Agentic AI systems are enabling autonomous decision-making, complex task execution, and multi-step reasoning with greater efficiency and adaptability. This presentation explores the theoretical foundations and practical methodologies for developing these systems, leveraging state-of-the-art open-source frameworks to achieve robust and scalable implementations.

We will discuss the architectural design principles underlying Agentic AI, including the use of frameworks such as LangGraph, LlamaIndex, and CrewAI. We will focus on task decomposition, memory management, and the application of chain-of-thought reasoning for enhanced problem-solving capabilities.

Attendees will acquire actionable insights into bridging theoretical concepts with practical deployment, fostering the development of autonomous systems that meet the demands of complex, real-world applications.