ENGINEERING

Watch Sentry for regressions after a dependency bump merges

After a dependency MR merges in GitLab, this watches Sentry for new error signatures tied to the bumped package's blast radius and alerts Slack the moment a likely regression…

CategoryEngineering
Enginesim
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerGitLab dependency MR merged to default branchGitLabGitLab
  • ActionResolve affected modules and record watch windowPostgreSQLPostgres
  • ActionPoll Sentry for new errors in affected modulesSentrySentry
  • LogicCompare to baseline to isolate regressions
  • OutputAlert Slack with MR, Sentry issue, and packageSlack

What it does

A bump can pass tests and still break in production. When a dependency MR merges, this workflow remembers which modules that package touches, then watches Sentry for new or spiking error signatures originating in those modules during a post-deploy window, and raises an alert if one appears.

When to use it

Run it when you auto-merge low-risk dependency updates and need a safety net that connects a fresh production error back to the specific bump that likely caused it, so you can roll back fast.

How it works

  1. 1A GitLab merge webhook fires when a dependency-bump MR is merged to the default branch.
  2. 2The workflow resolves the package's affected modules from the Postgres dependency graph and records a watch window.
  3. 3During the window it polls Sentry for new error signatures whose stack frames fall inside those modules.
  4. 4A logic step compares against the pre-merge baseline to isolate genuinely new regressions.
  5. 5On a match it posts to Slack with the offending MR, the Sentry issue link, and the suspect package.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitLabRepos, MRs, pipelines, registry.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect SentryErrors, performance, releases.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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