The iterative cycle in which an AI agent perceives its environment, selects an action, executes it, and observes the result before deciding on the next step toward a goal.
The agentic loop is the core operating pattern of autonomous AI agents. Each iteration follows a perceive-plan-act-observe cycle: the agent receives context (current state, goal, prior results), reasons about what to do next, executes an action (calling a tool, writing code, sending a message), and updates its context with the result before the next iteration.
The loop continues until the agent determines the goal is complete, encounters an unresolvable error, or hits a configured limit (max steps, max tokens, time budget). Well-designed agents include termination conditions, error recovery logic, and human-in-the-loop checkpoints to prevent runaway loops or compounding errors.
The ReAct (Reason + Act) pattern is the most commonly used agentic loop design, popularized by LangChain and adopted across most agent frameworks. Each loop iteration includes an explicit reasoning step ("I need to search for X to answer Y") before the action step, which dramatically improves reliability compared to action-only loops.
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