In the AI Agents for Architects training, you will gain practical architectural knowledge to understand agentic AI systems in a structured way and accurately position them within real projects. You will learn how agents are conceptualized as an interplay of core building blocks and what role context, memory, and state play in ensuring reliable behavior. The focus lies on interoperability—for example through standards such as MCP—as well as on quality and observability, and the requirements for security and scalability in productive IT landscapes.

• You understand the agentic paradigm and can clearly distinguish agentic AI systems from classical GenAI scenarios.
• You learn how agents are architecturally structured and how the core building blocks can be integrated into a reliable overall system.
• You acquire practical decision-making logic for context engineering as well as memory and state management.
• You learn how tool integration and interoperability are conceptualized in agent architectures, including in the context of MCP and multi-agent systems.
• You receive criteria and approaches for evaluating the quality, robustness, and security of agentic systems.
• You learn how agentic systems are prepared for productive environments, with a focus on observability, scalability, and secure operation.

• Tool Integration and Collaboration in Agent Landscapes
  ➤ How agents use tools and why interoperability should be considered early.
  ➤ MCP and multi‑agent systems as guidance for standardized collaboration and extensibility.
• Making Quality Visible
  ➤ How quality can be meaningfully conceptualized and assessed in non‑deterministic systems.
  ➤ Observability as the foundation for making system behavior traceable in operation.
• Safely into Production
  ➤ What defines production readiness — from scalability to security — without losing sight of the big picture.
  ➤ Guardrails for operations that can be integrated into existing development and release processes.