AI agents

AI agents transform the way you interact with your data by allowing you to ask questions in natural language and get answers back. Whether you’re exploring data in Lightdash or collaborating with your team in Slack, AI agents make data analysis as simple as having a conversation.

AI agents automatically select the most relevant data models and metrics to answer your questions, build and execute queries with appropriate dimensions, metrics, and filters, and present results in the most insightful format.

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Get started

Getting started with AI agents is simple - you can begin using them right away on any project in your Lightdash instance.

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Creating your first AI agent

1. Find the “Ask AI” button in your project - this will be your entry point to AI agents

2. Create a new agent (only admins and developers can create new AI agents)

3. Configure your agent:

   • Name and image - Give your agent a memorable name and visual identity
   • Instructions (optional) - Provide context about your models, tables, or specific use cases to help the AI give more relevant responses
   • Tags (optional) - Use tags to control which metrics and dimensions the agent can access

Once set up, you can start asking questions immediately! Try asking “What kind of data can you access?” to get started.

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What it can do

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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

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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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Best practices

To get the most accurate and useful answers from your AI agents, follow these best practices for preparing your data and configuring your agents.

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Think specialized, not general

Think of AI agents as your specialized analysts - each one can be configured to focus on specific areas of your business. For example, you might create a “Marketing Assistant” that only has access to marketing data like campaign performance, lead generation, and customer acquisition metrics. This focused approach ensures more accurate, relevant responses and prevents sensitive data from being accessible to the wrong teams. To find out more about how to configure specific access, see Limiting access to specific explores/fields.

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Document your data thoroughly

Good documentation is crucial for AI to understand your data models and provide meaningful insights. The quality of the results depend on the quality of your metadata and documentation.

• Write clear, descriptive names for metrics and dimensions
• Add detailed descriptions to all metrics and dimensions explaining what they represent
• Include example questions in descriptions that AI could answer with the metric

Remember: If your colleague wouldn’t understand your documentation, neither will the AI agent. The more context you provide, the better the AI can interpret and analyze your data.

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Set up multiple agents

You can create multiple AI agents, each configured for different tasks, tones, languages, or teams. Each agent can have access to different datasets to focus results and give more accurate answers.

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Limiting access to specific explores/fields

For each AI agent, you can configure which fields are used to answer questions using the tags you’ve defined in your YAML files. You can add one or many tags - fields with any of the tags in the list will be considered by the AI agent. Use tags to control which metrics and dimensions each AI agent can access. This helps focus the AI on the most relevant data for analysis and ensures agents only work with appropriate datasets. You can add tags at the model level to give access to entire explores, or at the individual metric and dimension level for more granular control.

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Adding tags at the model level

Tag entire models to give your AI agent access to all metrics and dimensions within that explore:

models:
  - name: marketing_campaigns
    meta:
      tags: ['marketing', 'ai']  #    <--------- tagging the entire model
    columns:
      - name: campaign_name
        meta:
          dimension:
            type: string
      - name: impressions
        meta:
          metrics:
            total_impressions:
              type: sum

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Adding tags to individual metrics & dimensions

For more granular control, tag specific metrics and dimensions:

- name: orders
  columns:
    - name: status
      meta:
        dimension:
          tags: ['ai', 'sales'] #    <--------- tagging the dimension
     - name: location
      meta:
        dimension:
          tags: ['ai', 'operations'] #    <--------- adding many tags
    - name: amount
      description: Total amount of the order
      meta:
        metrics:
          total_order_amount:
            type: sum
            format: usd
            round: 2
            tags: ['ai', 'finance'] #    <--------- tagging the metric

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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.

2. 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.