Snowflake’s ‘data agents’ leverage enterprise apps so you don’t have to

Today, data ecosystem giant Snowflake kicked off its BUILD developer conference with the announcement of a special new offering: Snowflake Intelligence.

Set to launch in private preview soon, Snowflake Intelligence is a platform that will help enterprise users set up and deploy dedicated ‘data agents’ to extract relevant business insights from their data, hosted within their data cloud instance and beyond, and then use those insights to take actions across different tools and applications, like Google Workspace and Salesforce.

The move comes as the rise of AI agents continues to be a prominent theme in the enterprise technology landscape, with both nimble startups and large-scale enterprises (like Salesforce) adopting them. It will further strengthen Snowflake’s position in the data domain, leaving the ball in rival Databricks’ court to come back with something bigger. 

However, it is important to note that Snowflake isn’t the very first company to toy with the idea of AI agents for improved data operations.

Other startups including Redbird, Altimate AI and Connecty AI, are also exploring with the idea of agents to help users better manage and extract value (in the form of AI and analytical applications) from their datasets. One key benefit of Snowflake’s is that the agent creation and deployment platform will live within the same cloud data warehouse or lakehouse provider, eliminating the need for another tool.

What to expect from Snowflake’s data agents?

Ever since Neeva AI CEO Sridhar Ramaswamy took over as CEO, Snowflake has been integrating AI capabilities on top of its core data platform to help customers take advantage of all their datasets, without running into technical complexities. 

From the Document AI feature launched last year to help teams extract data from their unstructured documents and to fully-managed open LLM solution Cortex AI to Snowflake Copilot, an assistant built with Cortex to write SQL queries in natural language and extract insights from data, Snowflake has been busy adding such AI features.

However, until now, the AI smarts were only limited to working with the data hosted within users’ respective Snowflake instances, not other sources.

How Snowflake Intelligence data agents work

With the launch of Snowflake Intelligence, the company is expanding these capabilities, giving teams the option to set up enterprise-grade data agents that could tap not only business intelligence data stored in their Snowflake instance, but also structured and unstructured data across siloed third-party tools — such as sales transactions in a database, documents in knowledge bases like SharePoint, information in tools like Slack, Salesforce, and Google Workspace. 

According to the company, the platform, underpinned by Cortex AI’s capabilities, integrates different data systems with a single governance layer and then uses Cortex Analyst and Cortex Search (part of Cortex AI architecture) to deploy agents that accurately retrieve and process specific data assets from both unstructured and structured data sources to provide relevant insights.

The users interact with the agents in natural language, asking business-related questions covering different subjects, while the agents identify the relevant internal and external data sources, covering data types like PDFs, tables, etc., for those subjects and run analysis and summarization jobs to provide answers.

But that’s not all. Once the relevant data is surfaced, the user can ask the data agents to go a step further and take specific actions around the generated insights.

For instance, a user can ask their data agent to enter the surfaced insights into an editable form and upload the file to their Google Drive. The agent would immediately analyze the query, plan and make required API function calls to connect to the relevant tools and execute the task. It can even be used for writing to Snowflake tables and making data modifications.

We’ve reached out to Snowflake with specific questions about these data agents, including the breadth of data sources they can cover and tasks they can (or cannot) execute, but have not heard from the company at the time of writing.

It also remains to be seen how quickly and easily users can create and set up these data agents. For now, the company has only said it only takes a “few steps” to deploy them.

Baris Gultekin, the head of AI at Snowflake says the unified platform “represents the next step in Snowflake’s AI journey, further enabling teams to easily, and safely, advance their businesses with data-driven insights they can act on to deliver measurable impact.”

No word on widespread availability 

While the idea of having agents that could answer questions about business data and then take specific actions with the generated insights to do organizational work sounds very tempting, it is pertinent to note that the capability has just been announced yet.

Snowflake has not given a timeline on its availability. It only says that the unified platform will go into private preview very soon.

However, the competition is intensifying fast, including from AI model provider startups such as Anthropic with its new Computer Use mode, giving users more options to choose from when it comes to turning autonomous agents loose on business data, and completing tasks from a user’s text prompt instructions.

The company also notes that Snowflake Intelligence will be natively integrated with the company’s Horizon Catalog at the foundation level, allowing users to run agents for insights right where they discover, manage and govern their data assets. It will be compatible with both Apache Iceberg and Polaris, the company added. 

Snowflake BUILD runs from November 12 to 15, 2024.