Analyze Using Dremio's AI Agent
Dremio's AI agent is built into the Dremio console and lets you work with your data end to end — from exploring and analyzing to troubleshooting — entirely in natural language, without writing a single line of SQL. The AI agent only operates on data and entities that you have the privileges to access.
To respond to your questions, the AI agent only accesses data and entities that you have privileges to access.
Open the AI Agent
You can access the AI agent in two ways:
- Type a question into the chat on the Home page in the Dremio console.
- Use the keyboard shortcut Cmd + Shift + G on Mac or Ctrl + Shift + G on Windows.
To use the AI agent, you must be granted CALL MODEL on the default model provider. See Configure Model Providers for setup instructions.
Use the AI Agent
Reference datasets with @ – Type @ in the chat box to trigger autocomplete and reference a specific table or view in your prompt. This helps the agent focus on the exact data you want to work with instead of searching across your entire catalog.
Audit the SQL – When the agent writes and executes SQL, you can expand the tool calls in the chat window to review exactly what query was run and how the results were generated.
What the AI Agent Can Do
Dremio's AI agent can help you across the full data workflow — from discovering and querying data to troubleshooting performance. Each section below includes example prompts to help you get the most out of the agent.
Discover and Explore
The AI agent helps you discover available data by breaking down table schemas, identifying relevant tables and views, and surfacing patterns in your data. The agent uses wikis and labels as well as sampling to determine relevance.
| Okay Prompt | Great Prompt |
|---|---|
| What tables can I use? | Which tables or views have customer location data? |
| How can I analyze time series data? | Which tables or views can I use to do a time series analysis on customer activity? |
What is the customer_activity table? | How is the customer_activity table structured, and what other tables does it relate to? |
Analyze
The AI agent writes and executes SQL on your behalf based on your natural language input and the information available from the AI Semantic Layer.
| Okay Prompt | Great Prompt |
|---|---|
| I want to see analysis of customer activity | I want to see an analysis of customer purchase activity by region, by customer type for each month of the year. |
| Which customers are the most valuable? | Which customers have spent the most with us over the lifetime of the relationship? |
Visualize
The AI agent generates visualizations based on your natural language input. You can specify chart type, axis requirements, grouping, and trendlines. Each visualization is accompanied by insights that explain the patterns the agent found.
Once a visualization is created, you can:
- Toggle between the chart and a table view of the underlying data
- Download the underlying data as a CSV
- Ask follow-up questions to refine or change the visualization
The agent supports these chart types: bar, line, area, scatter, pie, and heatmap. It can also create diagrams like flowcharts and entity-relationship diagrams when prompted or when it determines a diagram would be helpful.
| Okay Prompt | Great Prompt |
|---|---|
| Visualize the data | Visualize the data as a bar chart with month on the x axis and sum of purchase value as the y axis |
| Create a visual trendline showing me the activity | Create a visualization with a trendline showing customer activity by month |
Troubleshoot and Optimize
Ask the agent to review SQL queries or past jobs to identify bottlenecks and suggest optimizations. For more information, see Explain SQL and Explain Job.
Tips for Better Results
- Be specific – The more detail you provide, the better the response will be. Instead of "show me sales data," try "show me monthly sales by region for the last 12 months from the @customer_orders table."
- Include business context – Use the terms your team uses. The agent reads wikis and labels to understand your business terminology.
- Ask follow-up questions – The agent maintains context within a conversation. You can refine results with follow-ups like "break that down by product category" or "now show that as a line chart."
- Ask for explanations – If you're unsure about the results, ask "explain how you got this" or "show me the SQL you used."
- Verify the output – Generative AI can make mistakes. Always review SQL queries and data interpretations carefully.
Related Topics
- Discover Data – Find and explore data across Dremio in other ways.
- Configure Model Providers – Set up the AI model provider for your organization.
- Data Privacy – Learn more about Dremio's data privacy practices.