AI Agent Architecture
AI agent architecture refers to the structural design of systems that perceive environments, process data, and make autonomous decisions. AI agent architecture is foundational in building intelligent agents that operate in fields such as robotics, gaming, virtual assistants, and autonomous vehicles. Core components include perception modules (to interpret input from sensors or user interactions), decision-making units (such as rule-based engines or machine learning models), and actuators that perform actions based on chosen responses. The architecture may follow reactive, deliberative, or hybrid models, depending on the complexity and objectives of the system. Advanced AI agent frameworks use reinforcement learning, enabling agents to improve behavior over time through feedback. These architectures are essential in creating responsive, adaptive, and context-aware AI systems capable of functioning with minimal human intervention.
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