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.
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!
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
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
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.
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.
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
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.
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.
You can add AI hints at three levels:Model-level hints - Provide context about the entire table:
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models: - name: customers config: meta: ai_hint: | This is a customers table containing customer information and derived facts. Use this for customer demographics, behavior analysis, and segmentation.
columns: - name: last_name config: meta: dimension: ai_hint: | Customer's last name. Contains PII data. Use for customer identification but be mindful of privacy.
Metric-level hints - Clarify what metrics measure:
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columns: - name: customer_id config: meta: metrics: unique_customer_count: type: count_distinct ai_hint: | Unique customer count for business reporting. Use this for customer acquisition and retention analysis.
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.
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 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.
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.
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.
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.