An AI agent is software that wraps a large language model in a loop: it receives a goal, decides what to do next, takes an action (usually by calling a tool or API), observes the result, and repeats until the task is complete or it gives up. The defining feature is autonomy over multiple steps — unlike a single prompt-and-response call, an agent makes a sequence of its own decisions to get from a goal to an outcome.
In practice, an agent is built from a few moving parts: a model that does the reasoning, a set of tools it is allowed to call (search, a database query, a code interpreter, an internal API), a memory or context mechanism so it can carry state across steps, and a control loop that decides when to keep going and when to stop. The hard engineering is rarely the model itself — it's defining the tool surface, bounding what the agent can touch, and handling the cases where a step fails or the model proposes something nonsensical.
Production agents are most valuable for tasks that are multi-step, span several systems, and would otherwise need a person to copy data between tools — resolving a support ticket that touches three back-office systems, triaging an alert, drafting and filing a structured document, or running a research task across many sources. They are a poor fit when latency must be low and deterministic, when a single retrieval call would do, or when a wrong action is expensive and hard to reverse.
The reason agents matter is leverage: they turn a model from a text generator into something that can complete work end to end. But that same autonomy is the risk. A useful agent needs guardrails on its actions, evaluations that test it against real tasks rather than vibes, and observability so you can see every step it took when something goes wrong. Most teams that ship agents successfully start narrow — one well-bounded workflow with a tight tool set — and widen scope only once the evaluations hold.