CUSTOMER SUPPORT

Draft a De-escalation Reply for Angry Front Threads

When a Front thread spikes into anger, an agent reads the full history, pulls the customer's account and order context.

CategoryCustomer Support
Enginepaperclip
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerFront reply spikes negativeFront
  • ActionRead full conversation history from FrontFront
  • ActionPull recent orders and refund eligibility from StripeStripeStripe
  • ActionAgent drafts policy-grounded de-escalation replyOpenAI
  • OutputSave draft as Front comment and notify specialist in SlackSlack

What it does

On a sentiment spike in a Front conversation, an agent reads the entire thread, fetches the customer's recent order and billing context, and composes a calibrated de-escalation reply: acknowledging the specific grievance, owning what went wrong, and offering a concrete next step within policy. The draft is posted as an internal Front comment, never auto-sent, so the save specialist edits and sends it — cutting response time without removing the human.

When to use it

Use it when your save specialist spends too long crafting careful replies to furious customers from scratch. The agent does the context-gathering and first draft; the human keeps judgment and the send button.

How it works

  1. 1A Front reply spikes negative and fires the trigger.
  2. 2The agent reads the full conversation history from Front.
  3. 3It pulls the customer's recent orders and refund eligibility from Stripe.
  4. 4The agent drafts a de-escalation reply grounded in that context and your remedy policy.
  5. 5The draft is saved as an internal Front comment and the save specialist is notified in Slack to review and send.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FrontShared inbox, conversations.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect StripeCustomers, subscriptions, payments.
  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.

Run this workflow in your colony.

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