Building Agentic AI Automation with n8n: A Complete Practical Guide from Form Workflows to RAG Chatbots

This article introduces a complete practical tutorial on agentic AI automation, covering four progressive projects from basic form automation to advanced RAG chatbots. Developed by Singapore's Tertiary Courses, the course uses the n8n workflow platform, OpenAI large language models, and vector storage technology, providing learners with complete workflow files that can be directly imported and run, along with supporting web interfaces.

• Original Author/Maintainer: tertiarycourses
• Source Platform: GitHub
• Original Title: TGS-2023035977-Agentic-AI-Automation-with-n8n
• Original Link: https://github.com/tertiarycourses/TGS-2023035977-Agentic-AI-Automation-with-n8n
• Source Publication/Update Time: 2026-06-03

With the maturity of large language model technology, AI automation is evolving from simple task execution to more complex agentic models. Agentic AI can not only handle single tasks but also understand context, call tools, manage memory, and interact with external systems. This leap in capability brings new automation possibilities for enterprises and developers.

This WSQ-certified course material developed by Singapore's Tertiary Courses provides a complete path to learn agentic AI automation from scratch. Centered around the n8n open-source workflow automation platform, the course guides learners through four progressive practical activities to master the full skills from basic form processing to advanced RAG chatbot development.

This project uses a modern AI automation technology stack, with components working together to form a complete solution:

n8n is an open-source workflow automation tool that uses visual node editing, allowing non-programmers to build complex automation processes. Its core advantages include:

• Visual Editing: Drag-and-drop node connections for intuitive workflow building
• Rich Integrations: Pre-built nodes supporting hundreds of applications and services
• Self-hosting Options: Can be deployed locally or on private clouds to ensure data security
• Native AI Support: Built-in nodes for AI Agent, vector storage, memory management, etc.

The project uses OpenAI's GPT series models as the dialogue engine, and its embedding models for vectorization processing:

• GPT-4.1-mini: A lightweight yet powerful dialogue model
• Text Embedding: High-quality text vectorization service
• Function Calling: Structured output supporting tool invocation

Tavily is a search API designed specifically for AI applications, providing structured search results as an external knowledge source for agents. Compared to traditional search engines, Tavily's returned results are more suitable for large language models to understand and reference.

The project uses in-memory vector storage to implement the Retrieval-Augmented Generation (RAG) pattern, allowing chatbots to answer questions based on uploaded documents.