
Explore ai, genai, and agentic ai concepts, and learn ai-agentic frameworks while setting up n8n to build your first no-code ai agent with llms, memory, tools, and vectors.
Define artificial intelligence by comparing it to human intelligence, and explain how data, senses, and algorithms let machines learn, reason, and solve new problems with models.
Set up your n8n cloud account by visiting the site, entering your full name, password, and a unique account name, and verify your email code to start the 14-day trial.
Discover vectorization pipeline use case in a no-code n8n setup: load a PTO policy PDF, split into 500-char chunks with 50-char overlap, and index embeddings in Pinecone for rag pipeline.
Explore sanitized text guardrails in the guardrails node, which mask data instead of blocking, with options like phone numbers, secret keys, URLs via custom regex, keeping the flow intact.
Define outcomes and eval metrics, collect data, run and analyze results with LLM judge, and refine prompts, workflows, or models to ensure ethics and guardrails for AI agents with RAG.
Evaluate your ai agent with the evaluation node in n8n, comparing actual outputs to ground-truth expectations from the evals_data_set.xlsx in the data folder.
Agentic AI does not have to be complex or intimidating.
This beginner-friendly course introduces Agentic AI using n8n in a simple, practical, and easy-to-follow way. This course helps you understand the core ideas behind AI agents by building small, understandable workflows step by step.
The goal of this course is to help you learn how to think in terms of agents and workflows, not just prompts.
You will start by creating a very simple AI agent and gradually add important concepts such as tool usage, basic retrieval (RAG), human approval flows, quality checks, and safety rules, all using n8n’s visual workflow builder. The examples are intentionally kept small and beginner-friendly.
No coding, complex math, or deep AI theory is required.
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What You Will Build
Throughout the course, you will build focused beginner-level examples, including:
• A simple AI agent that responds to task assigned by using the provided tools through MCP
• A basic HR Policy agent that uses Agentic RAG to answer employee queries
• A Clinical Assistant that helps doctors to automatically pick a specialist ,draft and sent a recommendation email
• A Human-in-the-Loop that sends the mail only after human approval
• Simple evaluation workflows to score AI responses for clarity and correctness
• Basic guardrails to block unsafe or sensitive outputs and route them for human review
Each example is designed to introduce one new concept at a time.
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What You Will Learn
By the end of this course, you will be able to:
• Understand what Agentic AI means in simple terms
• Understand the difference between chatbots and agents
• Build your first AI agents using n8n
• Use tools such as Google Drive, Google Sheets, and Email inside agent workflows
• Understand the basics of Retrieval-Augmented Generation (RAG)
• Implement Vectorization and Agentic RAG using PineCone Vector Store
• Access tools using Model Context Protocol (MCP)
• Learn when and why Human-in-the-Loop workflows are important
• Add simple evaluations to measure AI output quality
• Create Multi Agent Workflows
• Apply basic guardrails for safer AI behavior
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Who This Course Is For
This course is ideal for:
• Beginners who are curious about creating AI agents
• Developers and non-developers new to Agentic AI
• Automation and no-code or low-code learners
• Product managers exploring AI workflows
• Anyone looking for a clear and gentle introduction to Agentic AI
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Prerequisites
• Basic GenAI knowledge
• No prior AI, machine learning, or data science background required
• No programming experience required