Snowflake kicked off its annual user conference, Snowflake Summit 2025, on Tuesday. The cloud-based data-storage company launched a slew of new features. The biggest highlight was agentic AI solutions that help organizations better make sense of their data: Snowflake Intelligence and Data Science Agent.
With the rise of agentic AI, Snowflake is the latest company to embrace the burgeoning technology to optimize how companies sort, analyze, and understand their data.
Snowflake Intelligence
AI chatbots have risen in popularity because they make it easy to find what you are looking for using a simple, conversational text prompt. Snowflake is now bringing that capability to company data with Snowflake Intelligence, allowing business users to access insights using natural language queries in one unified platform.
Also: Tech leaders are seemingly rushing to deploy agentic AI – here’s why
The experience, powered by OpenAI and Anthropic large language models and Cortex Agents, pulls from both structured data — think data that has been carefully organized into tables or standardized formats — and unstructured data, like documents, emails, etc. This eliminates a common technical challenge that companies face when adopting AI tools: their data isn’t structured correctly.
“Snowflake Intelligence breaks down these barriers by democratizing the ability to extract meaningful intelligence from an organization’s entire enterprise data estate – structured and unstructured data alike,” said Baris Gultekin, head of AI at Snowflake, in the blog post. “This isn’t just about accessing data, it’s about empowering every employee to make faster, smarter decisions with all of their business context at their fingertips.”
<!–>
Snowflake Intelligence runs within the organization’s existing Snowflake environment, keeping all of the existing security controls and governance policies in place. To make the data it pulls from as comprehensive as possible, it draws from different data sources, including Snowflake, Box, Google Drive, Workday, and data types, using Snowflake Openflow to bring together insights from spreadsheets, documents, images, and databases.
Beyond generating insights, Snowflake Intelligence can also render visualizations of the data and access third-party knowledge through Cortex Knowledge Extensions, which will be generally available soon on Snowflake Marketplace, incorporating expert content from Stack Overflow, The Associated Press, USA TODAY, and more.
Data Science Agent
Data scientists are in demand as ever, as they are responsible for building machine learning workflows. Using Anthropic’s Claude, the Data Science Agent seeks to help ML teams by taking on some of the manual tasks, such as data analysis, data preparation, and training.
Also: What are AI agents? How to access a team of personalized assistants
Specifically, the agent provides “verified solutions in the form of fully functional ML pipelines that can be easily executed from a Snowflake Notebook,” Snowflake said. The goal is for data scientists to be able to shift their priorities to more high-impact work, as well as reduce the amount of time an ML use case goes from idea to production.
Get the morning’s top stories in your inbox each day with our Tech Today newsletter.
