This module serves as a joint introduction and establishes a common level of knowledge. We create an initial understanding and show where the journey can take us. 

• The WOW demo: We start right away with practical application. A live demonstration of an autonomously operating agent shows the target vision of the course ("That's possible" moment). 
• Agent vs. workflow vs. LLM: Initial fundamental distinction. When are we talking about a chatbot, when about automation, and when about a real agent? 
• Market overview & classification: An initial guide to the jungle of tools (Agent.ai, Microsoft Agents, Lovable, Claude Code, Antigravity). We sort through the hype. 
• Outlook: Presentation of the learning journey and objectives for the coming weeks. 

This module serves as a joint introduction and establishes a common level of knowledge. We create an initial understanding and show where the journey can take us. 

• The WOW demo: We start right away with practical application. A live demonstration of an autonomously operating agent shows the target vision of the course ("That's possible" moment). 
• Agent vs. workflow vs. LLM: Initial fundamental distinction. When are we talking about a chatbot, when about automation, and when about a real agent? 
• Market overview & classification: An initial guide to the jungle of tools (Agent.ai, Microsoft Agents, Lovable, Claude Code, Antigravity). We sort through the hype. 
• Outlook: Presentation of the learning journey and objectives for the coming weeks. 

Here you will lay the theoretical foundation and set up your technical environment so that you are ready to work in the subsequent webinars. 

• Deep dive understanding: Why agents need external tools (tool use) and long-term memory (memory, e.g., Pinecone) to solve complex tasks

• Glossary & Tech Stack: Detailed explanation of key terms and tools (LangChain, Make, Context, vector databases). 

• Low-code vs. code: A decision-making aid for getting started. Analysis of when low-code (Make) is sufficient and when engineering (LangChain> Python/TS) is necessary. 

• Setup: Instructions for setting up the necessary accounts so that everything is ready to go for the next webinar. 

• Knowledge check & homework for webinar 2:Think of an agent use case for the second webinar. 

Here you will lay the theoretical foundation and set up your technical environment so that you are ready to work in the subsequent webinars. 

• Deep dive understanding: Why agents need external tools (tool use) and long-term memory (memory, e.g., Pinecone) to solve complex tasks

• Glossary & Tech Stack: Detailed explanation of key terms and tools (LangChain, Make, Context, vector databases). 

• Low-code vs. code: A decision-making aid for getting started. Analysis of when low-code (Make) is sufficient and when engineering (LangChain> Python/TS) is necessary. 

• Setup: Instructions for setting up the necessary accounts so that everything is ready to go for the next webinar. 

• Knowledge check & homework for webinar 2:Think of an agent use case for the second webinar. 

The focus is on the concept ("the agent's brain") and the economic perspective.

Strategy & Use Cases: 

• Decision matrix: Differentiation in practice—is my case an assistant, a workflow, or an agent? 
• Business Model Canvas for agents: Development of goals, required tools, contextual data, and ROI calculation. Validation of your own business case. 

Context Engineering (Hands-on): 

• Context > Prompt: Why engineering the context is more important than "prompt magic." 
• Live building: Together with the trainer, you as a learning group will build the "brain" of an agent (system prompts + context data) live. 
• Structuring: Write system prompts and define what knowledge the agent needs. 

The focus is on the concept ("the agent's brain") and the economic perspective.

Strategy & Use Cases: 

• Decision matrix: Differentiation in practice—is my case an assistant, a workflow, or an agent? 
• Business Model Canvas for agents: Development of goals, required tools, contextual data, and ROI calculation. Validation of your own business case. 

Context Engineering (Hands-on): 

• Context > Prompt: Why engineering the context is more important than "prompt magic." 
• Live building: Together with the trainer, you as a learning group will build the "brain" of an agent (system prompts + context data) live. 
• Structuring: Write system prompts and define what knowledge the agent needs. 

Deepening of concepts and preparation for the big agent construction day in Webinar 3. 

• The "Good Context Checklist": Criteria catalog for robust context. You check your designed use case for data completeness. 
• Enterprise readiness: A critical examination of the stability of solutions. Distinguishing between hype demos and stable enterprise solutions. 
• Fine-tuning the process: You prepare your company's internal process so that it can be technically implemented in the next webinar (create a flow chart, review API documentation). 

Deepening of concepts and preparation for the big agent construction day in Webinar 3. 

• The "Good Context Checklist": Criteria catalog for robust context. You check your designed use case for data completeness. 
• Enterprise readiness: A critical examination of the stability of solutions. Distinguishing between hype demos and stable enterprise solutions. 
• Fine-tuning the process: You prepare your company's internal process so that it can be technically implemented in the next webinar (create a flow chart, review API documentation). 

The big implementation day. We build real agents and make them ready for operation. 

Deep Dive Building: 

• Hands-on group work: Building an agent depending on skill level (e.g., research agent, RAG document agent, or Workflow Plus agent). 
• Integration: Connecting LLM (brain) and tools (hands). The trainer provides direct support in the breakout rooms if problems arise. 

Production Readiness & Governance: 

• Reality check: Dealing with loops, hallucinations, and unclear tools. 
• Safety first: Implementation of kill switches, human-in-the-loop governance, and logging. 
• Responsibility: Data protection, employee vs. agent responsibility, and ethical issues. 
• Roadmap: Outlook on upcoming technologies (e.g., Manus, Antigravity) and strategies for integration into the team ("Build with Agents vs. Act with Agents"). 

The big implementation day. We build real agents and make them ready for operation. 

Deep Dive Building: 

• Hands-on group work: Building an agent depending on skill level (e.g., research agent, RAG document agent, or Workflow Plus agent). 
• Integration: Connecting LLM (brain) and tools (hands). The trainer provides direct support in the breakout rooms if problems arise. 

Production Readiness & Governance: 

• Reality check: Dealing with loops, hallucinations, and unclear tools. 
• Safety first: Implementation of kill switches, human-in-the-loop governance, and logging. 
• Responsibility: Data protection, employee vs. agent responsibility, and ethical issues. 
• Roadmap: Outlook on upcoming technologies (e.g., Manus, Antigravity) and strategies for integration into the team ("Build with Agents vs. Act with Agents").