ENGINEERING

Scheduled release-adoption window evaluator

On a recurring schedule, checks the current staged Vercel release's accumulated adoption and crash-free rate.

CategoryEngineering
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSchedule tick during soak window
  • ActionRead active staged release from PostgresPostgreSQLPostgres
  • ActionFetch Sentry crash-free rate + elapsed soakSentrySentry
  • LogicBelow hard floor or window elapsed?
  • ActionPromote or rollback Vercel aliasVercelVercel
  • OutputReport soak verdict to SlackSlack

What it does

Runs a timed soak instead of an instant check. On each tick it pulls the staged release's session adoption and crash-free rate from Sentry, decides whether the soak window has elapsed, and either promotes the build, lets it keep soaking, or aborts immediately on a hard breach.

When to use it

Use it when you want a release to bake for a fixed period (say 30 minutes of real traffic) before promotion, and you would rather evaluate at intervals than react to a single webhook. Good for lower-traffic apps where sessions trickle in and a one-shot check would be premature.

How it works

  1. 1A schedule fires every few minutes during business hours.
  2. 2The flow reads the active staged release tag from a Postgres state row.
  3. 3It queries Sentry for that release's crash-free session rate and elapsed soak time.
  4. 4A branch aborts and rolls back the Vercel alias immediately if the rate is below a hard floor.
  5. 5Otherwise it checks whether the soak window has fully elapsed at or above the pass threshold.
  6. 6If yes, it promotes on Vercel and clears the state row; a Slack message reports the final decision.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SentryErrors, performance, releases.
  2. 2
    Connect VercelDeploys, runtime logs, analytics.
  3. 3
    Connect PostgresAny Postgres URL — query, write, migrate.
  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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