Core capabilities
AI agents in Lightdash allow you to:
- Ask questions in natural language - Simply type what you want to know about your data, like “What’s our total revenue by region?” or “Show me user growth over the last 6 months”
- Get instant visualizations - Receive bar charts, time series, and tables automatically generated based on your questions
- Explore interactively - Follow up with additional questions, drill down into specific data points, or request different chart types
- Maintain conversation context - AI agents remember your conversation history, so you can build on previous questions and refine your analysis
- Provide text-only responses - Get answers in natural language when visualizations aren’t needed
- Guide you to the right data - Direct you to the most relevant explores or tables for your questions
- Discover existing content - Find and share relevant charts and dashboards that have already been created in your project
- Generate complete dashboards - Create multiple related visualizations at once that tell a cohesive story about your data, perfect for executive summaries or thematic analyses
As mentioned earlier, Lightdash agents use the semantic layer defined in your dbt models to understand your data structure, relationships, and business logic.
This ensures that the AI generates accurate queries and visualizations based on your specific data context.
So, when an Agent generates an answer, the output is a semantic query, not SQL! This means that you can easily swap between the conversational AI interface and the standard Lightdash exploration experience.
Demo
Watch this comprehensive demo to see AI agents in action:
FAQs
- Does Lightdash store the query data?
Lightdash only stores simple one-line answers so you can look back at your conversation history. We also save the basic query info to recreate these when needed. The actual data and detailed results stays in your warehouse and gets pulled fresh when the results are revisited (unless data access is enabled).
- Why can’t I set multiple Agents for the same Slack channel?
Since you have to mention the Slack App for your organization, and to avoid unexpected results, we don’t allow multiple agents for the same slack channel. To align with best practices, we recommend one slack channel per project, so you prompt with confidence.
Known limitations
These limitations reflect the current state of AI agents as we continue
developing and improving the feature. Many of these constraints will be
addressed in future releases, so stay tuned! Your feedback and feature
requests help us prioritize what to build next.
Data analysis and calculations
As mentioned in the FAQs, AI Agents currently work with your dbt model metadata rather than actual data values. This means they can’t perform forecasting, predictive analytics or custom statistical calculations. They also can’t create table calculations or custom fields on-the-fly.
Query and visualizations constraints
Results are limited by configurable query limits set at server level to ensure good performance. These limits can only be adjusted through environment variables at the moment.
Agents can create tables, bar charts, vertical bar charts, line charts, scatter plots, pie/donut charts, and funnel charts, but don’t yet support custom visualizations or big number charts.
Data access and context
Agent access to your data is controlled thorugh tags in your dbt models. If certain fields aren’t accessible, check that they have the appropiate tags assigned to your agent.
Agents don’t remember context between different conversation sessions. Each chat start fresh.
