This blog post breaks down the massive 8-hour masterclass by Nate Herk on building no-code AI agents using n8n. Whether you are a complete beginner or looking to scale your AI agency, here is the step-by-step journey covered in the course.
Step 1: Understanding the Opportunity and Foundations [00:43]
Before diving into the technical setup, it’s essential to understand why AI agents are the current goldmine.
- The Goal: Move from simple automations to “agentic” systems that can make decisions and act autonomously [05:20].
- Setup: Start by signing up for a free two-week trial of n8n.
- UI Familiarity: Learn the layout, including how to create your first workflow, name it, and save your progress [17:19].
Step 2: Mastering Data with JSON [29:55]
AI models (GPT-4, Claude) communicate via JSON. To build effective agents, you must understand how to handle this data.
- JSON Structure: Learn to identify “keys” and “values,” and distinguish between different variable types like strings and arrays [47:29].
- Drag-and-Drop Mapping: n8n makes it easy to take output from one node (like an AI response) and “map” it into a new field using a Set node without writing code [23:26].
Step 3: Building Your First RAG System [59:07]
Retrieval-Augmented Generation (RAG) allows your AI to “read” your documents and answer questions based on them.
- Trigger: Set up a Google Drive node to trigger whenever a new file is uploaded [59:07].
- Download: Use a second Google Drive node to actually download the file’s content [59:32].
- Chunking: Use a Recursive Character Text Splitter to break long documents into smaller “chunks” (e.g., 1000 characters) so the AI can process them without losing context [01:05:07].
- Vector Storage: Send these chunks to a database like Pinecone so the agent has long-term memory.
Step 4: Connecting Tools and Memory [01:11]
An agent is only as good as the tools it can use.
- Tool Calling: Configure your agent to decide which tool is best for a task—for example, using Perplexity for web research or a Gmail node for sending emails [05:08].
- Prompting: Write a “System Message” that defines the agent’s role and rules. You can use expressions to drag in dynamic data like the content of an email [01:16:34].
Step 5: Advanced Architectures and MCP Servers [01:17]
As you progress, the course moves into complex setups:
- Multi-Agent Systems: Building workflows where agents talk to each other to complete multi-step projects.
- Webhooks: Triggering your AI agents from external apps.
- MCP Servers: Setting up Model Context Protocol (MCP) servers for even deeper integrations and self-hosting your n8n instance [01:31].
Step 6: Scaling and Strategy [08:23:44]
Once you know how to build, you need to know how to sell and scale.
- The “Hybrid” Approach: For massive data sets (millions of users), Nate recommends a mix of n8n for orchestration and custom Python scripts for heavy processing [08:25:06].
- Monetization: Nate shares lessons from his own agency, where he generated over $500k in revenue by providing these solutions to businesses [00:09].
Conclusion and Next Steps
The path from beginner to AI expert is now laid out in this 8-hour roadmap.
- Watch the full video: Build & Sell n8n AI Agents
- Join the Community: Nate offers a paid community for those coming from no-code backgrounds to collaborate and get live help [08:26:08].
Action Item: Start by building one simple automation today—like an AI that summarizes your emails—and build from there!
