CHATBOTS

Blast-Radius Policy Gate for Feature Flag Changes

Intercepts flag-change requests from Slack, enforces a blast-radius policy — small jumps auto-apply, large jumps require a second approver.

CategoryChatbots
Enginepaperclip
Difficultyadvanced
Triggerchat
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerFlag-change request in SlackSlack
  • ActionFetch current percentage and traffic tierPostgreSQLPostgres
  • LogicApply blast-radius policy; route large jumps to second approver
  • ActionWrite approved percentage to Vercel Edge ConfigVercelVercel
  • OutputLog decision and confirm in SlackPostgreSQLPostgres

What it does

Adds policy enforcement to flag changes requested in Slack. The bot reads the requested percentage delta and decides the path: a small, in-policy increase applies immediately, while a jump over your blast-radius threshold (say more than 20 points, or any change to a high-traffic flag) is held for a named second approver. Either way the final value is pushed to Vercel Edge Config and recorded.

When to use it

When you want self-serve flag changes for routine tweaks but mandatory two-person review for changes that could expose a large share of traffic at once.

How it works

  1. 1An engineer requests a flag change via Slack message to the bot.
  2. 2The agent looks up the flag's current percentage and traffic tier in Postgres.
  3. 3It evaluates the blast-radius policy: in-policy changes proceed; out-of-policy changes route to a designated approver with an approve/deny card.
  4. 4Once cleared, it writes the new percentage to Vercel Edge Config.
  5. 5It logs the decision, approver, and policy path to Postgres and confirms in Slack.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SlackChannels, DMs, threads, mentions.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect VercelDeploys, runtime logs, analytics.
  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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