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As part of its aim of becoming a singular control center for agentic AI systems, Snowflake on Tuesday unveiled updates to its Snowflake Intelligence agent for data exploration and Cortex Code agent for generating code to build pipelines and applications.
To better enable agents and multi-agent networks built on proprietary data in Snowflake to reflect how an enterprise’s business actually operates, the new versions of Snowflake Intelligence and Cortex Code add connections to new data sources, enterprise systems and AI models in a unified manner.
New Snowflake Intelligence features include Skills, a tool that enables users to describe workflows using natural language and then executes the workflows on the users’ behalf, and new Model Context Protocol (MCP) connectors that enable the agent to interact with tools including Gmail, Google Calendar, Google Docs, Jira, Salesforce and Slack.
New Cortex Code features include support for third-party data systems such as AWS Glue and Databricks to enable users to work with data outside their Snowflake environment, plug-ins to AI systems through MCP and Agent Communication Protocol (ACP) to speed development, and tools that enable access to Cortex Code while in development environments such as VS Code and Claude Code.
As data platform providers such as Snowflake and Databricks, along with hyperscale cloud vendors AWS, Google Cloud and Microsoft all compete to develop complete AI ecosystems, the new Snowflake Intelligence and Cortex Code capabilities are beneficial to Snowflake users but not competitive differentiators for the vendor, according to David Menninger, an analyst ISG Software Research.
For example, he noted that Databricks offers Genie Code, an agent for generating code, and a VS Code extension.
“It’s a race among data platform vendors to build the largest AI ecosystems with the most capabilities to attract and retain users,” he said. “These are required investments but not necessarily unique features.”
However, beyond the individual capabilities themselves, Snowflake’s integration of governance and AI with data and AI workflows could distinguish it from competitors, according to Stephen Catanzano, an analyst at Omdia, a division of Informa TechTarget.
“While competitors like Databricks, AWS and Google Cloud offer similar tools, Snowflake’s focus on combining enterprise-grade governance with seamless AI integration across data, tools and workflows sets it apart,” he said. “Its agentic architecture and personalized AI agents are particularly innovative compared to more generic AI offerings from competitors.”
Based in Bozeman, Mont., but with a campus in Menlo Park, Calif., Snowflake is a data platform vendor that has made enabling AI development a focus of its product development over the past few years. Recently, Snowflake added more complete support for the Apache Iceberg table storage platform as part of a widening embrace of open source technology.
Building agents capable of making employees better informed, autonomously generating insights and taking on certain business processes has been the dominant trend in AI development over the past couple of years.
However, many enterprises have struggled to deliver enough high-quality, relevant data to agentic AI applications to trust their outputs and turn over workflows such as customer service, fraud detection and supply chain optimization.
Despite the rising interest in AI development and focus from data and AI providers on providing capabilities that simplify building agents and other AI tools, a July 2025 report from MIT found that 95% of organizations have not yet gotten any return on their investments in AI.
The Snowflake Intelligence and Cortex Code updates are designed to provide AI tools with greater context so they can be trusted to perform as intended, according to Will Allen, head of Snowflake Intelligence and agents.
“Time and time again, we hear from customers that the bottleneck for enterprise AI isn’t the models, but rather tapping into the data and context needed to make those models useful,” he said. “Business users want AI that actually gets work done, not just answers questions. Builders want to develop faster without stitching together multiple tools. Across both groups, the biggest challenge is context.”
Toward the end of providing agents with proper context, beyond Skills and new MCP connectors, the Snowflake Intelligence update includes the following:
“The Snowflake Intelligence update transforms AI from a passive tool into an active, personalized agent that adapts to individual workflows,” Catanzano said. “This enables users to automate routine tasks, interact seamlessly with enterprise tools and gain deeper insights through multi-step reasoning. The ability to personalize and reuse workflows makes it a game-changer.”
Perhaps the most significant new Snowflake Intelligence feature is Skills, given that it eliminates repetitive manual tasks and broadens access to advanced AI capabilities, he continued.
Menninger, meanwhile, noted the value of the mobile app given that nearly all analytics providers offer a mobile app and users are not always at their desktops when decisions are made. Meanwhile, regarding the update as a whole, he added that the new capabilities further Snowflake’s goal of becoming the control center for AI by eliminating the need to integrate certain capabilities from third parties.
“Data platform vendors are trying to control more of the activities associated with data,” Menninger said. “One of the ways they are doing that is bypassing the need for separate analytics tools [with] new capabilities including creating automated workflows, deep research and a mobile app. They are also providing MCP connections to additional productivity tools.”
In addition to support for new data sources, new plug-ins through MCP and ACP and deeper integrations with development environments, new Cortex Code capabilities include an Agent Software Development Kit that enables users to integrate Cortex Code’s capabilities into their own applications and Cortex Code in Snowsight to give Cortex Code its own cloud environment.
Menninger noted that coding tools necessitate connections to be effective. As a result, the Cortex Code update includes valuable capabilities.
“Coding tools live in a connected world,” Menninger said. “They are used to build applications with data from a variety of sources [and] connect to a variety of applications. Developers have a variety of libraries and frameworks they work with. In short, the new Cortex Code capabilities create a larger ecosystem in which Snowflake developers can operate.”
Catanzano similarly noted that the Cortex Code update adds value for users, enabling developers to build, orchestrate, and operationalize AI applications within their existing tools and environments.
“By supporting multi-system data environments and providing native development experiences, it accelerates the transition from experimentation to production, making AI more accessible and actionable for enterprises,” he said.
With Snowflake Summit, the vendor’s annual user conference, scheduled for early June, Snowflake’s product development initiatives continue to focus on broadening agentic AI capabilities beyond query-and-response to include taking action, according to Allen. In addition, becoming the single platform for controlling AI and interoperability are points emphasis.
“[We have] the idea of data becoming the control plane for AI, with enterprises realizing that success depends on having a unified, governed data foundation with strong business context,” Allen said. “We’re also leaning into … making it easier for customers to work across models, tools and systems without lock-in.”
Adding more interoperability would be wise, according to Menninger, who noted that both the Snowflake Intelligence and Cortex Code updates broaden the vendor’s ecosystem for data and AI.
“Snowflake should continue building out its ecosystem, and these enhancements will help address some of its shortcomings for developers and data scientists,” he said. “Additional tools to address the entire agent lifecycle would also help more enterprises move their agentic applications into production.”
Catanzano likewise noted that Snowflake should continue focusing on interoperability. In addition, he suggested that the vendor keep adding industry-specific capabilities and enhancing those it already provides.
“To continue serving users and attracting new ones, Snowflake could focus on expanding its ecosystem by integrating with more third-party tools and platforms,” Catanzano said. “Additionally, enhancing real-time collaboration features and providing more industry-specific AI solutions could further solidify its position as a leader in enterprise AI.”
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.
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