ENGINEERING

Freeze risky merges when Honeycomb SLO burn rate spikes

Watches a Honeycomb SLO burn-rate alert and, when it crosses your fast-burn threshold, applies a merge-freeze label to open GitHub PRs touching the affected service and posts…

CategoryEngineering
Enginesim
Difficultyintermediate
Triggerwebhook
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerHoneycomb SLO burn-rate alert webhookHoneycomb
  • LogicCheck 1-hour burn rate against fast-burn threshold
  • ActionFind open GitHub PRs touching the affected serviceGitHubGitHub
  • ActionApply merge-freeze label to matching PRsGitHubGitHub
  • OutputPost freeze notice with burn rate and held PRs to SlackSlack

What it does

Turns a Honeycomb SLO burn-rate alert into an automatic, scoped merge freeze. When the error budget starts burning faster than your safe rate, this stops new risky code from landing on the at-risk service until the budget recovers.

When to use it

Run this when you trust Honeycomb SLOs as your reliability source of truth and want budget burn to gate merges automatically, instead of relying on an on-call engineer to remember to halt deploys mid-incident.

How it works

  1. 1A Honeycomb burn-alert webhook fires when the fast-burn budget rate trips.
  2. 2The flow reads the alert payload and checks whether the 1-hour burn rate exceeds your configured threshold (e.g. 14.4x).
  3. 3If under threshold, it exits quietly so normal alert noise does not freeze anything.
  4. 4If over, it queries open GitHub PRs labeled or pathed to the affected service and applies a `merge-freeze` label plus a status note.
  5. 5It posts a freeze announcement to the team Slack channel with the SLO name, current burn rate, and which PRs were held.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect HoneycombDistributed traces and queries.
  2. 2
    Connect GitHubRepos, issues, pull requests, actions.
  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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