CHATBOTS

Error-Rate Guardrail that Auto-Pauses Feature Flag Ramps

When Datadog signals an error-rate spike tied to a ramping flag, the bot pauses the ramp in Postgres, freezes the flag at its current percentage in Vercel.

CategoryChatbots
Enginesim
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDatadog monitor alert receivedDatadogDatadog
  • ActionMatch alert to active ramp planPostgreSQLPostgres
  • LogicProceed only if flag is mid-ramp
  • ActionFreeze flag percentage in Vercel Edge ConfigVercelVercel
  • ActionSet ramp plan to pausedPostgreSQLPostgres
  • OutputAlert channel with flag and tripped metricSlack

What it does

Watches for trouble during a rollout and stops the ramp before it spreads. When Datadog fires a monitor alert correlated to a flag that is mid-ramp, the bot freezes that flag at its current percentage, marks the ramp plan paused so the scheduled executor skips it, and tells the team exactly which metric tripped the guardrail.

When to use it

When flags ramp automatically and you want a safety net that halts a rollout the moment error rate, latency, or a custom SLO crosses threshold — without waiting for a human to notice.

How it works

  1. 1A Datadog monitor alert arrives, tagged with the affected flag key.
  2. 2The pipeline matches the alert to an active ramp plan in Postgres.
  3. 3It branches: if the flag is mid-ramp it proceeds to pause; if no active ramp matches it just notifies and exits.
  4. 4It freezes the flag at its current value in Vercel Edge Config and sets the ramp plan to paused in Postgres.
  5. 5It posts an alert to Slack naming the flag, the tripped metric, and the frozen percentage so an engineer can investigate.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect DatadogMetrics, traces, log search.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect VercelDeploys, runtime logs, analytics.
  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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