Agentic AI for Beginners: Build No-Code AI Agents with n8n

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.

Explore what generative AI is and how it differs from traditional AI and machine learning, including the training, testing, and production steps, and examples like GPT, DALL-E, and Whisper.

Explore how agents reason toward a goal, decide actions, and execute with tools using LLMs as a reasoning engine. See uses in healthcare, finance, HR, operations, retail, and customer support.

Explore agentic frameworks that handle thinking, decisions, tool calls, memory, guardrails, and humans in the loop, and compare code-first versus no-code options like LangChain, LangGraph, N8n.

Explore how n8n uses node-based automation to connect triggers and actions, moving data between apps, and how an AI agent node uses a language model, memory, and tools.

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.

Set up and verify OpenAI, Pinecone, Gmail, Google Sheets, and Google Drive credentials in n8n by creating API keys and OAuth connections, then paste and save them.

Implement your first agentic workflow by linking a chat trigger to an AI agent using the OpenAI chat model with date time, calculator, and Wikipedia tools and memory to respond.

Create your first agentic workflow in n8n by adding an ai agent node and a chat trigger, then wire an OpenAI chat model with Wikipedia, calculator, and date/time tools.

Discover how to configure memory for your agent using simple memory, add a memory node, and preserve context so the agent can summarize content in two points.

Explore how an agentic workflow in n8n creates a unique session ID for each chat message, routes input to the AI agent, and supports testing, publishing, and session management.

Learn how embeddings convert words and sentences into high-dimensional vectors, how vector stores index and retrieve similar chunks, and apply to document similarity and search.

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.

Create a Pinecone index to store embeddings and chunks, choosing a model like text embedding 3 small with a 1536-dimension vector for your n8n workflow.

Create a vectorization pipeline in n8n by downloading the PTO policy from Google Drive and indexing it in Pinecone with OpenAI embeddings and text splitting into chunks with 50-character overlap.

Explore RAG, or retrieval augmented generation, and how to use embeddings and a vector store to retrieve relevant data, chunk documents, and feed context to an LLM via prompts.

Learn to implement retrieval augmented generation (rag) in a no-code workflow with n8n, building an AI agent that uses Pinecone vector store, embeddings, and OpenAI to answer only retrieved context.

Learn how model context protocol (MCP) standardizes connecting AI agents to tools and data sources using a client-server model, with JSON RPC over HTTP or standard IO and streaming capabilities.

Decouple the agent from DateTime, Calculator, and Wikipedia by using an MCP server and MCP client in n8n to expose tools across workflows.

Learn how to implement human-in-the-loop (h-i-t-l) patterns in no-code ai agents, including accept-reject, review tool calls, fall back on uncertainty, and state editing to guide workflows.

Create an agent that helps a general physician recommend the right specialist from a Google Sheets list, generating a tailored email and letter, with human-in-the-loop approval before sending.

Build a clinical assistant agent in n8n, configure a system prompt to select the medical specialist, generate a referral email using a json schema, and test by sending through Gmail.

Configure a human-in-the-loop workflow in n8n to let a doctor approve or reject AI-generated email subject and body via chat or other channels before sending, with a conditional flow.

guardrails protect AI workflows by securing input and output, using n8n guardrails in violations or sanitizers mode to block violations or sanitize with placeholders, including handling personal data and keys.

Implement guardrails in a no-code ai agent workflow to filter and sanitize chat data, block violations, and ensure only safe messages reach the agent, with optional downstream actions like email.

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.

Implement evals by adding an evals trigger that uses Google Sheets as the data source, routing through guardrails to the AI agent and evaluating correctness with an LLM.

Capture and map evaluation inputs and outputs in an n8n workflow, add them to a dashboard and Google Sheets metrics, and compare runs to improve correctness and prompts.

Introduce multi-agent workflows where a supervisor agent coordinates sub agents to tackle tasks. Deploy research, draft, and review agents in a no-code n8n setup to improve quality and maintainability.

Create a multi-agent workflow in n8n using supervisor, research, draft, and review agents, configured with system messages and a chat model, to generate a final response.