Course Outline
Day 1: Workflows and the AI component
Introduction
- Identifying when to automate, when to apply an AI step, and when to deploy an agent
- n8n in context: comparing cloud and self-hosted deployments and their implications for company data
n8n fundamentals
- Understanding workflows, nodes, triggers, and executions
- Transferring data between nodes by dragging fields, eliminating the need for syntax entry
- Analyzing execution logs to identify and resolve issues
Connecting applications and data
- Configuring Webhooks and form triggers
- Utilizing nodes for spreadsheets, email, and calendars
- Managing credentials and authentication for third-party services
- Step-by-step guide to the HTTP Request node
The AI component
- Incorporating a model into a workflow
- Crafting prompts to ensure reliable outputs
- Generating structured outputs and automatically correcting malformed responses
- Classifying and extracting data from emails, forms, and documents
Decision-making and actions
- Implementing branching logic using If and Switch nodes
- Setting up approval steps that require human verification before action
- Recording results in spreadsheets and dispatching notifications
Practical lab: The first end-to-end workflow
- Processing an incoming request into a classified, recorded, and routed outcome
Day 2: Agents and operations
Agents
- Defining an agent: the combination of model, memory, and tools
- Choosing between a fixed workflow and an agent
- Granting agents access to company documents and spreadsheets
Active agents
- Distinguishing between tools that read data and those that write it
- Establishing system prompt rules: enforcing required fields, prohibiting invented data, and preventing duplicate records
- Testing agent robustness by attempting to introduce errors
Communication channels
- Implementing a chat interface for internal usage
- Linking the agent to a single messaging channel
Reliability
- Configuring error workflows, retry mechanisms, and notifications
- Reviewing logs and execution history
- Managing model usage and execution costs
Data and privacy
- Determining what data exits the company when a model is invoked
- Self-hosting and local models: instructor-led demonstration
- Utilizing a checklist for review with IT or legal departments
Building workflows with AI
- Using an AI assistant to draft a workflow
- Reviewing the draft: analyzing each node’s function, data sources, and handling of empty data
Final project and next steps
- Participants will complete 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 step, an agent integrated with company data, error handling mechanisms, and a review checklist.
Requirements
- No prior programming experience is necessary
- Familiarity with using web-based applications and spreadsheets
- A clear understanding of a repetitive process within the participant’s own role
Target Audience
- Professionals in business, operations, and customer-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
