Build Faster, Work Smarter with AI Agents and Apps Grounded in Data | Snowflake Snowflake announced new capabilities at Expedition 2026 across three areas — Ground, Act and Compound — aimed at grounding AI agents in governed data, metrics and business logic. Snowflake said its Cortex Sense context-building tool and Analytical Search are entering public preview, Cortex Search row access and masking enforcement is in private preview, and its Arctic Embed 3S embedding model (generally available soon) improves retrieval quality over Arctic Embed L-v2.0 by 3% in English, 6% in multilingual retrieval and 23% for structured tables and columns. The announcements target a gap Snowflake cites from Deloitte's 2026 Tech Trends report: only 11% of organizations have agents in production while 38% are piloting them. Only 11% of organizations have agents in production despite 38% piloting them, according to Deloitte's 2026 Tech Trends report https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html . We see this gap between pilots and production deployments happening across industries and companies of all sizes: Teams build a compelling demo and deploy it against real data, only to have it confidently return the wrong revenue number or miss a policy constraint that a junior analyst would have caught. The problem is a lack of context. Agents need to know which metric definition is authoritative, which data is current, what permissions each user holds and what happened in the last conversation. Without that grounding, even the most capable model operates on guesswork. And even when agents get the answer right, it can be difficult to share those insights with the rest of the business. At Expedition 2026, https://www.snowflake.com/en/expedition Snowflake is announcing capabilities across three dimensions that strengthen how agents and apps run alongside your data, with full governance and context: - Ground: Connect agents to your governed data, metrics and business logic so they answer questions from authoritative definitions, not guesswork. - Act: Allow agents to execute real work following each user's own permissions, in the tools teams already use, with clear auditability. Quickly and securely ship Snowflake apps on governed data so people and agents can extract insights and take action. - Compound: Share proven skills, profiles and memory across the organization so every new agent starts off better than the last. Ground: Give every agent relevant data and business context from Day 1 The a16z research team put it plainly in their March 2026 analysis https://a16z.com/your-data-agents-need-context/ : "A crucial reason agents didn’t work well was a lack of proper data context." An agent answering the question "What was revenue growth last quarter?" can write competent SQL against a plausible-looking table yet return wrong numbers with full confidence because nobody told it which field carries the authoritative definition of “revenue.” Snowflake's approach starts from the principle that agents should inherit the context your organization has already built: the schemas, metrics, business logic, governance policies and permissions that define how your data works. Several new and enhanced capabilities make this practical. Snowflake Cortex Sense https://www.snowflake.com/en/blog/enterprise-ai-agents-grounded-context/ public preview soon autobuilds context from your existing schemas, metric definitions and business logic, so you don’t spend weeks setting up and defining connections. Cortex Sense works across your own data, shared data and data you purchase through Snowflake Marketplace — so agents understand the entire picture, not just one team's slice of it. When agents need to reason over large volumes of unstructured information, Analytical Search public preview lets them count, compare and find trends across thousands of documents at once, from contracts and support tickets to internal reports. This is not keyword search; Analytical Search applies AI functions across document collections to answer questions such as "How many contracts contain a price escalation clause?" or "Which support tickets mention the same root cause?" And because Cortex Search now enforces row access and masking policies on source tables private preview , the documents an agent can see follow the same rules as the structured data it queries. As agents move from finding documents to understanding them, Snowflake’s new embedding model Arctic Embed 3S generally available soon improves retrieval quality over Arctic Embed L-v2.0 by 3% in English, 6% in multilingual retrieval, and 23% for structured tables and columns , while keeping the same compact size class. As frontier models become commoditized, the competitive advantage shifts to how well a model understands your specific business. Organizations are turning to open-weight foundation models that are customized to their specific domain using post-training methods such as supervised fine-tuning and reinforcement learning. Snowflake Cortex Training http://www.snowflake.com/en/blog/cortex-training-fine-tune-open-weight-llms/ public preview makes it easy to posttrain and deploy open-weight models — Qwen, DeepSeek, Mistral, Gemma and others — on proprietary data using fully managed GPUs. Benchmarks show up to 61% lower cost than competing serverless offerings.