SUMMARIZATION

Release health note per Vercel deploy

When a Vercel deploy goes live, summarizes the Sentry errors observed in the release window into a plain-English health note that separates brand-new error classes…

CategorySummarization
Enginesim
Difficultyintermediate
Triggerwebhook
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerVercel deployment succeeded webhookVercelVercel
  • ActionFetch Sentry issues in the release windowSentrySentry
  • LogicClassify each issue: new vs regressed vs carried-over
  • ActionSummarize into a plain-English health noteOpenAI
  • OutputPost the health note to the release channelSlack

What it does

After every production deploy, this workflow pulls the Sentry errors tied to the new release and writes a short, readable health note. It explicitly separates three buckets: brand-new error classes that never appeared before, regressions of issues that were marked resolved, and pre-existing noise that carried over. The result is a Slack message any on-call engineer can read in fifteen seconds.

When to use it

Use it if your team deploys often and nobody actually reads the raw Sentry issue feed after a release. It replaces the "is this deploy okay?" Slack question with an automatic answer scoped to exactly the release window.

How it works

A Vercel deployment-succeeded webhook fires with the release SHA. The flow queries Sentry for issues whose first-seen or regression timestamp falls inside the release window and tags each issue as new, regressed, or carried-over. An OpenAI step turns that classified list into a plain-English narrative with counts and the top offenders named. The note is delivered to a Slack release channel with a link back to each Sentry issue.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect VercelDeploys, runtime logs, analytics.
  2. 2
    Connect SentryErrors, performance, releases.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  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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