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Choose a local AI coding agent when you need to steer it closely, work with uncommitted files, or keep workspace access on your machine. Choose a cloud agent when a task is clearly defined, the repository is on GitHub, and you want work to continue asynchronously or return as a branch or pull request. The deciding question is not simply where the model runs: it is where the agent can access code and tools, and how much hands-on feedback the task needs. “Local” does not necessarily mean offline or that no data is sent for model processing.
What is the difference between a local and a cloud coding agent?
A local agent works directly in a workspace on your device, typically through an IDE or terminal session. A cloud agent works in a provider-managed environment against a GitHub repository, and commonly hands its changes back through a branch or pull request. These are workflow distinctions: the agent’s environment, how you interact with it, and where the work is handed off all change.
Microsoft’s Visual Studio Code documentation describes local third-party sessions as working in the local workspace and cloud sessions as working with a GitHub repository. Anthropic makes a similar distinction for Claude Code: local sessions are interactive, while cloud sessions can keep running after you leave and may open a pull request when complete. Neither label alone tells you where inference, telemetry, or every tool call is processed; check the selected provider’s data and privacy terms.
What “local” does—and does not—mean
Local means the agent operates on files in the local workspace. That is useful when your current work includes changes you have not committed or pushed. But it is not a guarantee that the model runs on your device or that the session works without internet access. For example, Anthropic lists internet access for Claude Code authentication and AI processing, even when it is installed locally.
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What “cloud” does—and does not—mean
A cloud task runs in a provider-managed environment rather than depending on your active local workspace. In Claude Code’s documented cloud workflow, the environment is an isolated virtual machine that clones the selected GitHub repository, runs configured setup commands, and can push a branch and open a pull request. That does not mean a cloud agent can automatically work on any repository: the repository and provider access must be available and configured.
Which setup is better for your project?
Start with the task’s uncertainty, repository state, and need for supervision. Local is the safer starting point when requirements are still taking shape or the agent must work with files that have not been pushed. Cloud is a stronger starting point for bounded work you can delegate and review later.
| Project condition | Better starting point | Reason |
|---|---|---|
| Requirements are unclear or likely to change | Local | You can inspect results, redirect the agent, and iterate immediately. |
| The task touches uncommitted files | Local | The agent can work in the active workspace without first pushing a branch. |
| A bug or feature is precisely specified | Cloud | You can delegate it and review the resulting branch or pull request later. |
| You have independent backlog issues | Cloud | Cloud work can run unattended; Claude Code’s workflow supports parallel work in isolated environments. |
| The repository is not cloned on your device | Cloud | A provider can clone a GitHub repository and prepare the environment remotely. |
| Code must stay within a controlled machine or network | Local, subject to the provider’s processing path | Workspace access remains on the machine, but model inference and telemetry still need verification. |
| Your team needs a review handoff | Cloud | A branch or pull request gives reviewers a shared place to inspect proposed changes. |
Choose local when the work is exploratory
Local work suits investigation, debugging, and features whose shape becomes clearer as you inspect the code. You can ask the agent to examine a result, change direction, or stop before it continues down the wrong path. It is also the practical choice when the relevant code exists only in your current working tree.
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Choose cloud when the work is ready to delegate
Cloud work is useful when you can describe an issue clearly enough for an agent to make progress without constant steering. It can be a good fit for independent backlog items, tasks that benefit from parallel execution, or work that should be reviewed later by someone else. The handoff is especially useful when the proposed changes can be evaluated as a branch or pull request.
Use a hybrid workflow when the project has both kinds of work
Local and cloud are not mutually exclusive choices for an entire project. A sensible split is to investigate ambiguous work and make changes that depend on local state in a local session, then delegate well-bounded issues against the GitHub repository. Before delegating, make sure the relevant code and setup instructions are available in that repository; do not assume a cloud environment can see unpushed local files.
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What should you compare before choosing an agent?
Compare the working boundary and handoff, not only the model name. Visual Studio Code explains that an agent harness coordinates tools, approvals, requests, and session state, so two sessions using a similar model can still behave differently because their harnesses and environments differ.
| Decision axis | What to establish | Why it matters |
|---|---|---|
| Workspace and data boundary | Which files and services can the session access, and where inference and telemetry are processed? | Local workspace access does not establish that model processing is local. |
| Repository availability | Does the agent work with the active local workspace, or require an accessible GitHub repository? | Cloud sessions documented by Visual Studio Code require a GitHub repository and access to an available cloud provider. |
| Interactive control | Can you review output and redirect the session while it works, or do you expect to return later? | Exploratory tasks often need synchronous course correction; delegated tasks may not. |
| Latency and feedback | How quickly can you inspect a change and continue the conversation? | Immediate interaction is valuable when each result changes what you want the agent to do next. |
| Permissions and tool access | What tools, approvals, and credentials are available to the session? | Access determines what the agent can attempt, and approvals affect how closely you supervise it. |
| Isolation | Does the work happen in your active workspace or a separate environment? | Isolation can separate delegated work from your current local changes, but you still need to inspect its result. |
| Setup and tests | What installation, environment setup, and test commands are required? | A cloud task depends on the remote environment being prepared correctly; local work depends on your machine’s setup. |
| Review handoff | Will changes stay in the workspace, or arrive as a branch or pull request? | The handoff should match how you and your team review and merge code. |
There is no authoritative performance statistic, benchmark, or productivity percentage in the available product documentation that establishes one mode as faster or more accurate in general. The better choice depends on the particular task, repository, environment, and review process—not a universal local-versus-cloud score.
