An AI agent is a piece of software that uses an AI model to pursue a goal, deciding its own steps and taking actions along the way. The key word is action: where a plain model returns text, an agent can read data, call a tool, check the result, and move on to the next step.
Think of the difference between a vending machine and a personal assistant. A vending machine gives you exactly what you punch in. An assistant takes a goal like “book me a flight under 200 euros” and handles the messy middle: searching, comparing, booking, confirming. That action-taking behaviour is what people mean by agentic AI, and it usually relies on agent memory so the agent remembers context between steps and across sessions.
In practice an agent is rarely magic on its own. It needs clear permissions, a defined set of tools, and a sensible scope, because an agent with vague instructions wanders. Give it a precise brief, say “tag every new support ticket by topic and assign urgent ones to the on-call queue,” and it stays on the rails. Hand it a vague one like “improve our support,” and it spends ten steps guessing what you meant. The good news is that a well-scoped agent is also predictable, which is what makes it safe to put near real customer work, where a wrong move costs trust rather than a wasted prompt.
At TopDevs we build AI agents the way we build any software, with a tight job description, logged actions, and a human checkpoint before anything irreversible, so the agent is useful without being a loose cannon.
