Snowflake Delivers Semantic View Autopilot as the Foundation for Trusted, Scalable Enterprise-Ready AI

Snowflake announced new innovations to help enterprises deliver real business impact with AI, which requires more than high-quality models alone. Snowflake is unveiling Semantic View Autopilot (now generally available), an AI-powered service that automates the creation and governance of semantic views, giving AI agents a shared understanding of business metrics to deliver consistent, trustworthy outcomes. Snowflake is also introducing new capabilities across agent evaluations and observability, end-to-end machine learning, and AI cost governance. These innovations build on Snowflake’s existing enterprise-grade foundations, ensuring that AI systems such as Snowflake Intelligence are trusted, governed, and ready to operate reliably at scale, all while working directly on organizations’ most valuable data. 

“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.”

Enterprises are deploying AI agents into environments where business metrics are manually defined and inconsistently governed, leaving them without a shared understanding of business context. This fragmented approach to building the semantic layer is a bottleneck for AI adoption, producing unreliable outputs and weakening trust in AI.

Semantic View Autopilot addresses this challenge by automatically building, optimizing, and maintaining governed semantic views, potentially eliminating the need for manual, error-prone semantic modeling. This builds on Snowflake’s commitment to initiatives like the Open Semantic Interchange (OSI), which establishes an interoperable semantic layer across ecosystem leaders. While OSI provides the connectivity to share business logic across the ecosystem, Semantic View Autopilot adds the intelligence to create and continuously maintain it, making it the connective layer for trustworthy, scalable AI across all data, wherever it lives. 

By learning from real user activity and using AI-powered generation, Semantic View Autopilot will help ensure business logic remains accurate and up-to-date across Snowflake data and consumption tools including dbt Labs, Google Cloud’s Looker, Sigma, and ThoughtSpot (generally available soon). Customers can create semantic views using business definitions not only from Snowflake, but also from the business intelligence tools they already rely on. As a result, enterprises can minimize AI hallucinations while cutting semantic model creation from days to minutes, accelerating time-to-market and delivering a decisive competitive advantage. 

Leading organizations including eSentire, HiBob, Simon AI, and VTS are already using Semantic View Autopilot to dramatically reduce data-to-insight timelines and free data teams to focus on higher-value AI innovation.

"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.”

To speed up the delivery of powerful ML models, Snowflake is unveiling significant advancements to Snowflake Notebooks (now generally available), a fully-managed Jupyter-powered notebook built for end-to-end data science and ML development on Snowflake data. 

Snowflake Notebooks is integrated directly with Cortex Code in Snowsight (generally available soon), a data-native AI coding agent built to automate and accelerate end-to-end enterprise development. This allows users to build and deploy fully-functional ML pipelines using simple natural language prompts, reducing manual effort and speeding up workflows. Experiment Tracking (now generally available) makes it easy for teams to compare training runs, share results, and reproduce the best-performing models from within Snowflake Notebooks, turning experimentation into a repeatable, collaborative process. 

When models are ready for production, Snowflake supports real-time use cases with Online Feature Store (now generally available) and Online Model Inference (now generally available), enabling features to be served in milliseconds and predictions delivered at scale. With training, serving, and monitoring all happening within the Snowflake platform, teams can operationalize ML while maintaining consistent governance from data to model to insight. 

Enterprises like Aimpoint Digital are already leveraging Snowflake Notebooks to run ML projects on Snowflake, unlocking use cases like personalization, fraud detection, and predictive analytics. 

When AI powers mission-critical enterprise decisions, trust and reliability are essential. Cortex Agent Evaluations (generally available soon) addresses this challenge, helping teams confidently bring AI agents into production by making their behavior traceable, measurable, and auditable. 

Cortex Agent Evaluations give developers deep visibility into how agents reason, act, and respond, which enables them to systematically assess answer correctness, tool use, and logical consistency. With visibility into an agent's thought process, teams can easily identify errors, refine decision logic, and validate that agents are behaving as intended before they impact the business. It also promotes efficiency of the AI interactions by preventing operational waste such as redundant tool calls and spiraling compute costs. Enterprises like WHOOP are already leveraging Cortex Agent Evaluations in Snowflake to improve agent quality, without moving data or stitching together external monitoring tools. 

As Snowflake continues to innovate across AI, it is also focused on making AI economically sustainable for enterprises through expanded cost governance capabilities in Cortex AI Functions (now generally available) that help organizations plan, control, and audit their AI usage with precision. Before AI workloads ever run, teams can proactively estimate consumption using the AI_COUNT_TOKENS function, making it easier to understand how prompt design and context size translate into real cost. 

Source: Snowflake media announcement