This article clarifies that AI agents are not just smarter chatbots but systems that can autonomously pursue goals by decomposing tasks, calling tools, observing results, and adjusting plans. It provides a high-level architecture overview that is valuable for engineers designing agent-based systems. Key components include task decomposition, tool integration, observation loops, plan correction, and state persistence. The content is evergreen and commercially relevant as agent-based AI applications grow. It serves as a solid foundation for understanding how to move from conversational AI to autonomous agents.
A high-level overview of AI agent architecture, covering task decomposition, tool use, and state management for autonomous goal pursuit.