SUMMARIZATION

Sentry Canary Burn Check with GitLab Rollback Brief

On a schedule during a canary rollout, compares the canary release's error-budget burn against the stable baseline and, if it regresses, posts a rollback recommendation…

CategorySummarization
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerRollout-window schedule tick
  • ActionFetch canary vs baseline release healthSentrySentry
  • LogicCompare burn against rollback tolerance
  • ActionDraft rollback recommendation briefOpenAI
  • ActionPost brief to GitLab pipelineGitLabGitLab
  • OutputSend rollback alert to SlackSlack

What it does

Watches a canary deploy and decides whether it is safe to continue. On a tight interval during rollout, it compares the canary release's crash-free rate and error-budget burn against the stable baseline. If the canary regresses past tolerance, it writes a rollback recommendation to the GitLab pipeline and pings Slack so a human can pull the trigger fast.

When to use it

Use it during progressive or canary rollouts where you need an automated stability second opinion within minutes, not at end-of-day. The branch keeps quiet while the canary is healthy.

How it works

  1. 1A schedule fires repeatedly during the active rollout window.
  2. 2Sentry release-health is fetched for the canary release and the stable baseline.
  3. 3A logic gate compares burn against tolerance; healthy canaries exit silently.
  4. 4On regression, an LLM drafts a rollback-recommendation brief.
  5. 5The brief is posted to the GitLab pipeline and a Slack alert is sent.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SentryErrors, performance, releases.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect GitLabRepos, MRs, pipelines, registry.
  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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