Foundations of Agentic Tool Use in Python

Übersicht

Master the practical building blocks of agentic systems in Python—prompt for structured outputs, define tool schemas, manage context windows, and build reliable self-correcting agent loops.

Lehrplan

• Introduction to Agentic Systems
Overview of agentic systems and their applications
Key concepts in agentic system design
• Prompt Engineering for Structured Outputs
Basics of prompt design in natural language processing
Techniques for creating effective and structured prompts
Case studies and practical examples
• Defining Tool Schemas
Introduction to tool schemas and their importance in agentic systems
Methods for designing robust tool schemas
Implementing and testing tool schemas in Python
• Context Window Management
Understanding context windows and their role in decision-making
Strategies for effective context window management
Implementing context management in agent systems
• Building Reliable Self-Correcting Agent Loops
Core principles of feedback loops in agent systems
Techniques for developing self-correcting mechanisms
Building and testing agent loops in Python
• Practical Workshop: Developing a Mini Agentic System in Python
Step-by-step guide to building a simple agent
Collaborative exercises focusing on integration of learned concepts
• Advanced Topics and Future Directions
Exploration of advanced agentic patterns and techniques
Discussion of emerging trends and technologies in AI agent systems

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Fachgebiete

Artificial Intelligence