You’ll increasingly see AI tools described as either a “copilot” or an “agent”, and the distinction is more than marketing.
A copilot sits alongside you and assists with a task you’re still doing. Think of Microsoft Copilot suggesting text in a document, or an AI tool drafting a reply for you to review and send. You stay in control of every step.
An agent is given a goal and takes a series of steps on its own to achieve it, searching, comparing options, filling in forms, even taking actions in other tools, checking in with you at key points rather than every step.
Why the difference matters for businesses:
- Copilots are lower-risk and easier to adopt today, they speed up work a person is already doing, with a human reviewing the output
- Agents can save more time on multi-step tasks (research, data gathering, scheduling) but need more careful setup, clear boundaries on what they can access and do, and checkpoints for anything important
A sensible approach: start with copilot-style tools for tasks your team already does (drafting, summarising, searching). As you get comfortable with where AI output needs checking, agent-style tools become useful for well-defined, lower-stakes multi-step tasks, research summaries, data collection, first-pass scheduling.
The terminology will keep shifting, but the underlying question stays the same: how much of this task can AI do reliably, and where does a person still need to check the work?
