What is an AI agent, and how does it differ from a simple LLM call?
The trap here is agent hype. A weak answer treats an agent like an LLM plus tools. The interviewer is usually probing whether you understand the operational difference between one-shot generation and a loop that can choose actions, mutate state, spend budget, and fail repeatedly before it finishes.
01Interview Context
The trap here is agent hype. A weak answer treats an agent like an LLM plus tools. The interviewer is usually probing whether you understand the operational difference between one-shot generation and a loop that can choose actions, mutate state, spend budget, and fail repeatedly before it finishes.
Senior and staff interviewers also probe restraint. The best candidates know when not to use an agent.
02The 90-second answer
A simple LLM call takes input and returns output once. An agent wraps the model inside a control loop. That loop decides whether to call tools, updates its state from the results, and stops only when it reaches a completion condition or hits a guardrail. The real difference is not the model. It is the orchestration logic around the model.
My short production stance: agents are useful when the task shape is uncertain and tool choice has to happen at runtime. If the workflow is already known, I would rather build a deterministic pipeline — it is cheaper, faster, and easier to test.
03Weak vs Strong Answer
Weak answer
"An AI agent is an LLM with access to tools so it can do more than chat."
Strong answer
"The difference is control flow. A simple LLM call returns once. An agent maintains state, chooses whether to act, uses tools, reacts to tool outputs, and needs explicit stop conditions. That flexibility is valuable, but only if you add limits, validation, and observability around the loop."