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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.

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.
note

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 PromptGreat Prompt
What tables can I use?Which tables or views have customer location data?

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 PromptGreat Prompt
I want to see analysis of customer activityI 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.

The agent supports the following chart types and diagrams:

  • Bar, line, area, scatter, pie, heatmap
  • Flowcharts and entity-relationship diagrams
Okay PromptGreat Prompt
Visualize the dataVisualize 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 activityCreate 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.

If the AI agent is unable to access a table or view, verify that you have the necessary privileges on that object. The agent can only operate on data you are authorized to access.

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.

Limitations​

  • Large tasks: The AI agent may not complete very large or highly complex tasks in a single session. For multi-step analyses, consider breaking your question into smaller parts and iterating across multiple prompts.