Understanding AI Agent Cycles as State Machines for Better Design

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A technical article on DEV Community explains how LLM-based AI agents can be modeled explicitly as Finite State Machines (FSMs), with defined states such as thinking, acting, observing, done, and error. The piece argues that many developers build agents through vibe coding — copying AI-generated code without grasping the underlying concepts — which can lead to unnecessarily complex implementations. By treating the agent loop as a state machine, developers gain clearer control over transitions, tool execution, and error handling. The article further introduces Hierarchical State Machines (HSMs) as a structured solution for when agents grow complex enough that a flat FSM becomes difficult to manage. The core goal is to help developers understand what they are actually building, enabling more deliberate and scalable agent design decisions.

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