Snowflake Intelligence: Making AI Accessible for Every Organization

Take the next step forward into AI with Snowflake Intelligence.

Nov. 12, 2025 | By Adam Tobin

Snowflake continues to lead the way in making data and AI accessible to all users, regardless of their technical prowess. With the introduction of Snowflake Intelligence, users can now interact directly with all their data, structured or unstructured, via an agent that applies reasoning, tool usage and reflection to natural language queries.

Put simply, this new feature allows users to ask questions of their data and get reliable responses in a consistent, desirable format.

The Vision: AI With Snowflake Intelligence That Works for Everyone

For years, organizations have been excited about the idea of asking natural language questions of their data—and actually getting useful answers. In practice, however, the path to get there has often been complex and fragmented.

Setting up models, connecting data sources, creating dashboards and maintaining pipelines typically requires a team of engineers and data scientists, putting true AI accessibility out of reach for most.

Snowflake Intelligence changes that.

By embedding each of the required AI components natively within your data cloud platform, Snowflake eliminates the need for separate infrastructure, integrations or third-party orchestration. Everything from data preparation to model execution happens inside the same platform that already powers your analytics, data sharing and applications.

How Snowflake Intelligence Works

At its core, Snowflake Intelligence is built around agents, which are created directly on the Snowsight UI. Setup is easy, and users can choose to create a basic agent, which utilizes only the power of the underlying LLM, or they can choose to equip their agent with a variety of tools such as:

• Cortex Analyst
  • Semantic views and models
• Cortex Search
• Cortex Knowledge Extensions

Users can even create their own custom tools in the form of a function or procedure. These tools work together to make natural language interaction possible.

Here’s a breakdown of the components that go into creating an agent:

• Agents: intelligent entities that act on behalf of users; each agent is configured to perform specific tasks and can use one or more tools, such as Cortex Analyst or Cortex Search
• Cortex Analyst: generates SQL queries automatically based on natural language
• Semantic views and models: map domain-specific business terms to database schemas to add contextual meaning; persist these definitions in a schema-level object through semantic views while powering downstream queries and Cortex Analyst
• Cortex Search service: retrieves relevant information from unstructured data like documents or emails through indexing and retrieval-augmented generation (RAG)
• Custom tools: extend agent capabilities by developing functions or procedures that fit your business needs

Because these components are all built within Snowflake, there’s no need to stitch together multiple systems or move data outside your secure environment.

Customizing Snowflake Intelligence for Your Business

Organizations can tailor Snowflake Intelligence to their specific needs by connecting it to cortex search, semantic models or even external APIs with a custom tool. Not only that, but agents can also be instructed on how to respond. Whether you want your specific agent to always respond with a chart or a detailed explanation, Snowflake allows teams to define exactly how their insights are delivered.

Some of the configurations users can specify when creating an agent include:

• Orchestration model: determines which model will be used for orchestration purposes
• Planning instructions: allows users to specify how and when the agent should use each tool with which it’s equipped
• Response instructions: allows users to dictate the output of the agent’s responses; this could be specific to a certain tone or whether the response should be a graph or text

This flexibility enables business users, analysts and engineers alike to interact with data in a way that best fits their workflows.

Snowflake Intelligence in Action: Hedge Fund Use Case

Consider a hedge fund that manages multistrategy portfolios. Each portfolio manager (PM) is responsible for being the subject matter expert within their assigned industries and advising on the projected valuations of companies in those sectors. To perform at the highest level, PMs must stay constantly informed by tracking financial metrics, monitoring world events and news, and understanding federal and local regulations that may affect company performance and valuation.

Traditionally, this process requires significant manual effort and time-consuming research. With Snowflake Intelligence, PMs can dramatically reduce that effort by creating an agent dedicated to these tasks. Using existing internal data, a PM can equip the agent to instantly retrieve relevant information from their organization’s database, including historical financial records, performance metrics and research notes from other analysts or managers.

To go a step further, the PM can enhance their agent with Cortex Knowledge Extensions, bridging the gap between internal proprietary data and the external world. This allows the agent to integrate and analyze sources such as:

• Articles from global news outlets and financial sites
• Earnings call transcripts for covered companies
• Federal and local laws and regulations

Once properly configured, the agent enables PMs to surface key insights through natural language queries, shifting their time from gathering and transforming unstructured data to directly extracting information and value. When combined with other Snowflake capabilities such as sentiment analysis and forecasting, this approach transforms complex, time-intensive research into fast, intelligent and data-driven decision-making.

Achieving Accessible AI With Snowflake

Snowflake Intelligence represents the next evolution of the Data Cloud, where AI becomes an everyday tool, not a specialized skill. By unifying data and AI in one place, Snowflake continues to close the gap between what’s possible and what’s practical, delivering on their vision of making AI truly accessible for everyone.