What is an LLM agent?

An LLM agent is an AI system that doesn't just answer questions — it takes actions to accomplish a goal, using an LLM as its decision-making brain. Where a normal chatbot responds and stops, an agent can plan a series of steps, use tools, check the results, and keep going until the task is done. Give it a goal like "research these three competitors and draft a summary," and an agent might search the web, open pages, extract details, and write the report — deciding each next move on its own.

How is an agent different from a chatbot?

A chatbot is a single round trip: you ask, it answers, and it's finished. An agent runs in a loop. It looks at the goal, decides on an action, actually performs that action, observes what came back, and then decides what to do next — repeating until it judges the task complete.

That difference between producing one fixed reply and working through a task step by step is the whole shift from AI that talks to AI that does.

What are the building blocks of an agent?

Most agents are assembled from a few core parts:

• Tools — the ability to do things beyond text: search the web, run code, query a database, send an email, or call an API.
• A loop — the act-observe-adjust cycle that lets it react to results instead of committing to one guess up front.
• Memory — some way to keep track of the goal and what it has already done across many steps.
• Planning — breaking a big goal into an ordered set of smaller moves.

Where do agents fall short?

They're capable but not flawless. Because the LLM is deciding each step on its own, an agent can take a wrong turn, get stuck repeating itself, or misuse a tool — and a small early mistake can snowball across a long chain of actions. That's why most real systems add guardrails, limits on what tools can do, and human checkpoints for anything risky.

The autonomy is exactly what makes agents useful and exactly what makes them worth supervising.