Creating Low-Code / No-Code AI Agents Using n8n

Introduction to AI Agents

• What are AI agents?

• Event-driven automation concepts

• LLM integration in workflows

• Agent vs simple automation

• Enterprise use cases

n8n Platform Fundamentals

• n8n architecture

• Nodes & connections

• Triggers and workflows

• Webhooks

• Environment setup (Cloud vs Self-hosted)

Building Basic AI Workflows

• Connecting to LLM APIs

• Creating prompt templates

• Handling JSON responses

• Conditional logic nodes

• Error handling basics

Integrating External Applications

• Email automation

• Slack integration

• Google Sheets / Databases

• REST API calls

• Authentication handling

Hands-on Labs

• Install and configure n8n

• Build first LLM-powered workflow

• Create Slack-based AI responder

• Add conditional branching

• Implement error handling

Agent Design Patterns

• Planner-executor model

• Tool invocation pattern

• Sequential vs parallel workflows

• Task chaining

• State persistence strategies

Memory & Context Management

• Session-based memory

• Database-backed memory

• Context injection patterns

• Managing token limits

• Data filtering

Advanced Workflow Logic

• Loops and iteration

• Retry & fallback strategies

• Rate limiting

• API error handling

• Logging & monitoring

Integrating Enterprise Systems

• CRM integration

• Ticketing systems

• Document storage systems

• Internal APIs

• Secure credential management

Hands-on Labs

• Build multi-step task agent

• Integrate CRM or mock API

• Add database memory

• Implement retry mechanism

• Build document processing workflow

Production Deployment Architecture

• Self-hosted vs cloud deployment

• Environment management

• Scaling workflows

• Concurrency handling

• Monitoring execution logs

Security & Governance

• Credential management

• Role-based access control

• Data privacy considerations

• Secure API usage

• Audit logging

Responsible AI & Risk Mitigation

• Prompt injection risks

• Data leakage prevention

• Guardrails implementation

• Human-in-the-loop validation

Performance & Cost Optimization

• Token optimization

• Efficient workflow design

• Caching responses

• Reducing redundant API calls

Capstone Project: Enterprise AI Automation Agent

• Define real-world business use case

• Design multi-step AI agent

• Integrate LLM + enterprise system

• Add monitoring & governance controls

• Present workflow architecture and trade-offs

Upon completion, participants will be able to:

• Build AI-powered workflows using n8n

• Design multi-step, low-code AI agents

• Integrate enterprise applications securely

• Implement memory and state management

• Deploy scalable and monitored automation workflows

• Apply governance and Responsible AI principles