At the Snowflake Summit 2025, the data cloud pioneer unveiled a new generation of AI-powered agents that promise to reshape how businesses interact with data.
Introducing Snowflake Intelligence and the Data Science Agent, the company aims to bridge the long-standing divide between raw data and actionable insight, putting analytics directly in the hands of anyone, not just data specialists.
Talking to Data, Not Coding It
Snowflake Intelligence represents a significant leap in simplifying enterprise analytics. Instead of relying on SQL experts or business intelligence teams, users can now query complex data sets in plain language. Imagine typing a request like “Show me last quarter’s top-selling product and the reason behind its performance.”
Behind the scenes, Snowflake’s Cortex AI engine interprets the request using large language models (including Anthropic’s Claude) to generate optimized SQL queries. It then scans both structured and unstructured sources, spreadsheets, CRM logs, PDFs, or support notes before synthesizing a narrative answer complete with visual insights. Users get contextual explanations, next-step suggestions, and even workflow automations, all within the Snowflake ecosystem.
By allowing data conversations instead of code writing, Snowflake Intelligence transforms data exploration into a natural dialogue. Moreover, because all processing happens within Snowflake’s governed environment, enterprises maintain compliance, data lineage, and security integrity. This is data democracy with enterprise-grade guardrails.
Automating the Science of Machine Learning
For technical teams, Snowflake’s Data Science Agent targets another pain point, building and deploying machine learning (ML) models. Instead of juggling notebooks, tools, and pipelines, data scientists can prompt the agent to “build a churn prediction model using 12 months of usage data.”
The AI then auto-generates an end-to-end ML pipeline inside a Snowflake Notebook, covering data preparation, feature engineering, model selection, and evaluation. Engineers can review and modify any step, and with one command, deploy it as a Snowflake ML Job. No exports, no external orchestration tools, no security compromises.
Snowflake’s platform even handles GPU orchestration, experiment tracking, and model serving, making ML pipelines reproducible, transparent, and production-ready.
A Shift Toward Conversational Intelligence
Snowflake’s vision aligns with the growing enterprise trend of AI-driven automation and no-code empowerment. From business leaders seeking instant insight to data scientists aiming to accelerate model deployment, these AI agents transform how organizations think about analytics. The result: faster decision-making, improved data literacy, and reduced dependency on specialized intermediaries.
The Future: From Static Dashboards to Dynamic Dialogue
With Snowflake’s AI agents, the company is redefining what data analytics means, moving from static dashboards to interactive, conversational systems. By embedding intelligence directly within its Data Cloud, Snowflake empowers enterprises to not just analyze but act on data in real time.
In a world where insight speed determines competitiveness, Snowflake’s AI agents mark a pivotal step toward truly democratized data intelligence, where every question, from any user, can spark a meaningful, data-backed answer.
Media Contact: Chithra Sivaramakrishnan | +1(646) 362-3877 | chithra.sivaramakrishnan@prolifics.com
