Using AI agents

Learn how to interact with AI agents in Lightdash and Slack

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.

SQL mode

SQL mode lets the agent reach for raw SQL when a question can’t be answered from the semantic layer alone — for example, when the data exists in your warehouse but hasn’t been modeled as an explore yet. With SQL mode on, the agent can list warehouse tables, describe their columns, and run SQL queries against your warehouse.

SQL mode is off by default on every new thread. You opt in per conversation using the SQL mode toggle below the message input.

When to use it

• The data you need lives in a warehouse table that isn’t part of an explore.
• You want a one-off lookup, audit, or shape-of-the-data check before modeling it.
• The agent has already told you it can’t answer from the semantic layer.

Prefer the default (semantic) mode for anything that can be answered from your modeled explores — answers stay governed by your dbt metrics, joins, and row-level filters.

Who can use it

The SQL mode toggle is visible only to users who hold the manage:SqlRunner ability — the same permission required to use the SQL Runner page. If you don’t see the toggle, ask a workspace admin to grant SQL Runner access to your role.

How approvals work

SQL mode is gated by a human-in-the-loop check on every query:

1. You flip SQL mode on for the thread.
2. You ask a question. The agent drafts a SQL query.
3. Lightdash shows the proposed SQL inline and waits for you to Approve or Reject before anything runs against your warehouse.
4. Approved queries return results to the agent, which then summarizes or visualizes them.

Each SQL call is approved individually — there is no “approve all” shortcut, and approvals don’t carry over between threads.

SQL mode in Slack

Slack-triggered prompts can only use SQL mode when your organization has the “Require Slack users to authenticate with Lightdash” setting (aiRequireOAuth) turned on. Without it, Slack messages run under a shared workspace-default identity, so SQL execution is disabled fail-closed regardless of the toggle. See Slack AI bot setup for how to configure OAuth.

Asking about a chart or dashboard

You can launch an AI conversation with a chart or dashboard pre-loaded as context. From the resource’s ⋯ menu, click Ask AI Agent.

This opens a new tab on the new-thread page for your default agent. The chart or dashboard appears as a pinned context card above the input, and the agent treats it as the subject of the conversation.

Pinned context

• The pinned card stays visible above your message in the thread, so anyone reading later can tell what was being discussed.
• Click the pinned card to open the chart or dashboard in a new tab.
• The pinned context persists across follow-up messages — “now break it down by region” still refers to the originally pinned chart.

What you can ask

When you pin a saved chart, the agent can read its actual data (subject to your data access setting). It honors the chart’s saved filters, sorts, and custom metrics, so you can ask:

• “Why is this trending up?”
• “Are there outliers in this chart?”
• “Compare this chart’s last 30 days to the previous period.”

If your agent has data access disabled, pinning still works — the agent sees the chart’s structure (name, dimensions, metrics) but no row values are sent to the underlying LLM.

Example use cases

Advanced visualizations with window functions

AI agents can handle complex analytical queries that would traditionally require writing intricate SQL or YAML configurations. In this example, we demonstrate building a 3-month rolling average visualization using nothing but natural language.

This demo shows:

• Creating complex window function calculations with plain English
• Building a 3-month rolling average without writing SQL or YAML
• AI agent understanding your semantic layer context automatically
• Generating production-ready charts from a single natural language query
• No need to manually configure partitions, ordering, or frame clauses
• From question to visualization in seconds, not hours

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).

2. Can I assign a default agent?

You can assign your default agent in Ask AI by clicking the star by your agent’s name.

The default agent setting is per-user, per-project. There’s no project-wide default at the moment. If you haven’t set a default, there’s no predictable way to determine which agent appears first.

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.