This document introduces the concept of AI agents, which enhance language models by enabling them to interact with the external world. It details the key components of an agent's cognitive architecture: the language model itself, tools (Extensions, Functions, and Data Stores), and the orchestration layer that manages reasoning and action. The paper contrasts agents with standard models and explores various prompting frameworks like ReAct, Chain-of-Thought, and Tree-of-Thoughts that guide agent behavior. Furthermore, it discusses methods for improving model performance through targeted learning approaches. Finally, the whitepaper presents practical examples using LangChain and highlights Google's Vertex AI platform for building production-ready agents.