ENGINEERING

Vercel preview failure triage to a Linear issue

When a Vercel preview smoke check fails, an AI step reads the failing responses and recent commit diff, writes a likely-cause triage summary.

CategoryEngineering
EngineSim + Paperclip
Difficultyintermediate
Triggerwebhook
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerVercel preview deployment readyVercelVercel
  • ActionRun smoke request against previewHTTP webhook
  • LogicContinue only if smoke check failed
  • ActionFetch PR diff and commits from GitHubGitHubGitHub
  • ActionDraft likely-cause triage with AIOpenAI
  • OutputFile assigned Linear issue with triageLinearLinear

What it does

This workflow watches Vercel preview smoke results and, when one fails, gathers the failing route output and the PR's changed files, asks an AI step to summarize the most probable cause, and opens a Linear issue with that triage attached — assigned to the PR author and linked back to the deploy.

When to use it

Use it when preview failures otherwise vanish into a Slack channel and never get tracked. This turns every failed preview into an actionable, owned ticket with a head start on root cause.

How it works

  1. 1A Vercel preview-ready webhook triggers a smoke request against the preview.
  2. 2A logic step proceeds only when the smoke check fails.
  3. 3The flow fetches the changed files and commit messages from GitHub for the PR.
  4. 4An OpenAI step analyzes the failure plus the diff and drafts a likely-cause triage note.
  5. 5A Linear issue is created with the triage, the preview URL, and the PR link, assigned to the author.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect VercelDeploys, runtime logs, analytics.
  2. 2
    Connect HTTP webhookTrigger any URL on agent actions.
  3. 3
    Connect GitHubRepos, issues, pull requests, actions.
  4. 4
    Connect OpenAIModels, embeddings, files.
  5. 5
    Connect LinearIssues, projects, cycles, triage.
  6. 6
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  7. 7
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  8. 8
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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