Introduction to Agentic AI

Prerequisites

• Basic understanding of AI and machine learning concepts
• Familiarity with Python programming
• Interest in building autonomous AI systems

Course Outline

1. 1What is Agentic AI? - Definitions, history, and current landscape
2. 2Agent architectures: ReAct, Plan-and-Execute, and Tool-Use patterns
3. 3Understanding agent reasoning and decision-making loops
4. 4Introduction to LLM-powered agents and their capabilities
5. 5Agent memory systems: short-term, long-term, and episodic memory
6. 6Tool integration basics: APIs, functions, and external services
7. 7Building your first simple AI agent with Python
8. 8Agent evaluation metrics and testing strategies

Learning Outcomes

• Understand the core principles of Agentic AI systems
• Identify use cases suitable for AI agent implementations
• Build simple AI agents using popular frameworks
• Evaluate agent performance and reliability
• Design basic agent architectures for specific tasks
• Integrate external tools and APIs with AI agents