An agentic loop is an iterative cycle of execution in which an AI agent receives tasks or objective, prepares or determines the next course of action, carries it out, evaluates the results, determines if the task is finished, and either terminate.According to experts, in the agentic loop, the agent makes decisions about its next course of action based on observations rather than pre-written rules.Every agentic loop follows the same four-stage cycle: in perceive, an agentic loop reads input, such as a user message, an API response, or a file state; in reason, an LLM processes the goal and current context to determine the next action; in act, a tool is called, a system is written to, or a sub-scenario is triggered; in observe, the outcome is evaluated and fed into the subsequent iteration.
