DEVOPS

Per-Deploy Log Regression Check: Comment the Offending PR on GitHub

After each Vercel production deploy, measures the new build's per-minute Axiom ingest rate against the previous deploy and, if it regressed sharply, posts a comment…

CategoryDevOps
Enginesim
Difficultyadvanced
Triggerwebhook
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerVercel deployment-succeeded webhook firesVercelVercel
  • ActionQuery Axiom ingest-per-minute for new vs prior deployAxiom
  • LogicStop unless rate increase exceeds regression threshold
  • ActionBreak down Axiom volume to noisiest new log sourceAxiom
  • OutputComment offending GitHub PR with delta + hygiene checklistGitHubGitHub

What it does

Catches log-volume regressions at the source: the pull request that introduced them. It measures each deploy's steady-state ingest rate, compares it to the prior deploy, and leaves the evidence as a PR comment so the author fixes it before it compounds.

When to use it

Use it when you want logging cost treated like a performance budget enforced in code review. Ideal for teams practicing trunk-based delivery where every deploy maps cleanly to a merged PR.

How it works

  1. 1A Vercel deployment-succeeded webhook triggers and the workflow waits a short settle window for traffic to normalize.
  2. 2It queries Axiom for the new deploy's ingest-per-minute rate and the prior deploy's rate over the same duration.
  3. 3A logic step compares the two; it stops if the increase is under the regression threshold.
  4. 4It runs an Axiom breakdown to find the log source or route contributing the most new volume.
  5. 5It maps the deploy's commit SHA to the GitHub PR and posts a comment with the rate delta, the noisy source, and a hygiene checklist.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AxiomLog streams, queries, dashboards.
  2. 2
    Connect VercelDeploys, runtime logs, analytics.
  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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