Course Outline
Day 1: Workflows and AI Integration
Introduction
- Determining when to automate, when to include AI steps, and when to deploy agents
- n8n deployment models: cloud versus self-hosted, and their implications for data privacy
Core n8n Concepts
- Understanding workflows, nodes, triggers, and execution cycles
- Transferring data between nodes via drag-and-drop fields, eliminating the need for syntax
- Analyzing execution logs to diagnose and resolve issues
Application and Data Integration
- Configuring webhooks and form-based triggers
- Utilizing spreadsheet, email, and calendar nodes
- Managing credentials and authentication for third-party services
- Detailed walkthrough of the HTTP Request node
Incorporating AI
- Integrating models into existing workflows
- Crafting prompts for consistent and reliable output
- Ensuring structured outputs and automatically correcting malformed responses
- Classifying and extracting data from emails, forms, and documents
Logic and Actions
- Implementing branching logic with If and Switch nodes
- Adding human-in-the-loop approval steps before actions are executed
- Logging results to spreadsheets and distributing notifications
Practical Session: First End-to-End Workflow
- Transitioning from an incoming request to a classified, logged, and routed outcome
Day 2: Agents and Operational Management
AI Agents
- Defining agents: the role of models, memory, and tools
- Choosing between fixed workflows and autonomous agents
- Granting agents access to internal documents and spreadsheets
Actionable Agents
- Distinguishing between read-only and write-capable tools
- Defining system prompt rules: mandatory fields, prohibition of hallucinations, and prevention of duplicate records
- Testing agent robustness through negative test cases
Communication Channels
- Setting up chat interfaces for internal use
- Linking agents to specific messaging platforms
Reliability and Monitoring
- Configuring error workflows, retry mechanisms, and alerts
- Reviewing logs and execution history
- Managing costs associated with model usage and executions
Data Privacy and Security
- Understanding data transmission when external models are invoked
- Demonstration of self-hosting and local model capabilities
- Providing a checklist for review with IT or legal departments
AI-Assisted Workflow Development
- Using AI assistants to draft initial workflow structures
- Reviewing the draft: analyzing node functions, data sources, and handling of empty data scenarios
Capstone Project and Next Steps
- Participants finalize and present a workflow featuring an AI step and an agent applied to their specific process
- Developing a maintenance plan and identifying future automation opportunities
Deliverable: A functional workflow including an AI component, a data-connected agent, error handling protocols, and a comprehensive review checklist.
Requirements
- No programming background is necessary
- Familiarity with web-based applications and spreadsheet tools
- Identification of at least one repetitive process within your current role
Target Audience
- Professionals in business, operations, and client-facing 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
