Q: What is an AI agent, actually?

An agent is an LLM running in a loop with tools and a goal.

That's the whole definition, and each part matters. A plain LLM call is one round: text in, text out. An agent is different. You give it a goal and a set of tools it's allowed to use. The model looks at the goal, decides on an action, calls a tool, sees the result, and then decides what to do next. It repeats this loop until it judges the goal is complete. That loop is called the agentic loop, and the model's ability to decide the next step itself is what makes it "agentic."

A concrete example: you ask an agent to research a company. It searches the web (tool call), reads the results, decides it needs financials, fetches those (another tool call), notices a gap, searches again, and then writes the summary. Nobody scripted that sequence. The model chose each step based on what the previous step returned.

This is also what separates agents from classic automation. A workflow runs fixed steps in a fixed order. An agent decides its path as it goes. That flexibility is the power, and it's also why agents need guardrails, evals, and traces, because a system that chooses its own steps can choose wrong ones.