Companion videos

The AI customer support agent that resolves 70% of tickets — companion videos

The article walks through what a "70% resolution rate" support agent actually looks like in practice — triage, knowledge retrieval, action-taking, and clean escalation. These videos provide the build-from-scratch view. The primary pick is the most-watched practical guide to assembling customer-facing AI agents; the second is a focused n8n build aimed at exactly this kind of ticket-deflection workflow.

Primary pick

3:50:39

How to Build & Sell AI Agents: Ultimate Beginner's Guide

Liam Ottley

Customer support agents are Liam's bread-and-butter use case, and a sizeable chunk of this course is given over to RAG-grounded chatbots, escalation logic, and the no-code stack he uses with real clients (Botpress, Voiceflow, Make, n8n). The "anatomy of an agent" section in particular maps almost one-to-one onto the article's triage → answer → action → handoff structure.

What you should get from this: See how a support agent combines retrieval, escalation and workflow tools before production hardening.

Watch or know first: No coding needed — the stack is no-code; just budget for the near-four-hour runtime.

AI Expert note: It's a build-and-sell agency course, so skip the selling chapters if you only want the build — the 'anatomy of an agent' section is the piece that maps onto the article.

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Also worth watching

31:19

How To Build An AI Customer Support Agent with n8n (free template)

Bart Slodyczka

A practical n8n workflow that plugs into Zendesk, Gorgias or Freshdesk, replies to tickets with a RAG-backed answer, and feeds solved tickets back into the knowledge base. Closest match on YouTube to the architecture the article describes.

What you should get from this: You can assemble an n8n support workflow that answers tickets with RAG-backed replies and feeds solved tickets back into the knowledge base.

Watch or know first: Basic n8n familiarity and access to a helpdesk like Zendesk, Gorgias or Freshdesk.

AI Expert note: Kept despite the small channel because it's the closest YouTube match to the article's architecture, down to feeding solved tickets back into the knowledge base.

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