How to Build Snowflake AI Agent

Business teams can benefit from the data stored in data warehouses such as Snowflake. But business users lack the technical skills necessary to take advantage of this data. This leaves business users without the insights they need.

However, with the emergence of AI agents, business users can now use natural language prompts to extract data and insights from Snowflake. This means business users don’t have to know SQL to leverage Snowflake.

In the following blog, we’ll show you how to build a Snowflake AI agent, which allows business users to extract data and insights from Snowflake without coding.

Data Warehouses: Challenges for Business Users

Data warehouses have become essential for businesses looking to harness the power of their data, but many business users still face significant challenges when trying to tap into these resources. 

One of the primary hurdles is the complexity of data warehouse structures. Unlike straightforward spreadsheets or flat databases, data warehouses often contain highly normalized, multi-dimensional data spread across numerous tables and schemas. 

This structure is designed to optimize storage and analytical performance, but it can be intimidating for non-technical users unfamiliar with complex joins, foreign keys, and data relationships. Without the right training or a strong data engineering team, extracting actionable insights from this data can feel like navigating a labyrinth.

Another common issue is the disconnect between business context and data organization. Data warehouses are typically built by IT and data teams with a focus on technical efficiency rather than business relevance. This means that the naming conventions, data hierarchies, and categorization used in these systems can differ significantly from the language and priorities of business users. 

For instance, a marketing manager trying to assess customer churn might struggle to locate the right data if it’s spread across multiple tables without clear, business-centric labels. This disconnect can lead to frustration, delays, and a reliance on technical intermediaries, ultimately slowing down decision-making.

Finally, the sheer scale and complexity of data in modern warehouses can be overwhelming. With thousands of tables and billions of rows, finding the right data without a precise understanding of the underlying structure is challenging. 

Additionally, data quality issues, outdated information, and inconsistent formats can further complicate analysis. Without robust data governance and user-friendly tools to guide them, business users often struggle to extract meaningful insights, leading to missed opportunities and underutilized resources.

AI Agents: Access Data Warehouses with Natural Language

AI agents are changing the way business users interact with data warehouses, making data more accessible and actionable. By automating complex data retrieval tasks, these intelligent systems bridge the gap between technical data structures and business insights. 

For example, natural language processing (NLP) capabilities allow business users to simply ask questions like, "What were our top 10 products last quarter?" without worrying about complex SQL queries or understanding the intricacies of data schemas. 

Moreover, AI agents can provide context-aware insights by understanding the specific needs of different business functions. They can learn and adapt to industry-specific terminology, user preferences, and organizational goals, ensuring that the data presented is both relevant and meaningful. 

AI agents also play a critical role in enhancing data quality and governance. They can automatically detect anomalies, correct formatting inconsistencies, and flag outdated information, ensuring that the data used for analysis is accurate and reliable. 

This proactive approach reduces the time business users spend cleaning data, allowing them to focus on generating insights and creating value. Additionally, AI agents can surface insights that might have been overlooked in traditional, manual analysis, unlocking hidden opportunities and driving better business outcomes.

Snowflake AI Agent: How to Build

Here’s how to build the Snowflake AI Agent. First, make sure to sign up for a free StackAI account. 

Navigate to the account dashboard. Click ‘New Project’.

Click the ‘Workflow Builder’ option.

From here, choose the Snowflake AI Agent template. 

The template will launch a pre-built workflow for the AI agent.

Let’s take a look at each component of the workflow. First, we have the input node. This is where the user will enter their natural language question. This is the query you will ask the Snowflake data warehouse. 

Next, you have the Text-to-SQL node. This will convert the natural language question into a SQL query. Note: you can choose the SQL language you want to convert into, in this case, Snowflake. 

The Snowflake node allows you to connect to your Snowflake instance.  

Click the gear icon to enter the settings menu.

Now click “New connection”. Enter the credentials for your Snowflake data warehouse into the fields.

The LLM runs the SQL query against Snowflake. This will answer the user’s initial question. The LLM is Anthropic — Claude 3.5 Sonnet.

The result is displayed in the Output box. This is the answer to the user’s initial natural language question.

Now go to the Export tab.

Give the AI agent a name and a description.

Click on the link to launch the web app. This allows you to use the AI agent in your browser. Now ask a question.

In this case, the answer returned from Snowflake is 470 products. 

Launch the Snowflake AI Agent Now!

Business users have difficulty accessing data warehouses such as Snowflake because they do not have the technical skill to do so.

But with AI agents, business users can ask questions in plain English instead of writing them out as SQL queries. This enables business users of any background to retrieve data from Snowflake. 

Sign up for a free account with StackAI to launch the Snowflake AI Agent now! It’s available as a pre-built template.