Byte-Sized n8n AI Agent Series: From Automation to Autonomy

Description:

Shift your perspective from "If-This-Then-That" logic to "Reasoning-Then-Action."

In this 90-minute session, we move beyond static sequences to build dynamic AI Agents. You will learn the mechanics of the n8n AI Agent node, exploring how to give your AI "tools" to interact with the real world and "memory" to maintain long-term context. We will cap off the session with a "Research Sprint," where you will build a live autonomous agent capable of searching the web and synthesizing disparate data points into a cohesive report—all without human intervention.

Duration:

90 minutes

Course Code: BDT 535

Learning Objectives:

After this course, you will be able to:

1. Master the n8n AI Agent node and its core components (Tools, Memory, Model)
2. Implement advanced memory management to maintain conversational context

Construct an autonomous research agent that utilizes live search engines

Automation specialists, power users of n8n, and developers who have a baseline setup of local LLMs and want to transition from linear workflows to reasoning-based autonomous agents.

Must have n8n and Ollama set up on local machine

Course Outline:

The Anatomy of an AI Agent

1. 1. Agentic Reasoning: Understanding the difference between a “workflow” and an “Agent”
   2. The AI Agent Node: Deep dive into Model inputs, tools and system prompts
   3. Giving the Agent “Hands”: How tools allow the LLM to execute code and fetch data
   4. Lab: Setting up your first Agent node and defining its “Persona”
2. Memory Management: Giving the AI a Brain
   1. The Context Window Problem: Why "forgetting" happens in long conversations
   2. Window Memory Buffer: Maintaining the last “n” exchanges for immediate relevance
   3. Summary Memory: Using the LLM to compress past interactions for long-term efficiency
   4. Lab: Implementing and testing different memory types in a live chat interface

3. The Research Spirit: Building the Autonomous Researcher
   1. Search Tool Integration: Connecting Tavily and/or Google Search to your Agent
   2. Recursive Problem Solving: How agents break down a prompt into multiple search tasks
   3. Synthesizing Results: Prompt engineering for clean, cited outputs
   4. Lab: Build and run a "Research Agent" that provides a 3-point summary of a trending topic

Training material provided: Yes (Digital format)

Hands-on Lab: Students will be provided with docker compose file and n8n workflow JSON.