What it means in practice
You can run an AI agent in three places, and the difference between them decides whether a prototype turns into something usable.
| Where it runs | What it is good for | What to watch |
|---|---|---|
| Locally on a laptop | Personal experiments, quick prototypes, temporary internal tools. Fast, simple, cheap. | Hard to share, data and access keys are local, reliability is limited. |
| Managed agent in the cloud | Long-running and repeated tasks, work with memory and tools. Runs with your computer switched off. | Access and permissions, data boundaries, API costs, credential management. |
| Hosted application | Shared internal applications and customer-facing products, a controlled user environment. | Deployment, monitoring, security, maintenance, scaling, ownership. |
When to move from the laptop to the cloud
When the task should run regularly without anyone starting it. Or when it takes a long time. Or when more people than you are waiting on the result.
A typical example from practice: an agent that sends the director a detailed report at set intervals. On a laptop it would only work while the laptop is on, which is exactly the kind of dependency companies do not want.
What to be careful about
The more access, tools and context an agent has, the more useful it is and the more security matters. A managed agent in the cloud typically has more access than a script on a laptop, so it is worth being clear about which data it can reach and who approved that.