I recently built this workflow in n8n to automate the first stage of handling customer support re...I recently built this workflow in n8n to automate the first stage of handling customer support re...
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I recently built this workflow in n8n to automate the first stage of handling customer support requests.
The idea was to take an incoming ticket and let the system handle the repetitive work:
→ Receive the customer request through a webhook
→ Process and structure the incoming data
→ Use an AI Agent to understand the ticket
→ Classify it as Genuine, Spam, or Unclear
→ Route it based on the classification
→ Trigger the appropriate email action
→ Log the interaction in Google Sheets
I also added an Unclear path so that tickets the AI isn’t confident about don’t get blindly processed.
The goal isn’t to remove humans from customer support.
It’s to reduce the repetitive sorting and routing work, so the team can spend more time on the requests that actually need their attention.
Would you automate customer support differently? I’d love to hear how you’d approach it.
Ibrahim's avatar
Really solid approach, especially adding the Unclear path instead of forcing the AI to make a decision every time.
Technical question: how are you handling AI confidence/scoring before routing a ticket? Are you using a structured JSON output with a confidence threshold, or...
Paul 's avatar
Yeah, I’m handling it with a structured output rather than letting the AI make the routing decision completely on its own. The AI returns the category along with a confidence score, and I use a threshold to decide whether it should be routed automatically or sent to the Unclear...
Anselmo's avatar
The Unclear lane is exactly the right guardrail. I’d add two production checks: redact PII before sending ticket text to the model, and measure false-positive/false-negative rates by category rather than a single accuracy score. Logging resolutions and human overrides back into...
Paul 's avatar
Yeah, definitely. I’m keeping the original ticket and the decision trace so there’s a record of what the AI decided and why. That way it’s easier to audit, debug, or replay when needed. And yeah, I like the idea of feeding the human overrides back into the dataset too.
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