What stays local when your agent works remotely
Remote execution does not require a remote agent loop. Keep intelligence local and move only the tools that create effects.
It is easy to treat an AI agent as one indivisible system. In practice, the model, conversation, orchestration loop, tools, and workspace are separate parts. Choosing where each part runs gives you more control than moving the entire stack to one provider or one machine.
Keep the decision loop close
The model and agent client hold the conversation, choose tools, and decide what to do next. Keeping that loop local preserves your preferred agent, model routing, prompt configuration, and interaction history.
Remote execution then becomes a capability of the agent rather than the place where the agent itself must live.
Send requests, return results
The bridge only needs to carry structured tool calls and their results. A request might read a file, run a test, or apply an edit. The remote workspace performs the operation and returns the information the agent needs for its next decision.
That flow is easier to inspect than broad remote control because each crossing has a declared operation and an observable result.
Place data deliberately
A remote workspace still contains project data, so its storage and access policy deserve the same care as any development environment. The useful distinction is that unrelated local data never needs to enter the workspace at all.
Good architecture minimizes both the authority of the remote machine and the amount of context sent across the boundary.