Snowflake Intelligence gains automation features, while Cortex Code will be able to access more data sources in more ways.
Snowflake is enhancing Snowflake Intelligence and Cortex Code to create a unified experience connecting enterprise systems, data sources, and AI models with Snowflake data. It’s part of the company’s vision to become the control plane for the agentic enterprise, enabling enterprises to align data, tools, and workflows with AI agents built on its platform.
With these updates, the company said, Snowflake Intelligence becomes an adaptable personal work agent for business users, and Cortex Code expands as a builder layer for enterprise AI that provides governed, data-native development.
Enhancements to Snowflake Intelligence include automation of routine tasks by describing them in natural language, new Model Context Protocol (MCP) connectors, and reusable artifacts that let users save and share analyses, visualizations, and workflows, all of which will be generally available “soon.” In addition, a new iOS mobile app, and multi-step reasoning with deep research that uses agentic architecture to reason across data will soon be in public preview.
The company said that all of these updates came out from customer feedback, as well as from insights gleaned from Project SnowWork, last month’s preview of an autonomous AI layer for its data cloud.
Cortex Code now supports additional external data sources, including AWS Glue, Databricks, and Postgres, connectivity with other AI agents via MCP and Agent Communication Protocol (ACP), a Claude Code plugin, and a new agent software development kit with support for Python and TypeScript. There are also enhancements to Cortex Code in Snowsight, Snowflake’s web interface, including Plan Mode to allow developers to preview and approve workflows, and Snap & Ask to enable interaction with data artifacts such as charts and tables.
Snowflake also announced the private preview of Cortex Code Sandboxes in Snowsight, a dedicated cloud environment where developers can execute code end-to-end with no setup.
Michael Leone, VP & principal analyst at Moor Insights & Strategy, thinks the roadmap is “ambitious,” noting the number of items announced that are “coming soon” or are in public preview. “These announcements are starting to blur together, with almost every vendor claiming their agents can reason, act, and transform the business,” he said, adding, “What makes this one worth slowing down on, at least for me, is that Snowflake is going after both halves of the enterprise at the same time. Intelligence is built for the business users who want answers and actions without writing SQL, and Cortex Code is built for the builders who actually have to put this into production.”
Most vendors pick one target, users or builders, and come back to the other later, he said, but Snowflake is putting both on the same governed data foundation. “[This] is a harder engineering problem, but I’d argue it’s a cleaner answer to the question enterprises are actually asking, which is how to open AI up to more people without losing control of the data underneath,” he said, noting that Snowflake has changed its approach from “let’s do it inside Snowflake,” to realizing that agentic AI only works if it’s interoperable with the rest of the stack.
Igor Ikonnikov, advisory fellow at Info-Tech Research Group, also sees the control plane play as part of an industry trend. “As always, the devil is in the details: what those platforms are composed of and how they offer to control AI agents,” he said. “Most platforms are built the old-fashioned way: All the controls are coded. Snowflake speaks about reusable analytics through saving the whole solution and reusing complete modules or models. It means that common semantics are still buried inside database models and code.”
All AI vendors are motivated by the same demand from the market, he said: “Move from Copilot-based generic chatbots to business-purpose-specific AI agents that understand business logic and can interact with one another.” With these updates, he sees Snowflake as having caught up with the competition, but not yet surpassing it.
Sanjeev Mohan, principal at SanjMo, said, “The good news for customers is the support for Databricks and AWS Glue. What Snowflake is saying is that even if your data lives in a competitor’s system, Snowflake AI coding agent can be used. And vice versa, the VS Code extension and Claude Code plugin can be used on Snowflake data. In other words, it reduces vendor lock-in fears.”
It’s also the right strategic direction, said Sanchit Vir Gogia, chief analyst at Greyhound Research. “Enterprise AI is moving from generation to orchestration to execution, and Snowflake’s focus on governed data as the foundation for action aligns with that shift,” he said.
“However, becoming the execution layer for enterprise AI requires more than integrating agents and expanding tooling,” he said. It also requires consistent semantics, reliable cross-system execution, strong governance, economic viability, and organisational readiness, as well as overcoming a structural constraint. “Control without ownership of the systems where work is executed introduces dependency that is difficult to fully resolve. This is the central tension in Snowflake’s strategy and will define how far it can realistically extend its influence,” he said. “Snowflake has taken a meaningful step in that direction. It has not yet proven that it can deliver this at scale. At this stage, it is one of the most credible contenders in a race that will be defined not by who builds the smartest AI, but by who can make that AI work reliably inside the enterprise.”
