underlying data semantics as on the models themselves.
Experts emphasize that the success of AI agents hinges on robust context layers with clear governance, lineage, and portability—not just a plug-and-play solution. Enterprises must carefully evaluate these factors to avoid costly errors from AI agents confidently delivering incorrect answers.
