ENGINEERING

Error-Budget Feature-Freeze Trigger to Linear

When the projected SLO exhaustion date falls inside the current release window, this workflow opens a high-priority Linear issue declaring a feature freeze and assigns it…

CategoryEngineering
Enginesim
Difficultyintermediate
Triggerschedule
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSchedule (several times daily)
  • ActionRead SLO budget and burn rateDatadogDatadog
  • LogicFreeze if exhaustion precedes window reset
  • ActionOpen high-priority feature-freeze issueLinearLinear
  • OutputAnnounce freeze in SlackSlack

What it does

This is the decision-and-act half of error-budget management. It evaluates whether current Datadog burn will exhaust the budget before the window resets, and if so it converts that signal into a concrete, trackable action: a feature-freeze ticket in Linear with the math attached.

When to use it

Use it when your team has agreed on an error-budget policy ("if we're projected to blow the budget, we stop shipping features and fix reliability"). This automates the trigger so the freeze is declared on data, not on whoever happens to notice.

How it works

  1. 1A schedule runs the check several times a day.
  2. 2It reads the SLO's remaining budget and burn rate from Datadog.
  3. 3A logic step compares the projected exhaustion date against the window-reset date; it only proceeds when exhaustion lands before reset.
  4. 4When the freeze condition is met, it creates a high-priority Linear issue titled with the service and projected date, includes the burn math in the description, and assigns it to the service owner.
  5. 5It posts a short Slack notice linking the Linear issue so the team sees the freeze immediately.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect DatadogMetrics, traces, log search.
  2. 2
    Connect LinearIssues, projects, cycles, triage.
  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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