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AI Agents in the Lightdash app will follow row-level, column-level, and table-level data access based on user attributes.In Slack, the AI will have the user attributes of the user who set up the agent. We plan to respect user attributes based on Slack user email in the future, reach out if you need that feature!
Get started
Getting started with AI agents is simple - you can begin using them right away on any project in your Lightdash instance.Creating your first AI agent

- Find the “Ask AI” button in your project - this will be your entry point to AI agents
- Create a new agent (only admins and developers can create new AI agents)
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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
- User and Group Access - Control which users and groups can access your agent through the agent settings

What it can do
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
Using AI agents in Slack
Connect your AI agents to Slack for collaborative data analysis and team-wide insights sharing, here’s how:- Select or create an AI agent in your Lightdash instance
- Add the Slack integration in your organization settings
- Under ‘Integrations’, add the channel you want to use
- Tag your Slack App in the channel you want to use
- Start asking questions like “What kind of data can you access?” or “Show me total order amount over time”
- Get instant results directly in Slack

Demo
Watch this comprehensive demo to see AI agents in action: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.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.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
- Use AI hints to provide additional context specifically for AI agents
Using AI hints
AI hints are specialized metadata fields that provide additional context specifically for AI agents. These hints help the AI better understand your data models, business logic, and how to interpret your metrics and dimensions.AI hints are internal metadata used only by AI agents and are not displayed to
users in the Lightdash interface. When both AI hints and descriptions are
present, AI hints take precedence for AI agent prompts.
Writing effective instructions
Think of your instructions as teaching your AI agent about your world. The better you explain your business context and preferences, the more useful and relevant your agent’s responses will be. Focus on four key areas: what your agent should know about your industry, your team’s goals and constraints, how you like data analyzed, and how results should be communicated.What to include
- Industry terminology and key metrics including acronyms your team uses regularly (e.g., “CPM means Cost Per Mille, not cost per mile” or “Our ARR calculations exclude one-time setup fees”)
- Communication style for how results should be presented to your team (e.g., “Keep explanations simple for non-technical stakeholders” or “Always include actionable next steps”)
- Business constraints like regulatory requirements or budget limitations that affect decision-making
- Analysis preferences your team relies on (e.g., “Always compare month-over-month growth” or “Flag any churn rates above 5% as concerning”)
- Context for interpreting your data (e.g., “Our Q4 always shows higher sales due to holiday promotions” or “Weekend traffic is typically 40% lower”)
Good example - Sales Team Agent:
You analyze sales performance for our SaaS company. Focus on MRR, churn, and pipeline health. When MRR growth drops below 10% month-over-month, flag it as concerning. Present insights in simple terms that our sales managers can act on immediately. Always include trend explanations and next steps.
You analyze sales performance for our SaaS company. Focus on MRR, churn, and pipeline health. When MRR growth drops below 10% month-over-month, flag it as concerning. Present insights in simple terms that our sales managers can act on immediately. Always include trend explanations and next steps.
What to avoid
- Contradictory instructions that create confusion about priorities
- Overly complex rules that are hard to follow consistently
- Vague guidance like “be helpful” without explaining what that means for your situation
- Too many different focus areas in one agent, remember to keep each agent focused, there are no limits on the number of agents you can create!
- Restating basic features, don’t tell the AI to “create charts” since it already does that
Poor example - Too vague:
Be helpful and analyze data well. Create good charts and explain things clearly.
Be helpful and analyze data well. Create good charts and explain things clearly.
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.Limiting access to specific explores and 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.Adding tags at the model level
Tag entire models to give your AI agent access to all metrics and dimensions within that explore:Adding tags to individual metrics & dimensions
For more granular control, tag specific metrics and dimensions:FAQs
- Does Lightdash store the query data?
- Why can’t I set multiple Agents for the same Slack channel?
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.