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Notion

Inside Notion's Multi-Agent System for Customer Feedback

平台
YouTube
时长
2:34
片中语言
美国英语
播放量
1918
镜头间隔中位数
8.09s
人脸出镜
40%
暗部画面
19%
Notion Inside Notion's Multi-Agent System for Customer Feedback at 0:00medium presenter
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Inside Notion's Multi-Agent System for Customer Feedback 的文字记录

由语音模型根据影片自身的音轨转写，并非品牌撰写。点击任意一行即可跳到那一刻。

  1. 0:00Hey, I'm Sandra, and today I'm going to show you a series of custom agents that I built within Notion
  2. 0:05that track our top feature requests coming from our customers.
  3. 0:09We've actually named each agent after a Sesame Street character,
  4. 0:12just to keep the agent interaction really fun.
  5. 0:15This system creates a monthly report that shapes our product roadmap,
  6. 0:19and we call it the Go-To-Market Top 10.
  7. 0:21So let's get started.
  8. 0:26Our first agent is the Intake Agent.
  9. 0:28It picks up the new gong call, reads the transcript, and
  10. 0:32asks itself one question.
  11. 0:34Is there a feature request in here?
  12. 0:36Let me show you an example of this agent running in real time.
  13. 0:40Nine minutes ago, we got a gong call, and our agent screened it and
  14. 0:44realized there was no feature request detected, so it labeled it as rejected.
  15. 0:49Doing its job.
  16. 0:52In the case that a feature request is detected on a call,
  17. 0:56the extractor agent wakes up.
  18. 0:58Now this agent pulls the exact quote from the transcript.
  19. 1:02For example, let's pull up this prior run.
  20. 1:05We can see that this feature request was detected from
  21. 1:08the CISO about admin controls.
  22. 1:13The next agent in our system is the matcher agent,
  23. 1:16which does a few things.
  24. 1:18First, it looks across our existing feature request database,
  25. 1:21which lives in Notion.
  26. 1:22And it either matches a request with something we're already
  27. 1:24tracking or recommends that we create a new one.
  28. 1:29Next comes the logger agent.
  29. 1:31This agent helps us link the feature request to the matcher's recommendation.
  30. 1:36It associates the extracted quote and customer information from a Salesforce
  31. 1:40worker with the feature request.
  32. 1:45We have our extracted quote,
  33. 1:46we know which feature request to associate it to, and we have our business metrics.
  34. 1:51So now the reporter agent brings it all together.
  35. 1:54It compiles all the feature requests from all the sources I mentioned into a digestible data-backed report on the first of every month.
  36. 2:02This is great because it ties back feature requests to real customer quotes, with sources, and to business impact.
  37. 2:11I love handing this work off to custom agents because it's important work that I couldn't imagine us doing any other way.
  38. 2:17With hundreds of hours of calls every month, this system filters a noise and
  39. 2:22gives us a clear data-backed picture of what customers are actually asking for.
  40. 2:27And as for your feedback, keep it coming and see you next time.
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