AI - Workflow & Automation.

Understanding Automation: Concepts, Tools & Market You’ll learn to distinguish between rule-based and AI-powered workflows, compare the leading automation platforms (n8n, Power Automate, Make, Zapier) based on specific criteria, and make an informed platform decision for a given use case.

n8n Quick Start: Integration Platform as a Service You’ll set up your personal n8n environment, navigate confidently through nodes, triggers, connections, data tables, and executions, and master the basics of debugging.

Your learning objective: Classify current automation platforms, understand the difference between rule-based and AI-powered workflows, and be able to confidently operate your own n8n environment.

Triggers & Integrations You select the appropriate trigger type (manual, Cron, webhook, app trigger) for a use case, set it up in n8n, and forward trigger data to specific Nodes.

Understanding & Transforming Data You understand the n8n data model (items, fields, JSON paths), read and write nested JSON structures, and connect apps end-to-end, from the input to the database entry.

Workflow Logic & Conditional Paths You implement conditional logic using IF and Switch nodes, build escalation and routing paths, and cleanly merge parallel paths using Merge nodes.

Robust Workflows & Error Handling You safeguard workflows against common sources of error (error workflows, retries, rate-limit strategies), extract specific information from execution logs, and ensure workflows remain maintainable through naming conventions and documentation.

Your learning goal: to master n8n as an automation platform—from triggers to the data model and branching logic, all the way to robust, maintainable workflows. Only then will AI be introduced in the next module.

APIs & Authentication You integrate REST APIs with various authentication methods (API key, Basic Auth, Bearer token, OAuth) into n8n and troubleshoot common authentication and rate-limit errors.

Tables as Data Sources & Destinations You use Excel and Google Sheets as practical databases—as sources, destinations, and lookup tables—and recognize when a full-fledged database makes more sense.

Preparing & Merging Data You’ll build simple ETL pipelines in n8n: extracting, standardizing, deduplicating, enriching, and loading data into the target system—mostly without writing your own code.

Your learning goal: Securely connect external data sources and APIs and prepare data—without programming—so that it forms a solid foundation for AI-powered workflows.

LLM Nodes in n8n You’ll configure LLM nodes and credentials in n8n, implement your first AI workflow, and reliably structure LLM outputs as JSON for further processing.

Advanced Prompt Engineering You will apply advanced prompt techniques (role-based instructions, separation of system and user prompts, parameter control) in a targeted manner and systematically iterate on prompts to ensure quality and reproducibility.

Models, Tokens & Costs You estimate the token consumption of prompts, select models based on cost, latency, and quality criteria, and compare proprietary AI models using specific prompts.

Productive AI Workflows You’ll build reusable prompt libraries, chain multiple LLM nodes into robust multi-stage workflows, and resolve common errors such as context overflow or JSON parsing errors.

Your Learning Goal: Combine prompt engineering, token economics, and LLM nodes so that generative AI runs reliably and cost-effectively in production n8n workflows.

First Agent in n8n You'll build your first AI agent in n8n from scratch and gradually expand it from a "Hello World" agent through tools and memory to a multi-tool agent.

Integrating Memory & Knowledge You’ll equip agents with memory and knowledge: You’ll configure short- and long-term memory and set up RAG pipelines for large data sets.

Incorporating Tools and Integrations (Tool Use) You’ll utilize the function-calling interface, formulate system prompts for connected tools, and encapsulate business logic deterministically in sub-workflows as tools.

Understanding & Controlling Agent Behavior You’ll read your agent’s execution log, perform forensic error diagnostics, secure the agent with fail-safes, and integrate cost monitoring via the OpenRouter Pricing API.

Your learning objective: Independently design AI agents in n8n, equip them with tools, memory, and RAG knowledge, and make them usable for business processes in a production-ready environment.

Analysis of Real-World AI Projects You will examine examples of success and failure from various industries in detail and derive key insights.

Keys to Success You will identify success factors such as clear goals, data quality and management support, and document them.

Interactive Group Assignment You analyze short case studies, identify success factors and mistakes, and compare them in a plenary session.

Cross-Industry Knowledge Transfer You apply best practices to your organization and identify gaps in existing structures.

Resources and Community You’ll discover relevant sources, conferences, and communities to continuously learn from real-world projects.

Your learning objective: Identify the success factors and stumbling blocks of AI projects and leverage them strategically for your own initiatives.

Quality & Validation of AI Outputs You systematically evaluate AI outputs for factual accuracy, consistency, and format stability; you validate workflows using JSON schema validation and plausibility checks; and you detect bias and hallucinations.

Security & Compliance You identify personal data in AI workflows, select GDPR-compliant LLM solutions (personal reference, data processing agreement, EU hosting), and apply anonymization and pseudonymization in a pragmatic manner.

Improving Workflows & Making Them Maintainable You systematically identify workflow vulnerabilities, apply refactoring patterns (sub-workflow extraction, config externalization), and establish standards for naming, documentation, and testing.

AI-Assisted Refactoring & Workflow Generation You configure n8n as an MCP server, have workflows generated and refactored by an LLM client, and critically review the results against quality and security criteria.

Modeling Processes with BPM Tools You will master the basic BPMN 2.0 notation and document your own n8n workflows as clear process diagrams using the free tool bpmn.io.

Your learning objective: To check your workflows for output quality, GDPR compliance, and maintainability; to refactor them in a targeted manner; and to document them using BPMN so that they can be communicated independently of n8n.

Theoretical Exam You assess your own level of proficiency based on the module catalog and complete the theoretical portion of the final exam through four thematic multiple-choice quizzes.

Project Briefing & Design You will draft a realistically scoped project brief (use case, stakeholders, success criteria, sprint plan) and verify feasibility and data availability before starting.

Implementation You implement your automation solution in four sprints: from the trigger and data flow through AI integration and output validation to edge case handling, monitoring, and GDPR compliance. Along the way, you iteratively improve it through peer reviews.

Presentation & Reflection You’ll present your project live (use case, architecture, demo, Q&A) and reflect in a structured manner on what went well, what was challenging, and what you would do differently on your next project.