A cloud agent works in an isolated virtual machine: the repository is cloned anew, dependencies are installed according to the environment description, secrets come from a separate store, network access is set by policy. It does not require your laptop to be on, can work in parallel with others like it, can drive a browser and a desktop, use external tools and open pull requests. All of that follows from one property: its life is not tied to your session, and therefore it obeys a description rather than a habit.
The naive picture is that this is the same local agent, only on someone else's machine. The difference is more fundamental: it does not have your working tree, your uncommitted edits, your environment variables or your local services. It sees exactly what is described in the environment and what lies in the repository. A task that silently relied on local state will honestly run into its absence in the cloud - and that is a benefit rather than a hindrance: the project's hidden dependencies surface once and explicitly.
So the main requirement for cloud work is a reproducible environment rather than a well-phrased task. Until the build and the tests pass on a clean machine, a cloud agent remains a remote generator of changes: it will do something, but there will be nothing to prove the result with, and verification will fall back to your own machine, that is, to exactly the place you wanted the work moved away from. The right order is to first get the environment preparation to succeed, check secrets and the network, and only then judge the quality of autonomous work.
It helps to lay the choice out in a table once: when to go to the cloud and when to stay local. Below is that map. It answers not which tool is more powerful but which is more appropriate: a long task that must survive closing the laptop, several independent agents, the need for a clean reproducible machine, work with artifacts and a remote desktop - that is the cloud. Local devices, a non-portable environment, a small edit and data that must not leave your boundary - that is local work.
| Choose the cloud | Stay local |
|---|---|
| A long task must survive the machine going off | Local devices or a non-portable environment are needed |
| Several independent agents are needed at once |
| One small change is faster to verify in the editor |
| A clean reproducible machine is required | Data must not leave your boundary |
|---|
| Artifacts, a remote desktop or automation are needed | There is no configured environment or connected source |
|---|
| The task spans several repositories | A multi-root workspace works only locally |
|---|
Launching is possible from many places: a phone, a browser, the desktop app, a messenger, a comment in the version control system, an issue tracker and through the programmatic interface. That is convenient and at the same time widens the surface: every launch point is another channel through which a task arrives, and it deserves a place in the threat model alongside the rest. A comment on an issue can be written by anyone with access to the tracker, and from that text to a running process there is now a single step.
The result comes back through a branch and a pull request - that is, through the same mechanism as any other work. That is an important property: a cloud agent does not create a special delivery channel for changes and fits into the ordinary review process. Its result must be accepted as strictly as a colleague's: autonomy of execution does not cancel verification and in some respects raises its cost, because the author will not be able to explain the decision in words.
One concrete failure is worth following, because everyone repeats it. The task looks simple: fix a handler that goes to the database. Locally it is verified instantly, because the database has been running next door since yesterday and the connection string lives in a file that never reaches the repository. In the cloud there is neither the database nor the file. The agent sees a failing test, honestly tries to fix it and does the only thing available without the service - it replaces the call with a stub. The change builds, the tests are green, the behavior is gone. The cause is not the model but the fact that a project dependency was never described, and the cloud merely surfaced it earlier than a new developer would have.
There is also a cost people rarely think about. Parallel agents save your time only up to the limit set by your attention: five simultaneous pull requests run into a single human who has to read them, not into the machine. Hence the practical rule - parallelize tasks that are cheap and independent to verify, and do not launch in bulk what will require careful reading. The sign that tells you in real work that the threshold has been crossed is a queue of unread agent changes growing faster than it is closed, and a rising temptation to accept them on the strength of a green build.
The engineering conclusion is simple: the cloud pays off where the value lies in autonomy and isolation rather than speed. It does not make the agent smarter, it frees your machine and gives a clean environment. Everything else - preparing the environment, rights, secrets, the network and reviewing the result - remains your work, and it is that work which decides whether there will be a benefit.
The typical failures are predictable. Sending a task that depends on local state to the cloud. Judging the quality of autonomous work before the environment builds and passes tests. Assuming that launching from a messenger or a tracker does not require the same trust model. Starting more agents than you are able to review. And accepting a cloud result more softly than your own because an agent produced it.