Course Outline
Day 1: Workflow Architecture and AI Integration
Introduction
- Distinguishing when to use standard automation, AI steps, or full agents.
- Understanding n8n deployment models: cloud versus self-hosted, and the implications for data security.
n8n Fundamentals
- Core concepts: workflows, nodes, triggers, and execution cycles.
- Data mapping between nodes using a drag-and-drop interface, eliminating the need for syntax knowledge.
- Analyzing execution logs to identify and resolve issues.
Application and Data Connectivity
- Setting up webhooks and form-based triggers.
- Integrating spreadsheets, email, and calendar services.
- Managing credentials and authentication for third-party services.
- A step-by-step guide to using the HTTP Request node.
Incorporating AI Steps
- Adding AI models to existing workflows.
- Crafting effective prompts to ensure reliable model outputs.
- Implementing structured output parsing and automatic correction for invalid responses.
- Extracting and classifying information from emails, forms, and documents.
Logic and Action Management
- Implementing decision logic using If and Switch nodes.
- Designing approval steps that require human intervention before critical actions.
- Logging results to spreadsheets and triggering notifications.
Practical Lab: First End-to-End Workflow
- Building a complete process from receiving a request to classifying, recording, and routing the result.
Day 2: Advanced Agents and Operational Reliability
Agent Architecture
- Deconstructing agent components: models, memory, and toolsets.
- Decision-making: When to choose a fixed workflow versus an autonomous agent.
- Granting agents secure access to company documents and data sources.
Action-Oriented Agents
- Differentiating between read-only and write-enabled tools.
- Defining system prompt rules to enforce data integrity, prevent hallucinations, and avoid duplicate records.
- Stress-testing agents to identify and mitigate potential failure points.
Communication Channels
- Setting up internal chat interfaces.
- Connecting agents to external messaging platforms.
Operational Reliability
- Configuring error-handling workflows, automatic retries, and alert notifications.
- Utilizing logs and execution history for auditing.
- Strategies for managing and controlling model usage costs.
Data Governance and Privacy
- Assessing data exposure risks when invoking external models.
- Demonstration of self-hosted and local model deployment.
- Creating a compliance checklist for review with IT and legal teams.
AI-Assisted Development
- Using AI assistants to draft initial workflow structures.
- Validating AI-generated workflows: verifying node functions, data sources, and null-state handling.
Capstone Project and Future Roadmap
- Participants will finalize and present a customized workflow featuring an AI step and an agent, applied to a real-world process.
- Developing a maintenance plan and identifying further opportunities for automation.
Outcome: A fully functional workflow integrating an AI step, a data-connected agent, robust error handling, and a compliance review checklist.
Requirements
- No prior programming knowledge is necessary.
- Familiarity with navigating web applications and using spreadsheets.
- Identification of at least one repetitive process in your current role that could benefit from automation.
Target Audience
- Professionals in business, operations, and customer-facing roles.
- Team leaders and individuals responsible for process management.
- Non-technical staff seeking 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
