Perception, planning, and action cycles
An agent loop is the core cycle that powers autonomous systems. The agent observes the current state (perception), reasons about what action to take given its goals (planning), executes that action, then observes the new state. This cycle repeats until the goal is achieved or the agent concludes the task is impossible. The loop is fundamental to robotics, game AI, and language model agents with tool use. In language model agents, perception is reading the current state and task; planning is reasoning about which tool to call; action is invoking the tool; and the new observation is the tool's output. The loop continues until the final answer is reached.
Scaling agents through tool use
Early agents were limited by what they could reason about directly. Modern agents access external tools: search engines, calculators, code interpreters, databases. This vastly expands capability. An agent reasoning about a math problem calls a calculator. One asked a question about current events searches the web. One asked to write code calls a code execution environment. Each tool expands the agent's abilities beyond its training knowledge. Tool use introduces a planning problem: which tool to call, in what order, with what arguments. Language models excel at this reasoning because they learned to produce text in specific formats. The loop become a scaffold on top of language model reasoning, extending pure prediction into goal-directed behavior.