What is an LLM agent, and how does it differ from a plain LLM call?

A plain LLM call maps one prompt to one text output. An LLM agent wraps the model in a loop where it can decide to take actions, in the environment, observe the results, and iterate toward a goal. The model is the reasoning engine; around it sit tools (functions, APIs, code execution), memory, and a control loop that feeds observations back in. The key shift is autonomy over multiple steps: instead of you specifying each API call, the agent chooses which tool to use, with what arguments, and when the task is done. This makes agents suited to open-ended tasks (research, debugging, booking) but also introduces failure modes a single call never has: looping, drifting off-goal, and compounding errors across steps.