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AI agents are available as an add-on for all plans. View pricing
Lightdash AI agents let your team ask questions in natural language and get answers built from your semantic layer — the saved dashboards, metrics, dimensions, joins, and descriptions you’ve already defined. An agent picks the relevant models and metrics, builds and runs the query with the right filters and parameters, and returns the result as the chart, table, or summary that fits the question. Because every answer runs through the semantic layer, it respects the same project permissions and user attributes as the rest of Lightdash. You can run agents in the Lightdash app or in Slack, scope each one to a domain with tags, and improve them over time with verified answers, evaluations, and knowledge documents. The pages below cover setup, day-to-day use, governance, and the features that make agents more accurate.
Looking for Autopilot? Autopilot is a separate, admin-only agent that runs on a schedule to keep your project clean — fixing broken charts, flagging stale content, and suggesting new ones. It does not build charts or dashboards from user questions like the conversational agents described here.

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Choosing your AI surface

Lightdash exposes several AI surfaces, and the right one depends on who is asking, what they’re trying to do, and how much governance you need. They share one foundation: the Lightdash semantic layer — governed metrics, joins, descriptions, permissions, project context, verified answers, and evaluations. Investing there pays off no matter which surface a user picks.

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Decision guide

Pick the first item that matches what you’re trying to do:
  • Let business users ask governed questions in natural language — use AI agents in the app, or in Slack when the answer belongs in a team conversation.
  • Send an agent answer on a schedule — schedule a recurring delivery from a conversation so Lightdash re-runs the agent on your chosen frequency and sends the refreshed chart plus an AI-written summary to Slack, email, or Microsoft Teams.
  • Query Lightdash from Claude, Cursor, Codex, ChatGPT, or a custom agent — use the Lightdash MCP server.
  • Give your agents tools from Notion, Linear, Confluence, or other services — connect external MCP servers.
  • Teach your AI coding tool Lightdash concepts and YAML syntax — add the Docs MCP and install agent skills.
  • Build an interactive, shareable app from a prompt — use data apps.
  • Create or edit saved charts and dashboards conversationally — use content editing. (Beta)
  • Make bulk, reviewable dashboard changes — use dashboards as code with lightdash download and lightdash upload.
  • Change a metric, dimension, join, or description — edit dbt or Lightdash YAML through Git, or use AI writeback. (Beta)
  • Change code in another connected repository (not the dbt project) — use the AI coding agent to open a pull request on any repo your org can write to, with verification delegated to that repo’s own CI. (Beta)
  • Fix something the agent got wrong — improve descriptions, AI hints, project context, verified answers, evaluations, or Issues findings. (Issues is Beta.)

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Compare the surfaces

A few principles hold across all of them: keep agents focused — several domain-scoped agents beat one project-wide agent; treat raw SQL as an escape hatch and fix the semantic layer when routine questions need it; and never upload sensitive material as knowledge documents unless everyone who can use the agent is allowed to see it. For the full treatment, see Effective analytics with agents.

Set up agents

Set up AI agents

Enable AI features, create your first agent, scope its data, and connect it to Slack

Agent visibility

How admins see, control, and give feedback on what AI agents do with your data

Data access control

How agents reach your data, and how to scope them with tags and user attributes

Thread retention

Automatically delete AI agent threads after a period of inactivity

Creating and editing content

Let AI agents build and edit charts and dashboards in Lightdash

AI Router

Automatically route questions to the best-fit agent, so users don't have to pick one first

Lightdash MCP server

Connect Claude, ChatGPT, and Codex to your Lightdash data over the Model Context Protocol

Connect external MCP servers

Give your Lightdash agents tools from external MCP servers like Notion, Linear, and Confluence

AI agents as code

Download AI agents to YAML, review them in Git, and promote them between projects

AI writeback (Beta)

Ask an AI agent to edit your dbt project in chat and open a pull request with the changes

AI coding agent (Beta)

Ask an agent in chat to edit any connected repository and open a pull request

Work with agents

Using AI agents

Ask questions, use SQL mode, mention charts, and schedule deliveries from an agent conversation

Deep research

Run a multi-step AI investigation and get an evidence-backed report with verified charts and sources

Agent memory

Agents learn from your questions, corrections, and feedback automatically with no setup required

Autopilot

Scheduled AI agent that fixes broken charts, flags stale content, and suggests new ones

Improve answers

Verified Answers

Train your AI agent with high-quality examples for better, more consistent responses

Create evaluation suites

Test and validate your AI agent's performance with custom evaluation suites

AI agent issues

Track AI-filed and human-filed data problems on one board, with one-click fixes

Agent context

Give every agent in a project the same terminology, table choices, and business rules

Effective analytics with agents

How to configure, scope, and improve your AI agents so they give accurate, governed answers