AI AGENTS

Post-QBR Recap and Action-Item Router

After a QBR call, an agent turns the Zoom transcript plus account context into a customer-ready recap email and routes the agreed action items into HubSpot and Linear.

CategoryAI Agents
Enginepaperclip
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerZoom QBR transcript readyZoomZoom
  • ActionMatch meeting to HubSpot company and contextHubSpotHubSpot
  • ActionExtract decisions and action items (LLM)OpenAI
  • LogicSplit customer-facing vs internal items
  • ActionCreate tasks in Linear and log on HubSpot dealLinearLinear
  • OutputSend recap email to customer via GmailGmailGmail

What it does

Closes the loop after the QBR happens. The agent takes the meeting transcript, blends it with account history, and produces a polished recap to send the customer while automatically creating follow-up tasks in the right systems so commitments do not slip.

When to use it

Use this when QBR follow-through is inconsistent: notes live in someone's head, recap emails go out late, and action items never make it into a tracker. This makes the wrap-up automatic and accountable.

How it works

  1. 1A Zoom trigger fires when a QBR recording's transcript is ready.
  2. 2The agent matches the meeting to its HubSpot company and pulls deal and renewal context.
  3. 3An LLM extracts decisions, risks, and concrete action items with owners from the transcript.
  4. 4A logic step splits items into customer-facing recap content versus internal follow-ups.
  5. 5The agent creates internal tasks in Linear and logs commitments and next steps on the HubSpot deal.
  6. 6It drafts and sends a branded recap email to the customer via Gmail and notifies the CSM.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ZoomMeetings, recordings, transcripts.
  2. 2
    Connect HubSpotCRM, deals, marketing, support.
  3. 3
    Connect LinearIssues, projects, cycles, triage.
  4. 4
    Connect GmailRead, draft, send, label.
  5. 5
    Connect OpenAIModels, embeddings, files.
  6. 6
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  7. 7
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  8. 8
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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