Snowflake Expands AI Agent Capabilities to Handle Enterprise Data and Workflows
Snowflake on Tuesday released updates to its AI agents for data exploration and code generation, adding connections to enterprise systems and external data sources as the company works toward becoming a central control point for AI operations.
The updates to Snowflake Intelligence and Cortex Code add support for Gmail, Google Calendar, Google Docs, Jira, Salesforce, Slack, and external data systems like AWS Glue and Databricks. A new feature called Skills lets users describe workflows in plain language and have the system execute them automatically.
The moves reflect a broader industry competition among data platform vendors to build complete AI ecosystems. Databricks, AWS, Google Cloud, and Microsoft are all racing to offer more integrated capabilities to attract and retain customers.
The Context Problem
Enterprises struggle to deliver enough high-quality, relevant data to make AI agents trustworthy enough to handle critical workflows like customer service and fraud detection. A July 2025 MIT report found that 95% of organizations have not yet seen returns on their AI investments.
Will Allen, head of Snowflake Intelligence and agents, said the bottleneck isn't the models themselves. "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.
The new Snowflake Intelligence features include a mobile app for data exploration, multi-step reasoning across structured and unstructured data, and continuous learning from user interactions. An Artifacts feature lets users share analyses and workflows.
Cortex Code Gets Developer Tools
The Cortex Code update adds integrations with development environments including VS Code and Claude Code. New capabilities include an Agent Software Development Kit for building custom applications and support for third-party data systems.
David Menninger, an analyst at ISG Software Research, said the individual features aren't necessarily unique differentiators. "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."
Stephen Catanzano, an analyst at Omdia, said Snowflake's integration of governance with AI workflows could distinguish it from competitors. "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.
Building for Production
Menninger noted that coding tools need broad connections to be effective. The Cortex Code updates address this by supporting multiple data sources and development frameworks.
Catanzano said the updates help accelerate the transition from experimentation to production. "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.
Snowflake's product focus continues to shift from query-and-response capabilities toward agents that take action. The company plans to emphasize data as a control plane for AI and cross-platform interoperability at its annual user conference in early June.
Menninger said Snowflake should continue building its ecosystem. "Additional tools to address the entire agent lifecycle would also help more enterprises move their agentic applications into production," he said.
For product development teams, understanding how AI for Product Development intersects with data platforms is increasingly critical. Similarly, Generative Code capabilities are becoming standard expectations in development tools.
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