Artificial Intelligence advancements are moving very fast for analytics builders and information consumers. Traditionally, business users depended on analysts and data teams to answer questions buried within complex databases. With Snowflake CoWork, organizations have a new path to create AI-powered agents that understand natural language and provide answers directly from enterprise data. Layering LLMs on top of data directly presents a host of challenges that Snowflake has approached throughtfully.
In this article, I’ll walk through my experience creating my first Snowflake CoWork agent using Snowflake CoWork and demonstrate how quickly you can build an AI assistant on top of your Snowflake data.
What is Snowflake CoWork?
Snowflake CoWork is an AI workspace that allows users to:
- Chat with enterprise data using natural language
- Connect structured and unstructured data sources
- Build specialized AI agents
- Generate insights without writing SQL
- Enable business users to self-serve analytics
Instead of asking a data analyst:
“Can you tell me why sales increased in July?”
You can simply ask your AI agent directly.
Why Snowflake CoWork Matters
Many organizations struggle with:
- Data scattered across multiple systems
- Long wait times for analytics requests
- Non-technical users unable to query data
- Knowledge trapped inside reports and dashboards
Snowflake CoWork bridges this gap by allowing AI agents to understand business context and retrieve answers from trusted enterprise data sources.
Setting Up the Environment
To get started, I followed Snowflake’s official Snowflake Intelligence quickstart guide.
The setup script automatically creates:
- Database and schemas
- Sample sales data
- Marketing campaign data
- Product catalog information
- Social media metrics
- Semantic models for business understanding
The result is a fully functional AI-ready environment.
Creating My First Agent
After completing the setup, I created a simple agent named Sales_AI. This agent was connected to a semantic model called:
SALES_AND_MARKETING_DATA
This semantic model allows the AI agent understand business concepts such as:
- Products
- Revenue
- Units Sold
- Marketing Campaigns
- Social Media Activity
This blended data pulls data from different sources with different grains. This is where data stewardship, subject matter expertise matter the most.
Instead of thinking in tables and columns, the agent understands business terminology. In this specific case, I had known data, a clear data dictionary, and established semantics from years of building similar reports. We have learned that AI produced semantics can be generic.
The Snowflake CoWork Interface
Here’s the agent running inside Snowflake CoWork:
In my environment, I created the Sales_AI agent and started interacting with it using natural language questions. The interface provides:
- Chat-based interaction
- Agent details panel
- Connected data objects
- Context review
- Retrieval transparency
One feature I particularly liked is that Snowflake shows what context the agent reviewed before generating an answer. This execution plan provides guidance and explainaiblity and signals for improvement. I am working on processes that help me automate this process.
Asking Business Questions
Once the agent was created, I started asking business questions.
Example:
Why did sales of Fitness Wear grow so much in July?
The agent automatically:
- Interprets the question
- Identifies relevant datasets
- Generates the required SQL
- Retrieves the data
- Produces a business-friendly explanation
This removes new analysts to figure out where and how to answer basic questions. For self service, getting these agents to production requires a much deeper level of validation, refinement team readiness.
Understanding Agent Context
During testing, I asked:
What issues are reported with jackets recently in customer support tickets?
The response revealed something important. The agent correctly explained that its current semantic model only included:
- Marketing campaign metrics
- Product catalog
- Sales transactions
- Social media activity
It did not have access to customer support ticket data. This demonstrates a key strength of Snowflake CoWork. The AI experience will not simply hallucinate answers. Instead, it understands its available data sources and explains its limitations when the requested information is unavailable.
Structured Data vs Unstructured Data
Snowflake CoWork becomes even more powerful when combining:
Structured Data
Examples:
- Salesforce opportunities
- Revenue
- Campaign performance
- Product sales
Unstructured Data
Examples:
- Support tickets
- Emails
- Meeting transcripts
- Knowledge base articles
- Customer feedback
By combining both, an organization can ask questions such as:
Why are sales declining for Product A?
The AI can correlate:
- Revenue trends
- Marketing performance
- Customer complaints
- Support tickets
- Social media sentiment
All within a single conversation.
Real-World Use Cases
Sales Analytics
Ask:
Which region generated the highest revenue last quarter and how are we pacing this quarter?
Marketing Performance
Ask:
Which campaign produced the best lead to revenue conversion rate?
Customer Support
Ask:
What are the most common complaints this month?
Key Takeaways
After building my first Snowflake CoWork agent, here are my biggest observations:
Extremely Fast Setup
The quickstart guide gets you running within minutes.
Business-Friendly Experience
Users interact through conversation rather than SQL.
Transparency
The platform clearly shows what data sources were used.
Strong Foundation for Enterprise AI
By combining semantic models, Cortex Analyst, Cortex Search, and Snowflake Intelligence, organizations can create powerful AI assistants on top of governed enterprise data.
Final Thoughts
Rather than navigating dashboards, reports, and database schemas, users can simply ask questions and receive answers grounded in enterprise data. Deploying agents has already reduced dashboard requests.
My first experience building the Sales_AI agent showed how quickly we can assemble raw data to AI-powered business insights.
As Snowflake continues investing in semantic models management, Cortex Analyst, Cortex Search, and Intelligence Agents, the future of enterprise analytics is becoming increasingly conversational and approachable for non-BI / analytics professionals to build.
If you’re already using Snowflake, I highly recommend spending an hour with the Snowflake Intelligence quickstart. It’s one of the fastest ways to understand where AI-powered analytics is heading. For more learning how to move from testing to production, check out some of the real-world production learnings deploying Snowflake Agents.
