AI Agent architecture

AI Agent architecture is the structural framework that defines how autonomous AI systems organize their core components to perceive environments, make decisions, and execute actions. This architecture typically encompasses perception modules for data intake, reasoning engines powered by large language models, memory systems for context retention, planning components for multi-step task execution, and action interfaces for tool integration and environment interaction.

Modern AI Agent architectures implement modular designs separating concerns between cognitive functions, enabling maintainable systems. Key architectural patterns include reactive agents that respond to stimuli, deliberative agents that plan before acting, and hybrid architectures combining both approaches. Advanced implementations feature hierarchical structures with specialized sub-agents, distributed coordination mechanisms, and feedback loops for continuous learning. The architecture determines an agent’s capabilities, performance characteristics, and integration potential, making it fundamental to building robust autonomous systems for enterprise applications.