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?
