Build Production-Ready Low-Code AI Agents with n8n

Description:

Unlock the power of modern AI automation by learning to build fully local, production‑ready AI agents in just one day. This hands‑on workshop shows you how to transform n8n into a private, extensible AI automation engine running entirely on your own hardware—no coding required.

You’ll start by deploying a self‑hosted n8n environment with Docker, then progressively layer in real‑world capabilities: autonomous agent reasoning, document intelligence, vector‑based retrieval, and multi‑agent orchestration. Along the way, you’ll integrate both local LLMs (via Ollama and DeepSeek) and cloud models (via OpenRouter), giving you the flexibility to choose the right model for each task.

By the end of the day, you will have built a complete AI agent stack capable of research, data extraction, lead enrichment, email drafting, scheduling, and more powered by the Model Context Protocol (MCP) and connected to tools like Fire Crawl, Google Workspace, and custom sub‑workflows. You’ll walk away with reusable templates, a working local environment, and the confidence to design and deploy agentic systems for your business or clients.

This course is ideal for professionals who want to move fast, build real AI capabilities, and maintain full control over their data and infrastructure.

Duration:

1 Day

Course Code: BDT 532

Learning Objectives:

After this course, you will be able to:

1. Docker Introduction: Basic introduction to Docker as it makes it easier to build Agentic AI systems.
2. Vibe Coding & AI Velocity: Master Agent development IDE, Python, and spec-driven deployment to automate 80% of boilerplate coding .
3. Stateful Agents with LangChain/LangGraph: Move beyond linear chains to build cyclical state-machine-based agents with persistent memory and human-in-loop approvals.
4. Pydantic Agents with Pydantic AI: Leverage strict type-safety, dependency injection, and Pydantic structures for production-grade agentic behavior
5. The MCP Revolution: Build custom servers to connect LLMs directly to your SQL databases, local files, and internal APIs without exposing data to the public web.
6. Multi-Agent Orchestration: Use to tools like n8n, LangFlow to manage a “digital workforce” where specialized agents collaborate on complex business goals.
7. Enterprise RAG & Search: Optimize document retrieval using hybrid search, advanced chunking, and vector databases (Pinecone, Supabase, Chroma). (RAG: Retrieval Augmented Generation)
8. Local AI and Sovereignty: Deploy high-performance models like DeepSeek, Llama locally using Docker for total data privacy.
9. Voice and Vision Automation: Build real-time voice assistants and automated media pipelines.
10. AI Security & Compliance: Practical training on the EU AI Act, preventions for prompt injection, and data poisoning defense.

By the end of this AI Bootcamp, you will have the confidence and competence to tackle building Agentic AI systems.

This course is designed for Entrepreneurs, Product Managers, AI Engineers, and QA Engineers. It is geared towards anyone looking to build production-grade AI agents quickly without writing code, regardless of technical background.

A computer capable of running Docker Desktop and a basic understanding of AI concepts (LLMs). No coding experience required.

Course Outline:

1. Local Environment Setup with Docker
   1. N8n Cloud vs Self-Hosted: Understanding the benefits of local deployment
   2. Docker installation: Setting up Docker Desktop on your computer
   3. Lab: Deploying n8n in Docker Container and configuring the admin panel
   4. Lab: Connecting Local AI: Integrating Ollama/Open Router for model access
2. Foundations of Agentic Workflows
   1. What is an AI Agent? Understanding autonomy vs linear automation
   2. n8n canvas basics: Nodes, Triggers, and the AI Agent Node
   3. Memory and Reasoning: How LLMs simulate context in n8n
   4. Lab: Building a “research agent” using local LLM
3. Data Handling and Google Integration
   1. Understanding JSON: Key-value pairs and data transformations in n8n
   2. Authentication: Setting up OAuth2 for Google Services
   3. Lab: Automating stock portfolio tracker with Google Sheets
   4. Lab: Building an AI Agent to draft Gmail replies based on incoming content
4. Advanced Document Intelligence & Agentic RAG
   1. Text Extraction: Processing PDFs and unstructured data
   2. Agentic RAG: Giving agents “proprietary knowledge” via vector stores
   3. Structuring outputs: Using Structured Output Parser for consistent data
   4. Lab: Building a PDF-to-Insight workflow using Fire crawl for web data
5. Multi-Agent Systems with MCP
   1. Intro to Model Context Protocol (MCP): Host, Client, and Server explained
   2. Sub-workflows: Creating a modular, reusable “skills” for your agents
   3. Orchestration: managing multiple agents for complex Go-To-Market tasks
   4. Lab: Evaluating model performance and cost estimation

Training material provided: Yes (Digital format, including Docker Compose files)

Hands-on Lab: Lab instructions and n8n workflow JSON