Artificial Intelligence (AI) Webinars

AI in Plain Terms – From Language Models to Production-Ready Agent Systems

3-part webinar series on Large Language Models (LLMs), AI Agents, and the Model Context Protocol (MCP) – clearly explained, practically oriented, and delivered in an interactive format.

Artificial Intelligence (AI) Webinars

Artificial intelligence is transforming the way companies operate – but there is often a significant gap between hype and real value. This webinar series closes that gap: compact, well-founded, and with a clear objective – not to present products, but to provide orientation in a field that is evolving at a rapid pace and where it is becoming increasingly difficult to distinguish what truly matters from what does not.

The three webinars build on each other conceptually: those who understand what Large Language Models (LLMs) can really do – and where their limits lie – are better equipped to assess when the use of AI agents makes sense. And those who want to implement agent systems need solid architectural principles such as the Model Context Protocol (MCP) and skills to operate them productively, maintainably, and securely.

Structure of each webinar

  • 30 minutes of compact knowledge
  • Product presentation
  • Live demo by our experts
  • Live Q&A session for your questions

Who is this webinar series for?

The series is intentionally aimed at a broad audience: IT decision-makers, executives, data scientists, BI professionals – as well as anyone who wants to understand where things are heading without diving deep into code. AI is not just a technical topic. It is a strategic and organizational challenge. And addressing it requires well-founded knowledge at all levels.

Your Benefits

  • Understanding how Large Language Models (LLMs) work, including their strengths and limitations

  • Clarity on when and how AI agents can be effectively used in a business context

  • Clear insight into the potential, limitations, and realistic use cases of AI

  • A solid foundation for decision-making when planning and evaluating your own AI initiatives

  • Understanding key architectural principles (MCP, Skills) for the productive use of AI systems

Webinar #1: How intelligent are LLMs really?

Webinar #1: How intelligent are LLMs really?

The term Large Language Model (LLM) has become indispensable in any AI discussion – but what actually lies behind it? And what does this mean in concrete terms for a company planning an AI initiative?

This first webinar lays the foundation. It provides an honest, technically sound understanding of how modern language models work – and helps answer the questions that arise in every boardroom and strategy meeting:

  • Should we invest in an AI initiative at all?
  • When does an LLM project make sense?
  • One key takeaway: LLMs are only one component of an AI initiative – not the solution in themselves. The quality of the underlying data is critical. A language model that is not supplied with the right, structured, and up-to-date data will fail – regardless of how powerful the model itself is. Garbage in, garbage out still applies in the age of generative AI and LLMs.


 

  • Focus

    Realistic assessment of language models in a business context

  • Live-Demo

    How LLMs are integrated into real business processes

  • Key Takeaways

    A solid basis for decision-making to realistically assess the potential and limitations of LLMs – and to launch your first AI initiative on a sound foundation

Webinar #2: What can AI agents do?

Webinar #2: What can AI agents do?

Agentic AI is the next major chapter after traditional LLMs. Agent-based systems extend language models with planning capabilities and the ability to use tools independently – from databases and APIs to entire software applications. The result: systems that can handle multi-step tasks autonomously, without requiring human input at every step.

  • Focus

    From reactive language models to autonomous process execution – and what this means for everyday work

  • Live-Demo 

    When AI becomes autonomous – agents in action

  • Key Takeaways

    A clear overview of which processes can be meaningfully and safely delegated to AI systems – and how to distinguish between real business value and inflated expectations

Webinar #3: MCP and Skills - Building Blocks of Productive Agent Systems

Webinar #3: MCP and Skills - Building Blocks of Productive Agent Systems

Anyone looking to deploy AI agents productively needs more than just a powerful LLM. They need a solid architecture – one that scales, remains maintainable, and integrates into existing system landscapes. This is exactly where this third webinar comes in.

At the center is the Model Context Protocol (MCP) – an open standard that enables AI agents to communicate in a standardized way with external data sources, tools, and systems. MCP is what truly connects agents: to enterprise data, software environments, and the real world.

MCP is complemented by the concept of skills: structured knowledge and instruction modules that teach an agent domain-specific behavior. Skills define how an agent should act in a given context. MCP defines what it can interact with. Together, they form the foundation for robust, adaptable agent systems.

Learn more

  • Focus

    Standardized integration and domain-specific control as the foundation for scalable AI architecture

  • Live-Demo

    Separation of infrastructure (MCP) and behavior (Skills) as an architectural principle

  • Key Takeaways

    An architectural understanding of robust, maintainable, and integrable agent systems – and the conceptual foundation for building sustainable AI initiatives

Key Topics in Our Webinar Series

  • NLP

    Large Language Models

    • Principle: Next-token prediction, training vs. prompt engineering
    • Strengths: Processing unstructured data, text and code generation
    • Limitations: Lack of logical consistency and causes of hallucinations
    • Success factors: Model selection, data quality, context engineering, evaluation, and safeguarding

  • Generative AI

    AI Agents & Agentic AI

    • Agent architecture: Model, memory, tool use, and planning
    • Multi-agent systems: How specialized agents collaborate (orchestration)
    • Process integration: Integration into existing business processes and systems
    • Digitalization: Vibe & agentic coding and AI prototyping
    • Governance: Human-in-the-loop, control mechanisms, and security architecture

  • Automation

    AI Architecture & Integration

    • MCP (Model Context Protocol): What it is, why it matters as a standard, and how it standardizes the connection between agents and data sources and tools
    • Skills: What skills are in the AI context, how they are defined, structured, and assigned to an agent
    • Interaction: Separation of infrastructure (MCP) and behavior (skills) as an architectural principle
    • Plug-and-play agents: How modular architectures improve the maintainability and scalability of AI systems
    • Practical implications: What this means for IT teams and AI leaders when building their own solutions

Our Expertise in the AI Field

Referent

Prof. Dr. Andreas Martin, FHNW – Professor of Applied AI, Visiting Applied AI Scientist, PhD in Information Systems – provides a practical understanding of the most important AI developments of our time: from Large Language Models (LLMs) and Agentic AI to modern integration standards such as the Model Context Protocol (MCP).

Pro. Dr. Andreas Martin

Moderator

Oliver Ruf, Head of Consulting & Projects at Informatec and External Lecturer at FHNW, guides you through the webinars and moderates the Q&A sessions.

Oliver Ruf

 

Register for Free Now

  • 3 webinars – practical & informative
  • Compact: only 30 minutes per session

  • Can be booked individually, but especially impactful as a series

    Gain a clear overview of modern AI – from LLMs and AI agents to scalable AI architectures

 

Updated: 02.06.2026