Learn how to build an AI agent using models that sense, reason, learn, and act within dynamic environments. Building an AI agent involves creating a system that perceives its environment, processes information, and takes actions to achieve specific goals. The process begins with defining the agent’s objectives, collecting data, and selecting appropriate machine learning or rule-based models. Developers then implement sensing modules, decision logic, and learning mechanisms. AI agents can be reactive or proactive, depending on their architecture, and may include components like reinforcement learning for dynamic environments. This blog walks through each step in building an AI agent, from training datasets and algorithms to deployment strategies and performance evaluation. Applications include robotics, gaming, autonomous vehicles, and virtual assistants. Understanding how to build AI agents helps developers create intelligent systems capable of real-time interaction and continuous learning.