ENGINEERING

Agent-drafted root-cause analysis for a new flaky test

When a test is freshly quarantined, a CEO agent reads the failure logs and recent diffs, drafts a likely root-cause hypothesis and suggested fix.

CategoryEngineering
Enginepaperclip
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerQuarantined label added to GitHub issueGitHubGitHub
  • ActionFetch failure logs, test file, recent commitsGitHubGitHub
  • LogicAgent drafts ranked root-cause hypothesisOpenAI
  • ActionPost analysis and assign owner on issueGitHubGitHub
  • OutputNotify owner in SlackSlack

What it does

When a new flaky test gets quarantined, an agent goes beyond filing a bare ticket: it reads the captured failure logs, the test source, and the commits that recently touched it, then drafts a plain-English root-cause hypothesis (timing race, shared fixture, network dependency, ordering) with a suggested fix direction. That analysis is attached to an owner-assigned GitHub issue so the owner starts with a head start instead of a blank page.

When to use it

Use it when your team loses hours reconstructing why a test is flaky each time one is quarantined, and you want a first-pass investigation written automatically.

How it works

  1. 1A `quarantined` label added to a GitHub issue triggers the workflow.
  2. 2The agent fetches the failure logs, the test file, and recent commits touching it from GitHub.
  3. 3It reasons over the evidence to produce a ranked root-cause hypothesis and a suggested remediation.
  4. 4The analysis is posted as a structured comment on the issue and the issue is assigned to the file's owner.
  5. 5The owner is notified in Slack that an investigation draft is ready for review.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GitHubRepos, issues, pull requests, actions.
  2. 2
    Connect OpenAIModels, embeddings, files.
  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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