Course Outline
Day 1: Workflows and AI Integration
Introduction
- Determining when to automate tasks, when to employ AI steps, and when to deploy full agents
- Understanding n8n’s context: cloud vs. self-hosted environments and their implications for corporate data
n8n Fundamentals
- Core concepts: workflows, nodes, triggers, and execution cycles
- Transferring data between nodes via drag-and-drop field mapping, eliminating the need for syntax entry
- Interpreting execution logs to identify and resolve issues
App and Data Integration
- Utilizing Webhooks and form-based triggers
- Working with spreadsheet, email, and calendar nodes
- Managing credentials and authentication for external services
- A detailed walkthrough of the HTTP Request node
AI Integration
- Incorporating AI models into workflow structures
- Crafting prompts to ensure reliable outputs
- Achieving structured outputs and automatically correcting malformed responses
- Classifying and extracting data from emails, forms, and documents
Decision Logic and Actions
- Implementing branching logic using If and Switch nodes
- Designing approval steps that require human verification before executing actions
- Recording results in spreadsheets and dispatching notifications
Practical Lab: First End-to-End Workflow
- Transforming an incoming request into a classified, logged, and routed outcome
Day 2: Agents and Operational Management
Agent Architecture
- Defining agents: the role of models, memory, and tools
- Selecting between fixed workflows and autonomous agents
- Granting agents access to internal documents and spreadsheets
Active Agents
- Differentiating between tools that read data and tools that write data
- Establishing system prompt rules: mandatory fields, preventing data hallucination, and avoiding duplicate records
- Testing agent robustness through adversarial scenarios
Communication Channels
- Setting up chat interfaces for internal use
- Connecting agents to a specific messaging channel
Reliability and Stability
- Implementing error workflows, retry mechanisms, and alert notifications
- Monitoring logs and execution history
- Managing model usage and execution costs
Data Security and Privacy
- Assessing data exposure when external models are invoked
- Exploring self-hosting and local models through instructor demonstrations
- Using a checklist for review with IT or legal teams
AI-Assisted Workflow Construction
- Utilizing AI assistants to draft initial workflow structures
- Reviewing generated code: analyzing node functions, data sources, and null-value handling
Final Project and Future Steps
- Participants finalize and present a workflow featuring an AI step and an agent applied to a personal process
- Developing a maintenance plan and identifying future automation opportunities
Outcome: A functional workflow incorporating an AI step, an agent linked to corporate data, error handling mechanisms, and a comprehensive review checklist.
Requirements
- No prior programming experience is necessary
- Proficiency with web applications and spreadsheet software
- A clear understanding of one repetitive process within your own workflow
Target Audience
- Professionals in business operations, customer service, and related roles
- Team leads and individuals responsible for process management
- Non-developers aiming to create automations and AI agents using internal 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
