Use AI Assistance to build dashboards faster and explore data more effectively.
AG Studio includes an AI Assistant that helps create Widgets, configure Filters, and arrange layouts. The Assistant understands the data structure and makes suggestions based on natural language requests, reducing manual configuration and enabling users to focus on analysis rather than dashboard building.
The AI Assistant is an experimental feature. Behaviour will be slightly different depending on the LLM being used, and results can vary across queries.
Opening the AI Panel Copy Link
The AI Assistant Panel is accessible from the toolbar on the right side of the Studio. Clicking the AI icon opens or closes the panel. When open, it appears as a conversational interface alongside the dashboard. For an overview of how the panel is organised, see User Interface.
Threads Copy Link
Each conversation lives in a thread. Multiple threads can be created — for example, one for building a sales dashboard and another for exploring inventory data. Threads are saved as part of the dashboard state, so conversations persist between sessions if state persistence is enabled.
To start a new thread, click the New button in the AI panel header. To switch between threads, click the thread name and select from the list.
Messages are sent using the input field at the bottom of the panel. Press Enter to send, or Shift+Enter to insert a new line without sending.
Asking Effective Questions Copy Link
The AI Assistant responds to natural language requests like:
- "Create a dashboard with KPIs for total revenue, average order value, and customer count"
- "Add a bar chart showing sales by product category"
- "Show me [Field] by [Category]"
- "What are the trends in [Measure] over time?"
- "Move the pie chart to the top right corner"
- "Add a date filter for the last 12 months"
More specific requests produce better results. Instead of "Make me a dashboard", try "Build a sales dashboard showing revenue by region, a top-10 products table, and monthly sales trends". Referencing actual Field names helps the Assistant understand the data more precisely.
Complex dashboards often build better through multiple requests rather than trying to describe everything at once. When the first result does not match expectations, describing the adjustment needed is more effective than starting over — for example, "Change the bar chart to a line chart" or "Add a quarterly breakdown".
Understanding the Assistant's Progress Copy Link
When a message is sent, the Assistant works through the request in stages. A thinking indicator is shown while it is processing. For complex requests, the Assistant may create a structured plan before executing it, and may delegate sub-tasks to specialised agents for layout, widget configuration, and data querying. When finished, the Assistant summarises what was done.
Limitations and Considerations Copy Link
The AI Assistant works best when the data structure is well documented with clear Field names and descriptions, and when requests are framed in business terms rather than technical terms. The Assistant can only work with the data sources currently loaded in the Studio.
The Assistant may not always choose the most optimal visualisation on the first attempt, and unusual or complex business logic may need to be explained explicitly. Response times and output quality depend on the AI provider configured by the application. Refinement through follow-up requests is a normal part of the workflow.