231 minutesHow to Build & Sell AI Agents: Ultimate Beginner's Guide
See how a support agent combines retrieval, escalation and workflow tools before production hardening.
Nate Herk | AI Automation. Nate is one of the most-watched practitioners on n8n specifically, and this masterclass goes deeper than the Futurepedia primer — memory, multi-agent setups, error handling, real business workflows. Reach for it once your lead-triage agent works and you want to extend it.
The concepts are valuable, but long platform masterclasses age at the UI level. Use this for patterns and failure modes; validate every node, credential and retry setting against the current n8n docs before production use.
Extend a simple agent into multi-step workflows with memory, error handling and realistic business integrations.
Have built or watched the primary n8n agent workflow first.
Last reviewed: May 18, 2026
Continue through the same learning path with the next curated companion videos.
231 minutesSee how a support agent combines retrieval, escalation and workflow tools before production hardening.
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Hand-picked external courses that go deeper on this topic.
Anthropic Academy
MCP is the protocol that's quietly replacing one-off tool integrations across the AI tooling ecosystem. Learn it from the source. By the end you'll have built and deployed your own MCP server, connected an LLM client to it, and understood why this standard is the closest thing the field has to USB-C.
João Moura (Founder, CrewAI)
Doubles as our sales and customer-support vertical pick and a genuinely practical agent-building course: you build an agentic sales pipeline (lead scoring, personalized outreach) and a customer-support data-insights pipeline as two of the five hands-on projects, taught by CrewAI's own founder. Requires basic Python, so it sits with our other builder-track courses rather than the no-code picks.
Hugging Face
The clearest open-source treatment of agentic systems available. Anchored in the three frameworks engineers actually evaluate (smolagents, LlamaIndex, LangGraph) rather than one vendor's stack. Concludes with a benchmark assignment and public leaderboard — accountability your team can verify.