Course Outline
Day 1: Workflow Fundamentals and AI Integration
Introduction
- Determining when to automate, when to employ an AI step, and when to deploy a full agent
- n8n in context: Implications of cloud versus self-hosted deployment for data governance
n8n Core Concepts
- Understanding workflows, nodes, triggers, and execution cycles
- Transferring data between nodes via visual field mapping rather than syntax coding
- Interpreting execution logs to diagnose and resolve issues
Application and Data Integration
- Implementing webhooks and form-based triggers
- Utilizing nodes for spreadsheets, email, and calendar management
- Configuring credentials and authentication for third-party services
- Step-by-step usage of the HTTP Request node
The AI Step
- Integrating AI models into the workflow
- Crafting prompts to ensure reliable output
- Enforcing structured outputs and automatically correcting malformed responses
- Classifying and extracting data from emails, forms, and documents
Logic and Actions
- Implementing branching logic using If and Switch nodes
- Introducing approval steps that require human intervention before critical actions
- Logging results to spreadsheets and dispatching notifications
Practical Lab: First End-to-End Workflow
- Transforming an incoming request into a classified, recorded, and routed outcome
Day 2: Agent Development and Operational Management
Agents
- Defining agents as combinations of model, memory, and tools
- Evaluating when to use a fixed workflow versus a dynamic agent
- Granting agents access to corporate documents and spreadsheets
Agentic Actions
- Differentiating between read-only and write-enabled tools
- Establishing system prompt rules: enforcing required fields, preventing data hallucination, and avoiding duplicate records
- Stress-testing agents by attempting to introduce errors
Communication Channels
- Setting up internal chat interfaces
- Connecting agents to specific messaging platforms
Operational Reliability
- Configuring error handling workflows, retry mechanisms, and alerts
- Analyzing logs and execution history
- Managing model inference and execution costs
Data Governance and Privacy
- Assessing data exposure when external models are invoked
- Demonstration of self-hosting and local model options by the instructor
- Reviewing a compliance checklist with IT or legal teams
Leveraging AI in Workflow Construction
- Using AI assistants to draft initial workflow structures
- Reviewing AI-generated logic: validating node functions, data sources, and empty-state handling
Final Project and Future Steps
- Participants finalize and present a workflow featuring an AI step and an agent tailored to their own process
- Developing a maintenance plan and identifying subsequent automation opportunities
Outcome: A functional workflow incorporating an AI step, a data-connected agent, error handling, and a comprehensive review checklist.
Requirements
- No programming background is necessary
- Familiarity with using web applications and spreadsheets
- Identification of one repetitive process within their current workflow
Target Audience
- Professionals in business operations, management, and client-facing roles
- Team leaders and process owners
- Non-technical staff aiming to implement automations and AI agents using corporate 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
