AI agent issues

The Issues board is where data problems get tracked. Two things land on the same board:

• AI-filed findings — every AI agent turn is scanned in the background; likely-wrong answers are grouped by root cause and filed automatically.
• Human-filed issues — an org admin can file an issue directly from the board.

Each issue has a priority, an assignee, an activity feed, and — where applicable — a one-click writeback that opens a pull request against your dbt project or your shared project context file.

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When to use it

Turn Issues on once you have agents in regular use and want a shared queue for data-quality work. It’s most useful when:

• Users ask the same kind of question repeatedly and the agent struggles
• You want to keep dbt metadata and agent instructions tight as your data model evolves
• You’re rolling agents out to a wider audience and want admin-level visibility into quality
• You want a single place to triage data problems users spot in dashboards or charts

If you’re still setting your first agent up, start with agent setup and evaluations first — Issues works best once there’s real usage to learn from.

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Enabling Issues

Issues is opt-in. Until an admin enables it, no agent turns are processed and no findings are collected.

1. Go to Settings → Ask AI → General.
2. Toggle Review AI agent turns on.

Once enabled, future agent turns are scanned in the background and human-filed issues can be reported from anywhere in the app. Existing threads are not back-filled. To stop AI scanning, toggle the setting off. Previously collected issues remain on the board until you act on or dismiss them.

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Opening the board

Open Settings → Ask AI → Issues to see the board. The Ask AI button in the navbar is the fast path: when there are open issues it previews recent findings; when the board is empty it shows a small dropdown that links straight to the Issues board so newly-filed manual issues are always reachable. Cards on the board are grouped by status lane (To Do, In Progress, Done) and each card shows its priority, assignee, and category badge. Click a card to open the issue detail modal.

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How issues get onto the board

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AI-filed findings

With the setting enabled, Lightdash scans every AI agent turn for signs the answer was probably wrong — a missing metric, an ambiguous field, a gap in your project context — and files an issue for each root cause it identifies. Before filing, the classifier checks the board’s existing issues and, if the same underlying problem is already tracked, attaches the new evidence to that issue instead of opening a duplicate. Recurring findings bump the Recurs N× tag on the existing card. Slack notifications (if configured) only fire when a new issue is opened. Recurrences accrue silently on the existing card so a chatty problem doesn’t spam the channel — check the board for the latest state. AI findings are opened in the same lane your triage workflow uses (existing findings behavior is unchanged).

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Human-filed issues

Any organization admin with AI agents enabled can file an issue from the New issue button on the Issues board. Manual issues land in the To Do lane.

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Filing an issue

The Create issue modal captures: Submit and the issue appears on the board in To Do, with the author recorded as the creator.

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The issue detail modal

Clicking a card opens a focused modal with a metadata rail (status, priority, assignee, agent, project) and a chronological activity feed that merges issue events with any existing writeback events:

• created
• status_changed
• assignee_changed
• priority_changed
• comment_added
• recurred

You can change status, priority, and assignee inline; each change writes an event to the feed with its author. Comments are supported and appear inline in the feed. For AI-filed findings, the modal also has an Evidence section that shows the curated excerpts the classifier actually cited — not the full agent thread. A Read full conversation action opens the source thread in a stacked modal (or in a new tab) if you need the wider context. Findings also show an affected-target chip and a Recurs N× tag next to the category badge, and — if writeback is blocked — a plain-language note explaining why (for example, that a GitHub or GitLab connection is required).

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Fixing an issue with writeback

For each issue, Lightdash proposes the smallest change that would prevent it:

• Semantic layer fixes open a pull request against your dbt project (rename a field, add a description, add a metric).
• Project context fixes add a short note to a shared file that your agents read before answering future questions.

Writeback runs the same way whether the issue was AI-filed or human-filed. A manual issue is eligible for writeback when it has:

• a root cause of semantic layer or project context,
• a project, and
• an assigned agent.

For manual issues, the Related explores you picked when filing are passed into the writeback prompt so the agent starts from the right model instead of guessing from the description. Project context fixes write to a shared file — lightdash.project_context.yml — that lives next to lightdash.config.yml in your dbt repo. See Agent context for the file shape, entry schema, and how to edit it by hand.

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Project context vs. semantic layer fixes

Both kinds of fix open a pull request, but they change different things and have different review costs. A rule of thumb: if a new analyst joining the team would also benefit from the change, it’s probably a semantic layer fix. If only the agent needs to know it, it’s project context.

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Privacy and data handling

• Issues runs against agent turns inside your organization only.
• Issues and findings are stored in Lightdash alongside your other agent data and respect your existing project and admin permissions.
• Disabling the toggle stops new AI scanning immediately; it does not delete previously collected issues.

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Related

• Agent context — the shared lightdash.project_context.yml file project context fixes write to.
• Agent visibility — where Issues fits into admin visibility overall.
• Evaluations — run a fixed set of prompts against your agent and grade the answers.
• AI writeback — let an agent open a dbt pull request from chat.
• Autopilot — scheduled agent that cleans up content and flags issues for review.