Snowflake CoWork Is Only as Smart as Your Data

Business users have wanted to ask their data a question in plain English for a long time. With Snowflake CoWork, formerly Snowflake Intelligence, they can. CoWork is Snowflake's agentic application with a conversational interface. People use it at ai.snowflake.com or on iOS to ask questions and get answers grounded in their company's own data.

The chat window is the easy part. Whether the answers are right depends on what sits underneath. That is where most data teams have work to do.

What sits under the conversation

CoWork runs on Cortex Agents. When someone asks a question, an agent plans the answer and calls the right tool:

• Cortex Analyst turns natural language into SQL. That SQL runs against semantic views over your structured data.

• Cortex Search retrieves relevant passages from unstructured content such as documents and policies.

• Custom tools are user-defined functions or stored procedures that carry your own business logic.

All of this runs inside Snowflake's governance model. CoWork "automatically inherits and respects all existing Snowflake data governance controls, including row-access policies and column-level security," and runs with each user's own privileges.

How Snowflake CoWork answers a question: agent, three tools, governed data

The agent picks a tool for each question, and every tool reads the same governed data. The quality of that data sets the ceiling on answer quality.

Three things that decide whether the answers are right

1. The data has to be in Snowflake, and it has to be modeled. An agent can only reason over what it can query. If your sales history sits in a legacy Oracle warehouse, your inventory in an ERP and your forecasts in spreadsheets, the agent sees part of the picture at best. Landing raw copies is not enough either. Curated, reconciled tables answer "what were net sales by region last quarter?" far better than a pile of staging tables.

2. Semantic views carry the business meaning. A semantic view is a schema-level object. It stores business concepts such as facts, dimensions, metrics and the relationships between tables directly in the database. Snowflake says semantic views "address the mismatch between how business users describe data and how it's stored in database schemas," and that they "improve accuracy by combining LLM reasoning with rule-based definitions." This is where you define what "net sales" means, which joins are valid, and which verified example questions the model should learn from. Snowflake recommends semantic views for new implementations.

3. Governance comes before rollout, not after. Because CoWork respects existing roles, row-access policies and masking, the access model you already have decides what each person can see. Review it before you open the chat window to a wider audience. That way the first surprising answer is a modeling question, not a security incident.

A practical readiness checklist

• Land the core business domains in Snowflake, modeled and validated, not only raw copies

• Collect the questions business users actually ask today, in their own words

• Build a semantic view for one domain first: metrics, synonyms, joins and verified queries

• Review roles, row-access policies and masking for the people who will use CoWork

• Add a Cortex Search service where answers live in documents rather than tables

• Pilot one agent with a small group, then expand domain by domain

• Watch warehouse usage during the pilot. Snowflake notes that "additional warehouse costs apply when you execute the SQL"

Where Pingahla helps

Pingahla is a Snowflake and AWS partner specializing in modern data architecture, migration and analytics enablement. Most of the work that makes conversational analytics trustworthy happens before the first question is asked. That means moving data off legacy platforms, building ELT pipelines and testing and validating the result.

Planning to put Snowflake CoWork in front of your business users? Talk to Pingahla about getting your data ready first.