Course Outline
Day 1: Workflows and AI Integration
Introduction
- Determining when to automate, when to apply an AI step, and when to deploy an agent
- n8n in context: comparing cloud versus self-hosted environments and their implications for company data
n8n Fundamentals
- Understanding workflows, nodes, triggers, and executions
- Transferring data between nodes via drag-and-drop field selection, eliminating the need for syntax
- Analyzing executions to identify and resolve issues
App and Data Connectivity
- Utilizing webhooks and form triggers
- Integrating nodes for spreadsheets, email, and calendars
- Managing credentials and authentication for external services
- Step-by-step guidance on the HTTP Request node
The AI Component
- Incorporating a model into a workflow
- Crafting prompts for consistent and reliable outputs
- Enforcing structured output and automatically correcting malformed responses
- Classifying and extracting information from emails, forms, and documents
Decision Logic and Actions
- Implementing branching with If and Switch nodes
- Introducing approval steps to involve human oversight before action execution
- Recording results in spreadsheets and dispatching notifications
Practical Lab: First End-to-End Workflow
- Transforming an incoming request into a classified, recorded, and routed outcome
Day 2: Agents and Operational Management
AI Agents
- Defining an agent: comprising the model, memory, and tools
- Selecting between a fixed workflow and an agent-based approach
- Granting agent access to internal documents and spreadsheets
Action-Oriented Agents
- Distinguishing between tools for reading and writing data
- Establishing system prompt rules: ensuring required fields are present, preventing data fabrication, and avoiding duplicate records
- Stress-testing an agent by attempting to disrupt its logic
Communication Channels
- Setting up a chat interface for internal utilization
- Linking the agent to a single messaging platform
Reliability and Stability
- Configuring error workflows, retry mechanisms, and notifications
- Reviewing logs and execution history
- Managing model usage and execution costs
Data Security and Privacy
- Assessing data transmission when invoking external models
- Demonstrating self-hosting and local model usage by the instructor
- Providing a checklist for review with IT or legal teams
AI-Assisted Workflow Construction
- Using an AI assistant to draft initial workflow structures
- Reviewing drafts: understanding node functions, data sources, and handling empty data scenarios
Final Project and Future Steps
- Participants complete and present a workflow incorporating an AI step and an agent applied to their own process
- Developing a maintenance plan and identifying future automation opportunities
Deliverable: A functional workflow featuring an AI step, an agent connected to company data, robust error handling, and a comprehensive review checklist.
Requirements
- No prior programming experience is necessary
- Familiarity with web applications and spreadsheet software
- Identification of one repetitive process within their own workflow
Target Audience
- Professionals in business operations, management, and customer service roles
- Team leaders and process owners
- Non-technical staff aiming to build automations and AI agents using company data
Testimonials (3)
The trainer is patient and very helpful. He knows the topic well.
CLIFFORD TABARES - Universal Leaf Philippines, Inc.
Course - Agentic AI for Business Automation: Use Cases & Integration
Good mixvof knowledge and practice
Ion Mironescu - Facultatea S.A.I.A.P.M.
Course - Agentic AI for Enterprise Applications
The mix of theory and practice and of high level and low level perspectives
