CUSTOMER SUPPORT

Draft personalized fix-live replies for support to review

When a Sentry issue resolves, an agent reads each linked ticket's full thread and drafts a tailored 'your fix is live' reply per requester.

CategoryCustomer Support
Enginepaperclip
Difficultyadvanced
Triggerwebhook
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSentry issue resolved in releaseSentrySentry
  • ActionPull linked tickets + full comment threadsZendeskZendesk
  • ActionAgent drafts a tailored reply per requesterOpenAI
  • LogicGuard drafts for tone and scope accuracy
  • ActionSave drafts as internal notes, assign ownerZendeskZendesk
  • OutputPing owning agents in Slack to reviewSlack

What it does

Adds a human-in-the-loop layer to shipped-fix notifications. Instead of blasting a templated message, an agent reads the actual conversation history on each affected ticket and writes a reply that references what that specific customer said, then parks it as a draft for a support agent to approve.

When to use it

Use it for high-touch accounts or sensitive issues where a generic auto-reply would feel cold. Good when the same bug produced very different customer conversations and one canned message would not fit all.

How it works

  1. 1A Sentry webhook fires when an issue is resolved in a release.
  2. 2The flow pulls every linked Zendesk ticket and its full comment thread.
  3. 3An agent reads each thread plus the release notes and drafts a context-aware reply per requester.
  4. 4A guard checks each draft for tone and that it does not over-promise beyond the fix scope.
  5. 5The drafts are saved as internal notes on each Zendesk ticket, assigned to the original agent.
  6. 6The owning agents are pinged in Slack to review and send.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SentryErrors, performance, releases.
  2. 2
    Connect ZendeskTickets, queues, knowledge base.
  3. 3
    Connect OpenAIModels, embeddings, files.
  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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