Building Agentic AI Workloads - Crash Course

Übersicht

Master agentic AI systems through hands-on Python development, exploring LLM-powered agents, architectural patterns, memory systems, and evaluation methods for dynamic task execution.

Lehrplan

• Introduction to Agentic AI Systems
Definition and overview of agentic AI
Key use cases and applications
• Setting Up Your Python Development Environment
Installing necessary libraries and tools
Overview of Python packages for AI development
• Understanding Large Language Models (LLMs)
Overview of LLMs and their role in agentic AI
Implementing LLMs using popular libraries (e.g., Hugging Face, OpenAI)
• Designing Agentic AI Architectures
Introduction to architectural patterns for AI agents
Modular design and component interaction
• Developing LLM-Powered Agents
Building and integrating LLM-based agents
Contextual understanding and natural language processing
• Memory Systems in AI Agents
Different types of memory (short-term, long-term)
Implementing memory architectures
• Dynamic Task Execution and Management
Techniques for dynamic decision-making
Handling multiple concurrent tasks
• Evaluation Methods for AI Agents
Designing metrics for agent performance
Testing and iterating agentic systems
• Hands-on Development Sessions
Guided examples and coding exercises
Building and deploying a complete agentic AI system
• Future Trends in Agentic AI
Predictions and research directions
Ethics and security considerations in agentic AI systems

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Fachgebiete

Artificial Intelligence