ENGINEERING

Sentry error spike to PagerDuty with blame-attributed GitLab issue

On a sudden Sentry event-rate spike, it pages on-call via PagerDuty and simultaneously files a GitLab incident issue naming the suspected deploy and commit from blame analysis.

CategoryEngineering
Enginesim
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSentry event-rate spike metric alertSentrySentry
  • LogicCheck spike magnitude against severity threshold
  • ActionTrigger PagerDuty incident to on-callPagerDutyPagerDuty
  • ActionCorrelate spike time with recent GitLab deploysGitLabGitLab
  • OutputCreate GitLab incident issue with rollback hintGitLabGitLab

What it does

Detects a sudden spike in a Sentry issue's event rate and treats it as a live incident. It pages the on-call engineer through PagerDuty and, in parallel, opens a GitLab incident issue that names the suspect commit and the deploy window it landed in, so responders arrive with a rollback candidate already identified.

When to use it

Use it for high-severity, time-sensitive errors where rate-of-change matters more than raw volume. Best for production-critical services where the on-call needs both an immediate page and a pre-built incident record pointing at the likely offending deploy.

How it works

  1. 1Sentry fires on a metric alert when an issue's event rate spikes.
  2. 2The flow checks the spike magnitude against a severity threshold to decide whether to escalate.
  3. 3If severe, it triggers a PagerDuty incident routed to the service's on-call.
  4. 4It correlates the spike start time with recent GitLab deploys to pick the suspect commit.
  5. 5It creates a GitLab incident issue with the trace, suspect deploy, and rollback hint, linked to the PagerDuty incident.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SentryErrors, performance, releases.
  2. 2
    Connect PagerDutyIncidents, on-call, escalations.
  3. 3
    Connect GitLabRepos, MRs, pipelines, registry.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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