How do local and cloud workflows differ in practice?
A local session starts from the code and environment on your device. A cloud session starts from a repository and a remote environment. That difference changes what you should prepare before asking the agent to work.
Before a local session
- Open the intended project folder in the IDE or terminal session, and check which files already have local changes.
- Give the agent a bounded request and enough context to understand the desired behavior; keep an eye on its actions when requirements are uncertain.
- Review the edits in the workspace and run the relevant tests using the project’s existing commands.
Because the session works in the active workspace, it can be useful before changes are ready to push. It also means you should know which local files are in scope and inspect the resulting diff before accepting it.
Before a cloud session
- Confirm that the repository is on GitHub and that the selected provider can access it.
- Describe a specific task, including expected behavior and any relevant constraints or tests.
- For workflows that use configured setup commands, ensure they prepare the remote environment the task needs.
- Review the returned branch or pull request, including its changes and test results, before merging.
Cloud setup is not automatic magic: an agent can only work effectively with the repository content and environment available to its task. If a required file, dependency, or setup step is absent from the repository or configuration, the remote session may not reproduce your local setup.
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Which tools illustrate the two approaches?
These examples show how the workflow distinction appears in current provider documentation; they are not a claim that one product is universally better. Availability, preview status, quotas, pricing, supported repositories, and organization controls can change, so confirm the provider’s current documentation before adopting a workflow.
| Tool or workflow | Documented local option | Documented cloud option | Practical detail |
|---|---|---|---|
| Claude Code | Local installation for supported desktop environments | Isolated virtual machine that clones a selected GitHub repository | The cloud workflow can run configured setup commands, push a branch, and open a pull request. |
| OpenAI Codex in Visual Studio Code | Listed as a provider harness for local sessions | Listed as a cloud-agent option working against GitHub repositories | The choice is between working in the local session and delegating against a repository. |
| GitHub Copilot Code | Microsoft says local Copilot Code works inside project folders on the device | Microsoft describes cloud Copilot Code as using a secure isolated environment accessible across devices | Check Microsoft’s current documentation for supported access and availability in your environment. |
Claude Code local requirements
Anthropic documents Claude Code local installation for macOS, Ubuntu/Debian, and Windows with WSL or Git for Windows. Its listed requirements include Node.js 18 or later, at least 4 GB of RAM, and internet access for authentication and AI processing. Those requirements apply to the documented Claude Code setup; they should not be treated as requirements for every coding agent.
How should you handle private or uncommitted code?
For uncommitted files, local is usually the more direct option because the agent can work in the active workspace without requiring you to push a branch first. For private code, “local” is not by itself a complete privacy guarantee: check the provider’s processing path, telemetry, permissions, and organizational policy before using it.
Cloud work requires a repository and a provider environment with access to it. If the code cannot be placed in the GitHub repository or made available to that provider under your organization’s rules, a cloud workflow may not fit. Conversely, if policy permits cloud processing but requires a review boundary, a pull request can make the handoff visible without granting the agent access to your local working tree.
- For local changes: use the local workspace when the task depends on edits that have not been pushed.
- For sensitive repositories: verify where code is processed and what account, network, or enterprise controls apply.
- For cloud delegation: confirm repository access and the remote environment’s scope before assigning work.
What are the main failure modes?
Most problems arise from a mismatch between the task and the environment, rather than from the local or cloud label alone. Check these conditions before switching modes or assuming the agent has done what you intended.
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The cloud task cannot find the code or setup it needs
A cloud session works against the GitHub repository and its prepared environment, not arbitrary unpushed files on your device. Ensure the required code is in the repository and that configured setup commands prepare its dependencies before expecting a remote task to run successfully.
The agent makes the wrong changes because the task is underspecified
A precise backlog issue is a better cloud delegation than an investigation whose goal may change after each finding. If the agent needs frequent clarification, bring the work into an interactive local session or narrow the cloud task to a result that can be reviewed independently.
The local session raises a privacy assumption
Workspace access on your device does not prove the model inference or telemetry stays there. Confirm the selected provider’s processing and policy details; if that boundary is unacceptable, do not treat a local installation as sufficient assurance.
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In either mode, inspect the actual changes and run relevant tests before accepting them. A cloud pull request provides a shared review point, while a local session leaves the edits in the workspace; neither handoff substitutes for review.
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Ask two questions: Does the agent need access to local state or close, immediate steering? If yes, start locally. Is the task well specified, available in GitHub, and useful to run without your active supervision? If yes, cloud is a reasonable starting point. For work that has both characteristics, investigate locally and delegate only the clearly bounded portion that can be reproduced and reviewed from the repository.
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Frequently Asked Questions
Does “local” mean an AI coding agent works offline?
Not necessarily. Anthropic lists internet access for Claude Code authentication and AI processing even for its local installation; requirements can differ across providers, so check the documentation for the agent you plan to use.
Can a cloud coding agent keep working after I close my laptop?
Cloud sessions can be designed to continue without your active supervision because they run in a provider-managed environment. Whether a particular task continues, and what it can access, depends on that provider’s workflow and setup.
Do I need to commit or push all my work before using a local coding agent?
No; a local agent can work directly in the active workspace, including files that have not been pushed. Keep track of existing changes and review the resulting edits so unrelated work is not accidentally included.
What should my team verify before enabling cloud coding agents?
Confirm which repositories and provider environments are permitted, who can review resulting branches or pull requests, and what organization policies govern code processing. Those controls and availability vary by provider and can change over time.
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