Course Outline
Day 1: Workflows and the AI Step
Introduction
- Identifying when to automate, when to use an AI step, and when to deploy an agent
- n8n in context: understanding cloud versus self-hosted deployments and their implications for company data
n8n Fundamentals
- Understanding workflows, nodes, triggers, and executions
- Transferring data between nodes using drag-and-drop fields, eliminating the need for syntax input
- Interpreting execution logs to identify and resolve issues
App and Data Integration
- Configuring Webhooks and form-based triggers
- Utilizing nodes for spreadsheets, email, and calendars
- Setting up credentials and authentication for external services
- A step-by-step guide to the HTTP Request node
The AI Step
- Integrating a model into a workflow
- Crafting prompts that yield reliable outputs
- Achieving structured output and implementing automatic correction for malformed responses
- Classifying and extracting information from emails, forms, and documents
Decision Making and Actions
- Implementing branching logic with If and Switch nodes
- Incorporating approval steps to require human confirmation before actions are executed
- Recording results in spreadsheets and dispatching notifications
Practical Lab: Your First End-to-End Workflow
- Processing incoming requests from classification and recording to final routing
Day 2: Agents and Operations
Agents
- Defining an agent: components of model, memory, and tools
- Choosing between a fixed workflow and an agent-based approach
- Granting agents access to internal documents and spreadsheets
Action-Oriented Agents
- Distinguishing between tools that read data and those that write it
- Establishing system prompt rules: enforcing required fields, preventing data fabrication, and avoiding duplicate records
- Testing agent robustness by attempting to introduce errors
Communication Channels
- Setting up chat interfaces for internal use
- Connecting agents to specific messaging platforms
Reliability
- Implementing error workflows, retries, and alert notifications
- Analyzing logs and execution history
- Managing model usage and execution costs
Data and Privacy
- Determining what data leaves the organization when external models are invoked
- Demonstration of self-hosting and local models by the instructor
- Creating a review checklist for collaboration with IT or legal teams
Building Workflows with AI
- Using AI assistants to draft workflow structures
- Reviewing drafts: verifying node functions, data sources, and handling of empty data scenarios
Final Project and Next Steps
- Completing and presenting a personalized workflow featuring an AI step and an agent applied to your own process
- Developing a maintenance plan and identifying future automation opportunities
Deliverable: A functional workflow incorporating an AI step, an agent linked to company data, error handling, and a comprehensive review checklist.
Requirements
- No prior programming experience is necessary
- Familiarity with web-based applications and spreadsheets
- A clear understanding of at least one repetitive task within your current workflow
Target Audience
- Professionals in business, operations, and customer-facing roles
- Team leads and process owners
- Non-technical staff interested in building automations and AI agents using company data
Testimonials (3)
Strong confirmation that current LLMs are deployable for customer projects when the proper setup is in place.
Oliver Fromm
Course - Agentic AI for Enterprise Applications
Machine Translated
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
The mix of theory and practice and of high level and low level perspectives
