AI AGENTS
Nightly Top-Sentry-Issues Repro Batch to GitLab MRs
Each night, an agent pulls the top unresolved Sentry issues by user impact, reproduces each one, and opens a batch of failing-test GitLab MRs ranked by blast radius.
How it runs
The automated pipeline, trigger to output.
- TriggerNightly schedule fires the batch
- ActionQuery top unresolved Sentry issues by user impactSentry
- ActionReproduce each issue in shell sandboxShell
- LogicKeep only deterministically reproducible issues
- ActionWrite a failing test per confirmed issueShell
- OutputOpen one labeled GitLab MR per reproGitLab
What it does
Runs as a scheduled batch instead of reacting to single alerts. Every night it queries Sentry for the highest user-impact unresolved issues, attempts to reproduce each in a shell sandbox, and opens one GitLab MR per confirmed repro, each containing a failing test. MRs are labeled and ordered by how many users the error touched so the morning queue is pre-prioritized.
When to use it
Use it when you want a steady, prioritized intake of test-backed bugs each morning rather than per-alert noise during the day.
How it works
- 1A nightly schedule triggers the batch run.
- 2The agent queries Sentry for the top unresolved issues ranked by affected-user count.
- 3For each issue it fetches the trace and runs a shell repro in an isolated checkout.
- 4Logic gate: keep only issues that reproduce deterministically.
- 5The agent writes a failing test per confirmed issue.
- 6It opens a GitLab MR for each, applying impact labels and linking the Sentry issue.
Set it up
What you configure once, before turning it on.
- 1Connect SentryErrors, performance, releases.
- 2Connect ShellRun sandboxed commands inside the workspace.
- 3Connect GitLabRepos, MRs, pipelines, registry.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More AI Agents workflows
Custom Metrics Cardinality Spike Pager
A webhook from a Datadog monitor fires when custom-metric cardinality jumps; an agent pinpoints the offending metric and tag, estimates the added cost.
Sentry-to-Confluence Runbook Updater
When a Sentry issue is resolved, the agent finds the matching Confluence runbook page and proposes an inline update with the verified fix.
Stale Doc-PR Chaser for Runbook Gaps
On a daily schedule the agent finds runbook doc PRs that were opened from resolved incidents but never reviewed, summarizes what each one fixes.
Resolved Incident to Public Troubleshooting Doc
For customer-facing errors resolved in Sentry, the agent drafts a sanitized troubleshooting entry and opens a PR to your ReadMe documentation.
On-Call Runbook Gap Closer: Resolved Sentry Issues to Doc PRs
An agent reads each newly resolved Sentry issue, compares the actual fix against your existing runbook, and opens a GitHub PR adding the missing remediation steps.
Weekly On-Call Doc-Gap Digest
Each week the agent reviews every Sentry issue resolved in the last 7 days, ranks the ones whose runbook coverage is missing or thin.
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.