DEVOPS

Triage a failed preview smoke gate with an AI root-cause agent

When a preview smoke gate fails, an agent pulls the build logs and failing responses, diagnoses the likely cause, files a Linear issue, and comments the analysis on the PR.

CategoryDevOps
Enginepaperclip
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerPreview smoke gate failed
  • ActionFetch Vercel build and function logsVercelVercel
  • ActionAgent diagnoses likely root causeOpenAI
  • ActionFile Linear issue with evidenceLinearLinear
  • OutputComment root-cause analysis on PRGitHubGitHub

What it does

Replaces the manual scramble after a red preview gate. When smoke checks fail, an agent gathers the Vercel build and runtime logs plus the failing HTTP responses, reasons about the most probable root cause (bad env var, broken route, failed migration, upstream timeout), and writes a plain-language diagnosis. It then files a Linear issue with the evidence and drops the same summary as a PR comment so the author starts debugging with a hypothesis already in hand.

When to use it

Use it when preview failures are common enough that triage eats real engineering time, and the first ten minutes are always the same log-spelunking. Best for teams that want failures captured as tracked work rather than lost in a CI log.

How it works

  1. 1A preview smoke gate failure event starts the run.
  2. 2A Vercel action fetches the deployment's build and function logs.
  3. 3The agent correlates logs with the failing endpoints and drafts a root-cause hypothesis.
  4. 4A Linear action files an issue with the diagnosis, preview URL, and log excerpts.
  5. 5A GitHub action posts the same analysis as a comment on the PR.

Set it up

What you configure once, before turning it on.

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

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