Summary of "Build & Sell n8n AI Agents (8+ Hour Course, No Code)"
Course Overview
- The course is designed for complete beginners with no coding experience.
- Goal: Build powerful no-code AI agents and workflows using n8n.
- Instructor has earned over half a million dollars teaching AI Agent building.
- By course end, students will have 15+ AI automations ready.
- The course is comprehensive, timestamped for easy navigation, and covers foundational knowledge through advanced multi-agent architectures.
Key Concepts & Foundations
What is an AI Agent vs AI Workflow?
- Large Language Model (LLM) alone: Takes input, generates output (e.g., ChatGPT writing an email), but cannot take actions.
- Tools: Integrations like Gmail, CRM, Google Sheets, Airtable, etc. that perform actions.
- AI Workflow: Linear, deterministic process combining LLM + tools in fixed steps (e.g., new lead → research → draft email → send email).
- Pros: Reliability, cost efficiency, easier debugging, scalability.
- AI Agent: Autonomous, non-deterministic system with a "brain" (LLM + memory) and instructions (system prompt) that decides which tools to use and when.
- More flexible but slower, costlier, and error-prone if used unnecessarily.
- Decision: Build workflows first (crawl, walk, run), then move to agents.
Anatomy of an AI Agent
- Input → AI Agent → Output.
- Agent consists of:
- Brain: LLM + memory (short-term and long-term).
- Instructions: System prompt defining role, tools, and usage guidelines.
- Memory allows context retention across conversations.
- System prompt is static; user input is dynamic.
Setting up n8n (Naden)
- Sign up for a 14-day free trial.
- Dashboard overview: executions (runs), workflows, projects, admin panel.
- Workflows consist of triggers + nodes (actions, AI, data transformations).
- Credentials (API keys, OAuth tokens) needed to connect to external services.
- Nodes have input, configuration, and output panels.
- JSON is the core data format; understanding JSON structure and manipulation is crucial.
- Binary data represents non-text files (images, PDFs, videos).
Step-by-Step Workflow Builds
1. RAG Pipeline and Chatbot
- RAG: Retrieval Augmented Generation — agent queries a vector database to answer questions.
- Vector database (e.g., Pinecone) stores document chunks as vectors based on meaning.
- Workflow:
- Tools used: Google Drive, Pinecone, Open Router (for multiple LLM models).
2. Customer Support Workflow
- Trigger: New Gmail email.
- AI text classifier node categorizes email (customer support, other).
- Customer support branch:
- Workflow is deterministic and more cost-effective than agentic approach.
3. LinkedIn Content Creator
- Trigger: Scheduled or manual.
- Google Sheets node pulls content ideas (status = to-do).
- HTTP request node calls Tavly API for web search.
- AI Agent creates LinkedIn post from research.
- AI Agent creates image prompt from post.
- HTTP request node calls OpenAI image generation API.
- Google Sheets node updates row with created content.
- Tools used: Google Sheets, Tavly API, Open Router, OpenAI image generation.
Bonus: Invoice Processing Workflow
- Trigger: New invoice PDF in Google Drive folder.
- Download invoice, extract text from PDF.
- AI information extractor extracts key fields (invoice number, client name, amount, dates).
- Append row to Google Sheets invoice database.
- AI crafts email to billing team.
- Gmail node sends email.
- Workflow handles invoices with varying formats using AI extraction.
APIs & HTTP Requests
- APIs = Application Programming Interfaces, enabling communication between systems.
- HTTP requests (GET, POST) used to interact with APIs.
- Native integrations in n8n wrap API calls with UI; HTTP request nodes allow custom API calls.
- Important API call components:
- Method (GET, POST)
- Endpoint URL
- Query parameters
- Header parameters (often for authorization)
- Body parameters (data sent in POST requests)
- Use cURL import feature in n8n to quickly set up HTTP requests.
- Common API errors:
- 200 = success
Category
Educational