CHATBOTS

Feature Flag Rollout Request and Gradual Ramp Scheduler

A Slack bot that turns a flag rollout request into a confirmed, scheduled gradual ramp — it asks for the target percentage, restates the blast radius for sign-off.

CategoryChatbots
Enginepaperclip
Difficultyintermediate
Triggerchat
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • Trigger/rollout slash command in SlackSlack
  • ActionLook up current flag audience sizePostgreSQLPostgres
  • LogicCompute blast radius and require operator confirmation
  • ActionWrite staged ramp plan and audit rowPostgreSQLPostgres
  • OutputPost scheduled ramp summary to channelSlack

What it does

Lets an engineer kick off a feature-flag rollout from Slack without touching a dashboard. The bot interviews the requester for the flag key and target percentage, calculates how many users that exposes, makes the operator confirm the blast radius, and records a multi-stage ramp plan (e.g. 5% → 25% → 50% → 100%) so the flag increases gradually instead of all at once.

When to use it

When your team ships behind flags and wants a guardrailed, auditable way to start rollouts from chat — avoiding fat-finger 0%-to-100% jumps and giving every ramp a paper trail.

How it works

  1. 1An engineer runs `/rollout start checkout-v2` in Slack, triggering the bot.
  2. 2The agent asks for the target percentage and the ramp window, then queries Postgres for the flag's current audience size.
  3. 3It computes the blast radius (users newly exposed) and posts a confirmation card the requester must approve.
  4. 4On approval it writes a staged ramp plan and audit row to Postgres.
  5. 5It posts a summary to the channel with each scheduled stage and time.

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
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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