Overview
This lecture introduces building AI agents in n8n, covering foundational concepts, key system components, node types, hands-on workflow creation, memory integration, tool usage, and agent chaining.
Introduction to Agentic Systems
- Agentic systems consist of agents and workflows built to automate tasks.
- Workflows are predefined automations triggered by specific events and always produce a predictable output.
- Agents use large language models (LLMs) to dynamically decide which tools and actions are needed based on user input.
- Agents can have access to multiple tools and can call other agents within workflows.
n8n Platform Basics
- The n8n homepage displays workflows, credentials (API keys), and executions (workflow runs).
- Workflows are grouped into projects, and new workflows are created via the "Create Workflow" button.
Node Types in n8n
- Triggers: Start automations based on events or user input (e.g., chat message).
- Actions: Perform operations in external apps/services (e.g., Google Sheets).
- Utilities: Transform or manage data (e.g., filters, data storage).
- Code: Run custom code, HTTP requests, or set web hooks.
- Advanced AI Agent: Makes workflows agentic and allows AI-driven decisions.
Building a Basic AI Agent Workflow
- Start with a trigger node (e.g., on chat message) to initiate the workflow.
- Add the AI Agent node (brain of the system) and connect a chat model (e.g., OpenAI's GPT).
- Credentials (API keys) are essential for connecting to LLM providers.
Adding Memory to Agents
- Without memory, AI agents can't track conversation context.
- Add a window buffer memory node to store recent chat history for context-aware responses.
Extending Agents with Tools
- Tools allow agents to perform specific actions, such as database searches or updates.
- Example: Connect Airtable to search a home inventory database.
- Define tool descriptions to help the agent understand what each tool does.
Updating Databases with Agents
- Add additional tool nodes for updating records in Airtable.
- Use dynamic expressions from AI to map conversation content to database fields (e.g., record ID, new quantity).
Multi-Agent and Workflow Chaining
- Agents can call other workflows or agents, enabling modular automation (e.g., inventory agent called by another agent).
- This approach allows complex, layered agentic ecosystems.
Key Terms & Definitions
- Agentic System — An environment of interacting agents and workflows making dynamic decisions.
- Workflow — A set of predefined automation steps resulting in a fixed output.
- Agent — An LLM-powered automation that chooses which tools to use based on user input.
- Node — Individual functional components (triggers, actions, utilities, code, AI) in n8n workflows.
- Tool — External application or utility accessible by the agent for task execution.
- Memory — Feature that allows agents to retain and use recent conversation context.
Action Items / Next Steps
- Practice creating a basic AI agent workflow in n8n with memory and tool integration.
- Explore adding and configuring various tool nodes for agent expansion.
- Consider joining the AI Foundations community or complete additional modules for deeper learning.