ENGINEERING

Slow trace + Sentry error → deploy-blame note on the GitLab merge request

Correlates a budget-breaching Honeycomb trace with concurrent Sentry errors, finds the most recent GitLab deploy/merge that touched the affected service.

CategoryEngineering
Enginesim
Difficultyadvanced
Triggerwebhook
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerHoneycomb over-budget trace alert (webhook)Honeycomb
  • ActionPull correlated Sentry errors for the windowSentrySentry
  • ActionFind most recent GitLab MR touching the serviceGitLabGitLab
  • LogicBranch: culprit MR found vs fall back to new issue
  • OutputComment joined evidence on the merge requestGitLabGitLab

What it does

Points latency regressions back at the change that likely caused them. When a slow trace correlates with Sentry errors, the workflow identifies the most recent merge to the affected service and comments the trace + error evidence directly on that GitLab merge request, so the author sees the regression in the context of their own change.

When to use it

Use when most latency regressions trace back to a recent deploy and you want feedback to land where the author already looks. Ideal for teams practicing continuous deployment who want fast attribution without a separate triage ticket.

How it works

  1. 1A Honeycomb trigger fires on a trace exceeding its latency budget.
  2. 2The workflow pulls correlated Sentry errors for the service and window.
  3. 3It queries GitLab for the most recent merged MR touching that service before the trace timestamp.
  4. 4It branches: if a likely-culprit MR is found, build a blame note; otherwise fall back to a new issue.
  5. 5It posts the joined trace and error evidence as a comment on the suspect merge request.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HoneycombDistributed traces and queries.
  2. 2
    Connect SentryErrors, performance, releases.
  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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