What is an AI agent?

Why it matters

A plain chatbot answers the question in front of it and stops. An AI agent keeps going: it breaks a goal into steps, decides what to do next, and uses tools to get there. That difference is what lets you automate real workflows, like triaging a support ticket, pulling data from three systems, and drafting a reply, instead of just having a conversation. The trade-off is that more autonomy means more ways to go wrong, so agents need guardrails and a clear stopping point.

How it works, briefly

Most agents are an LLM wrapped in a loop. The model reads the goal and the current state, picks the next action, and calls a tool: a search, an API, a database query, a calculation. The result comes back, the model reads it, and decides the next step, repeating until the goal is met or a limit is hit. Good prompt engineering shapes how the agent reasons and which tools it reaches for, and a retrieval step like RAG gives it grounded facts to act on rather than guesses.

How we use it

We build AI features and agents into client products where they earn their keep, not as a demo. That usually means a tightly scoped agent with a few well-defined tools, hard limits on what it can do, and checks on its output, so it stays reliable in front of real users rather than impressive only in a sandbox.