AI agent architecture is the structural framework that defines how an AI system gathers information, processes data, makes decisions, and executes tasks. A well-structured architecture ensures that systems are functional, reliable, scalable, and secure. The perception layer handles data ingestion from various sources, while the reasoning engine evaluates the data, applies logic, and determines the next best action based on its goals. Once decisions are made, the AI agent moves into the action execution phase, where it takes its internal reasoning and translates it into real-world actions. AI agents must adapt and evolve through real-world interactions, with feedback and learning components helping them improve by integrating insights from user behavior, system performance, and environmental changes. The choice of architecture affects everything from an agent's efficiency to its adaptability in complex environments, with common architectures including the Layered Architecture, Blackboard Architecture, Subsumption Architecture, Hybrid Architectures, Single-agent systems, and Multi-agent systems. However, designing and maintaining efficient AI agent architectures presents various challenges, including scalability issues, managing uncertainty in dynamic environments, and ensuring compliance with regulatory frameworks.