How to Build AI Agents INSTANTLY with n8n’s NEW NATIVE AI Builder
Mark Kashef
0:00 / 0:00
How to Build AI Agents INSTANTLY with n8n’s NEW NATIVE AI Builder
37 546 просмотров · 1 год назад
Mark Kashef
84,6 тыс. подписчиков
37 546 просмотров · 1 год назад
🚀 Gumroad Link to Assets in the Video: https://bit.ly/4fWiflA
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What if you could type a plain-English prompt and get a production-ready automation in seconds? In this 22-minute hands-on demo, I put n8n’s brand-new AI Assistant Builder (Prompt → Workflow) through real projects—showing how it plans the architecture, wires nodes, explains each step, and even helps you generate mock data to test. You’ll see three builds: a lead-qualification pipeline (Calendly → GoHighLevel with OpenAI + Perplexity + Outlook), a Fireflies-powered meeting analysis that branches to a nurture sequence, and a multi-agent LangChain “content swarm” where one agent uses another as a tool. I also cover when to use an AI Agent node vs a simple LLM call, how to fix red-state nodes fast (credentials & params), and how to iterate with the sidebar co-pilot. Note: this feature is rolling out in phases—if it’s not in your account yet, it’s coming soon. Perfect for beginners leveling up and builders who want to go from idea → working automation without MCP servers, giant JSONs, or endless copy-paste.
⏳ TIMESTAMPS:
00:00 – n8n’s AI Assistant Builder changes everything
00:34 – Why old hacks broke (giant prompts, JSONs, MCP)
01:00 – What Prompt → Workflow actually does
01:18 – Rollout note: phased access & where to find it
01:34 – Prompting tips: name your tools & outcomes
02:00 – Built-in templates (Invoice, RAG assistant)
02:36 – Still possible: start manually if you prefer
02:49 – Meet the sidebar co-pilot & build log
03:22 – Test #1 prompt: Calendly → GoHighLevel lead qual
04:18 – Plan → nodes → connections auto-wired
05:12 – Post-build checklist: creds, params, models
06:01 – Flow explained (trigger → research → score → store → notify)
06:46 – Clearing red errors & picking models
07:20 – Prefilled prompts, placeholders, dynamic vars
08:10 – Generate & pin mock Calendly JSON; execute run
09:04 – Results review: what worked, what to tweak
10:42 – 60–70% head start; what’s left to ship
11:20 – Architecture: AI Agent vs plain LLM node
11:58 – Extension idea: Fireflies transcript → nurture path
12:40 – Swap in community node; get transcript step
13:10 – Sales-readiness (BANT) prompt + drafted emails
14:56 – New canvas: LangChain agent brief (autonomous handler)
15:29 – Build output; wrong trigger spotted
16:02 – Iterate in chat: replace webhook with Form node
16:31 – Open form, submit, pin data, re-run
17:59 – Test #3: multi-agent “content swarm”
18:31 – Master/worker agents with shared tools
19:40 – Final setup: Sheets/Docs, credentials, models
20:10 – Takeaways: beginner → intermediate with a tutor
21:32 – Early AI-dopters access & outro
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