Schulungsübersicht
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.
Voraussetzungen
- 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
Erfahrungsberichte (3)
Starke Bestätigung dafür, dass aktuelle LLMs im richtigen Setup für Kundenprojekte einsetzbar sind.
Oliver Fromm
Kurs - Agentic AI for Enterprise Applications
Der Trainer ist geduldig und sehr hilfsbereit. Er kennt sich gut mit dem Thema aus.
CLIFFORD TABARES - Universal Leaf Philippines, Inc.
Kurs - Agentic AI for Business Automation: Use Cases & Integration
Maschinelle Übersetzung
Die Mischung aus Theorie und Praxis sowie den Perspektiven auf hohem und niedrigem Niveau
Ion Mironescu - Facultatea S.A.I.A.P.M.
Kurs - Autonomous Decision-Making with Agentic AI
Maschinelle Übersetzung
