Snowflake, the AI Data Cloud company, has introduced a powerful set of new innovations designed to help enterprises achieve real business value with artificial intelligence. While high-quality AI models remain important, Snowflake emphasized that true enterprise success requires much more including trust, governance, consistency, and scalability.
To address these needs, Snowflake announced the general availability of Semantic View Autopilot, an AI-powered service that automates the creation and governance of semantic views. This breakthrough allows AI agents to operate with a shared understanding of business metrics, ensuring outcomes remain consistent, reliable, and trustworthy across the organization.
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“AI is quickly becoming part of the operating fabric of the enterprise, not a side project,” said Christian Kleinerman, EVP of Product, Snowflake. “Our focus is to make that future a reality now by ensuring AI agents operate on consistent business logic, behave as expected, and scale without surprises. By unifying trust, governance, and execution on one platform, we’re delivering AI that actually works in the environments our customers care about.”
Automating the Semantic Layer for Reliable AI Outcomes
Enterprises often deploy AI into environments where business metrics are manually defined and inconsistently managed. As a result, AI systems lack shared context, which can lead to unreliable outputs and reduced trust.
Semantic View Autopilot solves this problem by automatically building, optimizing, and maintaining governed semantic views. Instead of relying on error-prone manual modeling, enterprises can now establish accurate semantic layers in minutes rather than days.
Moreover, this innovation supports Snowflake’s broader commitment to the Open Semantic Interchange (OSI) initiative, which promotes interoperability across ecosystem leaders. While OSI enables shared business logic, Semantic View Autopilot adds intelligence that continuously maintains it across all data environments.
Additionally, the service learns from real user activity and integrates with popular tools such as dbt Labs, Google Cloud’s Looker, Sigma, and ThoughtSpot. This helps enterprises reduce AI hallucinations, accelerate deployment, and improve competitive advantage.
Leading companies like eSentire, HiBob, Simon AI, and VTS are already leveraging this technology.
“At Simon AI, our focus is helping businesses turn data into real, actionable outcomes. But inconsistencies between business logic have historically slowed how far AI can be applied,” said Matt Walker, CTO at Simon AI. “Semantic View Autopilot provides our AI systems with a consistent, governed understanding of business metrics that we can collaborate upon with our customers. This allows us to deliver reliable personalization and AI-driven engagement that our customers can trust to drive measurable results.”
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Snowflake Boosts Machine Learning Production and Deployment
Beyond semantic automation, Snowflake is also accelerating ML development through major enhancements to Snowflake Notebooks, now generally available. Built on Jupyter, this fully managed environment supports end-to-end data science and ML workflows directly on Snowflake data.
Snowflake Notebooks now integrate with Cortex Code in Snowsight, an AI coding agent that enables teams to build and deploy ML pipelines using simple natural language prompts. Furthermore, Experiment Tracking helps teams compare training runs, reproduce models, and collaborate efficiently.
Once models reach production, Snowflake supports real-time business needs through Online Feature Store and Online Model Inference, enabling millisecond-level predictions at scale.
Ensuring Trusted AI Agents with Cortex Evaluations
As AI becomes central to enterprise decision-making, Snowflake is also introducing Cortex Agent Evaluations, which will soon be generally available. This capability helps teams trace, measure, and audit AI agent behavior before deployment.
By providing deeper visibility into reasoning, tool usage, and response quality, organizations can refine agent performance while preventing unnecessary compute waste and rising costs. Companies like WHOOP are already using these tools to enhance AI reliability.
Finally, Snowflake expanded AI cost governance features, including the AI_COUNT_TOKENS function, allowing enterprises to estimate and control AI consumption proactively.
With these innovations, Snowflake continues strengthening its position as a trusted platform for scalable, governed, and economically sustainable enterprise AI.
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