CUSTOMER SUPPORT

Monthly Knowledge-Base Audit Agent Across Zendesk and Confluence

An agent reviews the past month of Zendesk tickets against the full Confluence knowledge base, decides which gaps are worth documenting.

CategoryCustomer Support
Enginepaperclip
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerMonthly schedule starts the agent
  • ActionPull month of Zendesk tickets and Confluence indexZendeskZendesk
  • LogicAgent decides which gaps merit documentation
  • ActionDraft missing Confluence articlesConfluenceConfluence
  • ActionOpen ClickUp review task per draftClickUpClickUp
  • OutputPost prioritized audit summary to SlackSlack

What it does

Runs a judgment-heavy monthly audit. Instead of a fixed threshold, an agent reasons over a month of tickets and the existing knowledge base to decide which gaps genuinely deserve a new article, then drafts each one for a human to approve.

When to use it

Use it for a deeper, less mechanical review than a weekly counter — when you want prioritized recommendations and starter drafts rather than just a list of tasks. Suited to teams comfortable letting an agent propose KB structure.

How it works

  1. 1A monthly schedule starts the agent.
  2. 2The agent pulls a month of Zendesk tickets and the current Confluence KB index.
  3. 3It reasons about which question themes are frequent, costly, or rising, and which are already documented well enough.
  4. 4For each chosen gap it drafts a Confluence article in the right space and section.
  5. 5It opens a ClickUp review task per draft, linking the page and the tickets that motivated it.
  6. 6It posts a prioritized audit summary to Slack for the support lead to sign off.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ZendeskTickets, queues, knowledge base.
  2. 2
    Connect ConfluenceSpaces, pages, blueprints.
  3. 3
    Connect ClickUpDocs + tasks + chats in one workspace.
  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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