"Agent": everyone uses the word, rarely with the same meaning. Marketing has loaded it with promises, the press with superlatives.
Behind the label, though, hides a simple idea, one that's useful to an executive. Understanding it spares you plenty of misunderstandings, and a few bad decisions.
Giving meaning back to the word "agent"
The word "agent" is everywhere. Vendors talk about it, the press multiplies the promises, your teams use it. Over time, it no longer designates anything very precise.
You're not the only one to notice. Engineer Simon Willison collected 211 different definitions of the word from his peers. As early as 1994, researchers already acknowledged that it "defies any universal definition".
This fuzziness isn't neutral. As long as the word stays vague, you can't decide what an agent would do at your company. Restoring meaning is the prerequisite for any decision.
Chatbot, copilot, agent
The confusion comes from a stacking-up. Under a single label, three distinct things are actually grouped together.
- The chatbot answers. It converses, rephrases, explains, then waits for your next question.
- The copilot assists. It proposes, suggests, prepares. You keep a hand on every step.
- The agent acts. It pursues a goal and triggers actions to reach it.
These three levels aren't opposed, they complement each other. But confusing them means expecting a chatbot to act, or fearing that a copilot will decide in your place.
What really makes an agent
An AI agent comes down to three ingredients. A goal to reach. A capacity for action, via tools, for example querying a database or sending a message. A degree of autonomy, wider or narrower.
The language model is only one of these ingredients. It's the engine, not the car. A powerful engine doesn't drive itself: it needs a transmission, brakes, a dashboard and a driver. Simon Willison sums up the agent in one formula: a system that chains tools together, in a loop, to reach a goal.
One point deserves your attention. A system that handles language very well isn't a system that acts reliably. The fluency of an answer says nothing about the correctness of an action. This distinction keeps you from confusing a brilliant demo with a production tool.
A concrete example
Take a customer service agent. A simple chatbot would tell you your order is "being processed". An agent, on the other hand, goes further.
It checks the order's real status in the system. It opens a ticket if an anomaly appears. It sends a follow-up to the carrier, then keeps you informed. It doesn't just talk, it chains actions toward a result.
According to Gartner, nearly a third of enterprise software could integrate this kind of capability by 2028 (forecast reported by L'Usine Digitale). The phenomenon is real, even if the word remains fuzzy.
"Autonomous", a false friend
The word "autonomous" worries or excites, depending on the mood. In a business context, it's best to defuse it. An agent's autonomy isn't a state, it's a dial.
An agent can propose an action and wait for your approval. It can execute low-stakes steps on its own. It can, within a narrow scope, act without intervention. The right setting depends on the cost of an error and the ability to undo it.
So the right formula isn't "total autonomy", but governed delegation. You define the objectives upfront. You supervise sensitive actions. AI proposes and executes, you arbitrate and take responsibility.