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An on-premises AI coding agent can access files, credentials, tools, and network resources available to its running process, subject to the permissions and isolation configured for it. Where the agent runs and where its AI model runs are separate choices: a locally installed agent may send prompts and code context to an external provider, while a self-hosted runner may still be part of a cloud-managed service.

To assess what stays inside your organization, check six boundaries separately: the agent process, filesystem, model endpoint, credentials, enabled tools, and network rules. “On-premises” describes a deployment location; it does not, by itself, establish that every component or data flow is local.

What determines what an on-premises coding agent can access?

The agent can generally act on resources its process can reach. Its actual access depends on the product, how it is configured, and the authority available to the process and its tools. For example, VS Code says its built-in agent tools are limited to the current workspace by default, with optional additional read access. That is a product-specific default, not a universal restriction for all coding agents.

Use these six layers to map the boundary:

  • Agent process: Identify where the agent application or service executes: a developer workstation, organization-managed server, self-hosted runner, or vendor environment.
  • Repository and filesystem: Check which checkout and paths the process can read or write. A workspace boundary may not cover other folders or system paths.
  • Model inference: Find out where prompts and selected code context go for completion. The model may run on the same machine, another internal service, or an external provider.
  • Credentials: Inventory tokens, environment variables, SSH-agent access, cloud credentials, and secrets available to the process or its tools. A credential being available to a tool does not mean the model automatically sees it.
  • Tools: Check for terminal, browser or fetch, MCP servers, database clients, deployment tools, and other integrations that can reach beyond the repository.
  • Network: Review outbound destinations and inbound connectivity. Proxies, firewall allowlists, and sandbox network rules determine which systems the process can contact.

These boundaries interact. A tool may be limited to a workspace but still run commands with the user’s permissions; a model may be local while an extension or tool contacts an external service. Product documentation describes capabilities and configuration examples, not a universal security guarantee.

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Can an AI coding agent read the whole codebase?

It depends on the agent’s file scope and permissions. Cline says it reads project structure and can make coordinated changes across a project. VS Code documents workspace-limited built-in agent file access by default, while allowing additional read access to be configured. Neither example establishes how every product behaves, or what a specific installation can reach outside its workspace.

Before use, confirm the selected workspace, permitted read and write paths, and whether the agent can access sibling directories, home folders, or system paths. Also check whether tools can retrieve files through a shell or other integration, since those routes may have different limits than the agent’s built-in file access.

Does the model run locally?

Not necessarily. An on-premises agent and its model can run in different places. Cline lists local runtimes such as Ollama and LM Studio as well as hosted and self-hosted model endpoints. A locally installed agent can therefore use a remote model, and a self-hosted endpoint may run on a separate machine or service.

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GitHub’s Copilot CLI documentation offers a concrete example of why “local model” and “offline” should not be treated as synonyms: when configured to use a user’s own model provider, prompts and code context go directly to that provider. Its offline mode restricts requests to the configured provider, disables web-based tools and several GitHub-connected features, and disables telemetry to GitHub; it still contacts the model provider.

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For any setup, identify which provider receives prompts, code context, and responses, then review that provider’s data-handling terms and the product’s network behavior. The available documentation does not support a general percentage for how much code an on-premises deployment transmits; the amount depends on the product and configuration.

Can it reach internal systems?

Potentially, if the process or an enabled tool has network access and suitable credentials. Cline documents terminal commands and MCP connections to databases, APIs, and cloud infrastructure. That makes tool configuration and credential scope as important as repository access: a coding agent with a database connector or cloud CLI may be able to operate beyond the code it edits.

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GitHub documents that its cloud agent can use self-hosted runners to align with CI/CD or access internal-network resources. This does not make the entire service on-premises: GitHub also documents service endpoints and networking requirements for runners. A runner’s physical location is only one part of the deployment boundary.

When a runner or tool can reach internal systems, treat it as a privileged environment. Limit permitted hosts, isolate the runner, and use ephemeral single-use runners where appropriate, as GitHub recommends for self-hosted runner use.

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How do common deployment options differ?

Deployment What the documentation establishes What to verify for your setup
Local agent with local model Cline lists local Ollama and LM Studio models among its provider options. Whether the agent, model, extensions, embeddings, telemetry, and tools are all local. A local model listing alone does not establish that the entire setup works offline.
Local agent with external model provider Cline supports provider endpoints; GitHub’s BYOK documentation says prompts and code context go directly to the selected provider. Which provider receives which content, and what network and data-handling terms apply. An IDE running locally does not make an external inference path on-premises.
Cloud agent in a vendor environment GitHub says Copilot cloud agent uses an ephemeral GitHub Actions development environment to explore code, edit, and run tests. Which repository, branch, tools, secrets, and network destinations the agent can access.
Cloud agent on a self-hosted runner GitHub documents self-hosted runners as a way to align with CI/CD or provide internal-network access, and recommends ephemeral single-use runners and network controls. The external service and inference connections, permitted hosts, and runner lifetime, in addition to the runner’s location.

Compare a specific deployment on four axes: where the agent process runs; where inference happens and which provider serves it; which files and credentials are in scope; and which tools and network destinations are available.

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How should you limit access safely?

Scope files and permissions

Confirm the product’s actual file scope rather than assuming “workspace” means only the repository. In VS Code, built-in agent tools are workspace-limited by default, with additional read access configurable. Check the setting used in your environment and whether any other agent or extension has broader access.

Choose tools and approvals deliberately

Review the enabled tools and their permission levels. VS Code documents a tools picker and permission settings. Cline says edits and terminal commands require approval by default, while auto-approval can be enabled. Approval behavior varies by product and configuration, so verify the active setting instead of relying on a general expectation.

Assume shell commands inherit process authority

VS Code’s security documentation says development tasks operate with the same permissions as the user. Commands can therefore have consequences beyond editing a file if the process has access to other resources. VS Code documents OS-level sandboxing and recommends sandboxing or a dev container when prompt injection is a concern; its security guidance also warns that approval rules have limitations.

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Keep credentials and network reach narrow

Pass only the secrets a particular agent or tool needs. GitHub says its cloud agent cannot access general Actions organization or repository secrets; only secrets and variables specifically added to its copilot environment are passed to the agent. This is a GitHub-specific control, not a guarantee for other products.

For self-hosted runners, configure firewall controls and allow only required hosts. Review outbound connections as well as access to internal systems, especially where the runner is connected to a private network.

Check every path that sends data to a model provider

Trace which prompts, code context, and responses go to the selected model provider. If the goal is to prevent data leaving the organization, verify the endpoint and all related components—including extensions and tools—rather than relying on the agent’s installation location or the label “local.”

Which documentation should you check?

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