Autonomous AI agents are reshaping the AI landscape by revolutionizing how we interact with technology and the capabilities of LLM-powered AI systems. These agents, which can independently perceive their environment, make decisions, and take actions without human intervention, are becoming increasingly prevalent across various industries and applications.

In this talk, "Agentic AI: The Rise of Autonomous AI Agents and LangGraph," we will delve into the emerging theme of agentic AI and explore the progressive levels of maturity in constructing generative AI applications using large language models (LLMs).

Attendees will be introduced to the world of autonomous AI agents, understanding what an agent is and how it differs from other maturity levels of building a Gen AI application using LLMs like Retrieval-Augmented Generation (RAG) systems. The session will cover the key components and operational principles of agentic AI, highlighting popular frameworks like Chain of Thought (CoT) and ReACT that guide the cognitive processes of autonomous agents. Additionally, we will examine how to enhance RAG applications with agentic RAG, pushing the boundaries of what these systems can achieve.

LangGraph is closely related to agentic AI, providing a framework for defining agentic AI workflows as graphs. Through demonstrations of LangGraph, we will witness the powerful capabilities of these autonomous agents. We will explore LangGraph's functionalities and various architectures that exemplify agentic AI systems, such as Supervisor, Self Reflection, and Human Reflection.