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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.

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Using AI agents in Slack

Connect your AI agents to Slack for collaborative data analysis and team-wide insights sharing, here’s how:
  1. Select or create an AI agent in your Lightdash instance
  2. Add the Slack integration in your organization settings
  3. Under ‘Integrations’, add the channel you want to use
  4. Tag your Slack App in the channel you want to use
  5. Start asking questions like “What kind of data can you access?” or “Show me total order amount over time”
  6. Get instant results directly in Slack
You can also summon the bot on a thread to continue the conversation. In order for the bot to be able to respond, you need enable this context sharing in your Lightdash Integrations settings.

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Demo

Watch this comprehensive demo to see AI agents in action:

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Providing feedback on responses

AI agents can improve over time with your feedback. After receiving an answer from your agent, you can provide feedback to help improve future responses: How to provide feedback:
  1. Look for the thumbs up/down icons on any agent response
  2. Click thumbs down if the answer wasn’t helpful or accurate
  3. Optionally provide detailed feedback explaining what went wrong
  4. The agent will use your feedback when you follow up with additional questions in the same conversation
  • Lightdash
  • Slack
Feedback interface in Lightdash web application

Feedback in Lightdash web app

Your feedback is valuable for:
  • Immediate improvement - When you follow up after providing feedback, the agent uses your feedback to adjust its next response
  • Identifying common issues - Helps spot patterns in misunderstandings or incorrect interpretations
  • Long-term learning - Improves the agent’s understanding of your data and business context over time
The more specific your feedback, the more helpful it is. Instead of just clicking thumbs down, try to explain what was wrong (e.g., “Used the wrong metric”, “Chart type doesn’t match the question”, “Missing important filter”).

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FAQs

  1. 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).
  1. 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.

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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.

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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.

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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.

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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.