CUSTOMER SUPPORT

Turn Repeated Agent Replies into Suggested Zendesk Macros

Weekly, analyzes agents' public replies on solved tickets to find answers that are typed over and over.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule fires
  • ActionPull public agent replies from solved ticketsZendeskZendesk
  • ActionGroup near-identical replies and count frequencyOpenAI
  • LogicDrop patterns below repeat threshold
  • ActionDraft reusable macro text per patternOpenAI
  • OutputFile proposed macros as Linear issuesLinearLinear

What it does

Agents often retype the same explanation dozens of times. This workflow detects those repeated answers across solved tickets, distills each into clean, reusable macro text, and routes it to your team's backlog as a proposed macro. The result is less typing and more consistent answers.

When to use it

Reach for this when agents complain about repetitive replies or when answer quality drifts because everyone phrases the same thing differently. It surfaces macro candidates you'd otherwise never notice.

How it works

  1. 1A weekly schedule fires the workflow.
  2. 2It pulls the public agent replies from tickets solved in the past week.
  3. 3OpenAI groups near-identical replies and counts how often each pattern appears.
  4. 4Patterns repeated fewer than a configurable number of times are dropped.
  5. 5For each surviving pattern, OpenAI writes a polished macro with a name, the reusable body, and placeholder fields.
  6. 6Each proposed macro is filed as a Linear issue in the support backlog, labeled "macro-suggestion," for a lead to review and publish.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ZendeskTickets, queues, knowledge base.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect LinearIssues, projects, cycles, triage.
  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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