Snowflake Intelligence offers a powerful solution for organizations to access and activate their vast data. It addresses common challenges for business users struggling to get timely answers from scattered data, and for data teams overwhelmed by ad hoc requests. By using AI agents, Snowflake Intelligence enables employees to securely talk with their data, derive deeper insights, and initiate actions, all from a unified, easy-to-use interface. This transforms how businesses operate by bridging the gap between data and actionable insights.

NOTE: Snowflake Intelligence is in Public Preview as of August 2025.

What is Snowflake Intelligence?

Snowflake Intelligence is an agentic AI solution, enabling business users to directly and securely interact with their organization's structured and unstructured data using natural language. Snowflake Intelligence provides:

Snowflake Intelligence

Use Cases

Snowflake Intelligence streamlines data-driven decision-making across various business use cases:

Prerequisites

What You Will Learn

How to create building blocks for creating a Snowflake Intelligence agent that can intelligently respond to questions by reasoning over both structured and unstructured data.

What You Will Build

A Snowflake Intelligence agent that can intelligently respond to questions by reasoning over both structured and unstructured data.

Create database, schema, tables and load data from AWS S3

Cortex Analyst

This tool enables the agent to query structured data in Snowflake by generating SQL. It relies on semantic views, which are mappings between business concepts (e.g., "product name," "sales") and the underlying tables and columns in your Snowflake account. This abstraction helps the LLM understand how to query your data effectively, even if your tables have complex or arbitrary naming conventions.

Cortex Search

This tool allows the agent to search and retrieve information from unstructured text data, such as customer support tickets, Slack conversations, or contracts. It leverages Cortex Search to index and query these text "chunks," enabling the agent to perform Retrieval Augmented Generation (RAG).

OPTIONAL: Aggregated Support Cases using Cortex AISQL

Instead of creating a Cortex Search service for individual support cases, you may create one on aggregated support cases. This really depends on your use case, but it's shown here as an example using Cortex AISQL.

Execute the following SQL statements that use AI_AGG() to create aggregated support cases summary which is inserted into a new table AGGREGATED_SUPPORT_CASES_SUMMARY. Then, a Cortex Search service is created on that table.

-- Use AI_AGG to aggregate support cases summary and insert into a new table AGGREGATED_SUPPORT_CASES_SUMMARY

use database DASH_DB_SI;
use schema RETAIL;

create or replace table AGGREGATED_SUPPORT_CASES_SUMMARY as
 select 
    ai_agg(transcript,'Read and analyze all support cases to provide a long-form text summary in no less than 5000 words.') as summary
    from support_cases;

-- Create Cortex Search service on table AGGREGATED_SUPPORT_CASES_SUMMARY

create or replace cortex search service AGGREGATED_SUPPORT_CASES 
on summary 
attributes
  summary 
warehouse = compute_wh 
embedding_model = 'snowflake-arctic-embed-m-v1.5' 
target_lag = '1 hour' 
initialize=on_schedule 
as (
  select
    summary
  from AGGREGATED_SUPPORT_CASES_SUMMARY
);

Create Agent

An agent is an intelligent entity within Snowflake Intelligence that acts on behalf of the user. Agents are configured with specific tools and orchestration logic to answer questions and perform tasks on top of your data.

Note that you can create multiple agents for various use cases and/or business teams in your organization.

Add Instructions

Add the following starter questions under Sample questions:

Add Tools

Tools are the capabilities an agent can use to accomplish a task. Think of them as the agent's skillset and note that you can add one or more of each of the following tools.

Open Snowflake Intelligence and make sure you're signed into the right account. If you're not sure, click on your name in the bottom left » Sign out and sign back in. Also note that your role should be set to SNOWFLAKE_INTELLIGENCE_ADMIN and warehouse is set to DASH_WH_SI.

Now, let's ask the following questions.

Q1. Show me the trend of sales by product category between June and August

Q1

Q2. What issues are reported with jackets recently in customer support tickets?

Q2

Q3. Why did sales of Fitness Wear grow so much in July?

Q3

Q4. Send a summary email

NOTE: Check your inbox to see the summary email that would have been sent to the email address set it AI & ML » Agents » Sales_AI » Custom tools » Send_Email » recipient_email » Description: "If the email is not provided, send it to YOUR_EMAIL_ADDRESS_GOES_HERE".

Other Questions

Here are some other questions you may ask.

Which product categories perform best on social media?

What's the relationship between social media mentions and sales?

How do different regions respond to marketing campaigns?

Congratulations! You've successfully created a Snowflake Intelligence agent that can intelligently respond to questions by reasoning over both structured and unstructured data

What You Learned

You've learned how to create building blocks for creating a Snowflake Intelligence agent that can intelligently respond to questions by reasoning over both structured and unstructured data.

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