ENGINEERING

Escalate High-Frequency Flaky Tests from Datadog CI Visibility

Polls Datadog CI Visibility for flaky-test rates, and when a test's flakiness breaches the SLO it pages the owning team in PagerDuty and files a tracking issue.

CategoryEngineering
Enginesim
Difficultyadvanced
Triggerschedule
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled flakiness SLO check
  • ActionQuery Datadog CI Visibility for flake ratesDatadogDatadog
  • LogicFlag tests breaching SLO with min-runs guard
  • ActionPage owning team via PagerDutyPagerDutyPagerDuty
  • OutputOpen or update GitHub tracking issueGitHubGitHub

What it does

This workflow treats chronic flakiness as an operational incident. On a schedule it queries Datadog CI Visibility for per-test flake rates, compares each against your reliability SLO, and for any test breaching the budget it pages the owning team via PagerDuty and opens a GitHub issue to anchor the fix.

When to use it

Use it when a few persistently flaky tests are eroding developer trust in CI and a passive backlog is not enough. It is for platform or DevEx teams who already emit CI metrics to Datadog and want flakiness governed by an SLO with real escalation.

How it works

  1. 1A scheduled trigger runs the check at a fixed cadence.
  2. 2The flow queries Datadog CI Visibility for flaky-test rate and run volume over the trailing window.
  3. 3A branch evaluates each test against the SLO and the minimum-runs guard so rarely-run tests do not page.
  4. 4Tests in breach trigger a PagerDuty event routed to the owning team's service.
  5. 5The flow opens or updates a GitHub issue capturing the flake rate, sample failing runs, and the SLO breach for tracking to resolution.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect DatadogMetrics, traces, log search.
  2. 2
    Connect PagerDutyIncidents, on-call, escalations.
  3. 3
    Connect GitHubRepos, issues, pull requests, actions.
  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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