As enterprises move from AI experimentation to production deployments, one challenge is becoming increasingly apparent: AI systems are only as reliable as the business context they operate in.
Snowflake is attempting to address that problem with Horizon Context, a new set of semantic and metadata-management capabilities, currently in preview, that it unveiled Tuesday at its annual Snowflake Summit conference.
, the company’s existing data discovery, management and governance suite, collects metadata from across an enterprise’s data estate, enriches it with business definitions, relationships, lineage, and governance information, and makes that context available across AI and analytics systems.
These capabilities, according to Avanes, build on and , practice lead of the AI Stack at HyperFRAME Research.
“The value is not simply cataloging where data lives; it is giving AI systems the metadata, lineage, permissions, and business context needed to retrieve the right data safely,” Walter said.
In fact, according to , principal analyst at Moor Insights & Strategy, is the actual reason why most teams in an enterprise would end up with slightly different versions of a business metric, such as monthly active users, resulting in AI agents also being inconsistent downstream.
But, said Walter, the stitching together of different catalogs and semantic layers only works for semi-autonomous workflows. “What changed with AI agents is that those systems increasingly need access to context at runtime rather than through documentation and human interpretation,” she said. “Snowflake is trying to pull those pieces closer to the data platform, so context, semantics, access control, and execution are part of the same operating environment”
Automatically maintaining business context for AI agents
To supplement Horizon Context, Snowflake is also adding Semantic Studio, currently in private preview, to help enterprises reduce the effort required to build and maintain business context for agents and agentic workflows.
“Semantic Studio is a core part of the Enrich layer of Horizon Context, providing the AI-assisted workspace where teams define, test, and publish that business logic,” Avanes said.
The Enrich layer itself, which consists of companion capabilities like the Semantic View Autopilot, automatically layers intelligence on data assets, providing insights such as which assets are most trusted, how they connect, what they mean, and how to correctly calculate metrics, Avanes added.
According to Leone, Semantic Studio solves a critical challenge: “It will reduce the burden of SQL-savvy data engineers and let business owners author the shared definitions. That’s where most semantic-layer projects die today.”
For Walter though, there is still a caveat to these offerings.
“While these offerings reduce complexity, they do not remove the work. Someone still has to decide which metrics are authoritative, which data products are trusted, and who owns the business definitions,” she said.
Addressing security and governance concerns
Alongside these updates, Snowflake is also trying to address security concerns around agentic workflows with new capabilities inside Horizon Catalog’s Trust Center.
These capabilities, which are focused on AI Security Posture Management (SPM), include a new agent identity capability that enables enterprises to distinguish between human activity within a session and actions taken by an AI agent operating on a user’s behalf, Avanes said.
“This should provide greater transparency and auditability for agents, while also enabling enterprises to apply existing data access controls, such as dynamic masking and row access policies, on an agent-by-agent basis,” he said.
Another new capability, data exfiltration policies, currently in private preview, will help enterprises prevent unauthorized movement of sensitive data. These policies are part of Horizon Catalog’s broader governance framework, and will allow enterprises to define controls around how data can be accessed, shared, and moved across systems, Avanes said.
For enterprises, Walter noted, these capabilities could help address one of the biggest barriers to deploying AI agents in production: governance.
“Data exfiltration controls, AI Security Posture Management, and other centralized AI controls help CIOs move from experimentation to production by giving security teams a way to see and govern how AI workloads interact with enterprise data,” she said.
Echoing Walter, Leone pointed out that the “security story” is the gating issue for almost every agent deployment that Moor Insights & Strategy is currently tracking.
“Features such as data exfiltration policies and AI SPM are what convert a CISO from a ‘no’ to a ‘yes’ on moving an agent deployment into production,” he added.
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