What is Agent Loop?
The agent loop is the fundamental execution cycle that defines how an AI agent operates continuously to accomplish its goals. It typically follows a pattern of perceive-reason-decide-execute-observe, where the agent continuously processes new information, evaluates its current state, makes decisions about what actions to take, executes those actions, and observes the results to inform the next iteration.
This cyclical process enables agents to operate autonomously over extended periods, adapting their behavior based on feedback from their environment and the outcomes of their actions. Each iteration of the loop allows the agent to refine its understanding, adjust its strategy, and move closer to its objectives. The loop continues until the agent achieves its goal or determines that it cannot make further progress.
The agent loop is central to implementing autonomous behavior in AI systems. It provides the structure for agents to maintain state across multiple interactions, learn from experience, and handle complex tasks that require multiple steps. Different agent frameworks may implement variations of this loop, but the core concept of continuous perception, reasoning, and action remains consistent across most agentic systems.