ENGINEERING

Sentry regression blame and Slack ping to the suspected author

On a new Sentry regression, it identifies the commit that most likely caused it via stack-frame blame and DMs the suspected author in Slack with the trace and a one-click GitLab…

CategoryEngineering
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSentry regression on a resolved issueSentrySentry
  • ActionFetch trace and commits since last resolutionSentrySentry
  • LogicRank suspect commits by changed-file match
  • ActionResolve top author email to Slack userSlack
  • OutputDM author with trace, suspect commit, issue linkSlack

What it does

When Sentry marks an error as a regression (a resolved issue that came back), this flow finds the recent commit that most plausibly reintroduced it and pings the author directly in Slack. The author gets the stack trace, the suspect commit, and a prefilled link to open a GitLab issue, so the person with the most context sees it first.

When to use it

Use it for fast accountability on regressions without a triage bottleneck. Ideal for teams that release frequently and want the engineer whose change likely caused a regression to be notified within minutes rather than waiting for a daily triage sweep.

How it works

  1. 1Sentry fires on a regression event for a previously resolved issue.
  2. 2The flow pulls the stack trace and the commits deployed since the issue was last resolved.
  3. 3It matches the failing frame's file against the changed files in those commits to rank suspects.
  4. 4It resolves the top suspect's author email to a Slack user.
  5. 5It sends that user a Slack DM with the trace, the suspect commit, and a GitLab new-issue link.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SentryErrors, performance, releases.
  2. 2
    Connect GitLabRepos, MRs, pipelines, registry.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  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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