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What “on-premises” means—and what it leaves unanswered
There is no universal cross-vendor definition that settles where every component of an AI coding agent runs. In practice, the term describes a deployment and control arrangement: the organization operates relevant software or services on infrastructure it manages. That could include the agent process, a model-serving service, or both.
These are separate architectural questions. An agent can run in a developer’s IDE while sending prompts and code context to a remotely hosted model. Conversely, an organization could use a remotely provided agent interface with a model or other services hosted separately. The product’s data path, not its deployment label, determines what leaves the organization’s environment.
Visual Studio Code’s documentation makes a product-specific distinction: local agents run on a developer’s machine and process data locally, while cloud agents run on GitHub infrastructure and are subject to GitHub Copilot data-handling policies. GitHub also documents local IDE agents separately from its asynchronous cloud agent. These descriptions explain those products; they are not a standard definition for every vendor. Visual Studio Code: Manage AI settings in enterprise environments; GitHub Docs: Agent management for enterprises.
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Which components to locate
To evaluate a deployment, map where each component executes and who operates it. A single “local” or “on-prem” label may conceal a mixed architecture.
- Agent execution: Does the agent run in the developer’s workstation or IDE, on organization-managed infrastructure, or in a provider’s cloud?
- Model inference: Where does the model process prompts and code context—a local or organization-managed model service, or a remote provider endpoint? Confirm this independently of the agent’s location.
- Repository and context services: Where are source code, indexed context, and retrieval requests processed or stored?
- Tools and execution environment: Can the agent reach terminals, MCP servers, APIs, package registries, or other systems? Where do those tools run, and which credentials do they receive?
- Logs and telemetry: What is collected, where is it retained, who can access it, and for how long?
- Operations and governance: Who patches and monitors components, sets policy, retains audit records, and responds to incidents?
For each service, establish what data it receives, what it returns, and whether it is operated by the organization or a provider. GitHub documents enterprise-level agent management and controls, but the available controls vary by product. GitHub Docs: Agent management for enterprises.
Is on-premises the same as keeping code inside your network?
No—not based on the label alone. A locally running agent may still send prompts or code context to a remote model, use external services, or make requests to connected tools. To establish whether code stays within a defined network or boundary, verify the complete data flow and the specific product’s terms, including handling of prompts, code, logs, telemetry, and tool requests.
Ask the vendor or implementation team for a component diagram and written details on data retention, use for model training, residency, and administrative controls. The evidence needed is product-specific; there is no one answer that applies to every coding agent.
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How local and cloud coding agents differ
Local IDE agent
A local IDE agent operates in the developer’s local environment, but that does not establish that model inference is local. Check the configured model endpoint and every connected service. In VS Code, the documentation describes local agents as running on the developer’s machine and processing data locally; verify the chosen configuration rather than extending that description to other products or services. Visual Studio Code: Manage AI settings in enterprise environments.
Cloud agent
A cloud agent can work asynchronously on provider infrastructure rather than solely in a developer’s local environment. GitHub describes its cloud agent as able to work from an issue or prompt and create a pull request. GitHub also says code generated by third-party coding agents is scanned for security issues before a pull request is finalized. That is a product-specific safeguard, not a guarantee that generated code is safe or a feature to assume elsewhere. GitHub Docs: About third-party coding agents.
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Security depends on access and controls, not location alone
Hosting components yourself does not automatically make an agent secure or isolated. An agent with access to files, commands, credentials, or external systems can take consequential actions wherever it runs. Review both what it may access and what it may do.
- Limit workspace access: Define which files and repositories the agent can read or change.
- Constrain tools: Review enabled tools, MCP servers, APIs, and allowed network destinations; provide only the credentials each task requires.
- Sandbox execution: Restrict terminal and build actions. VS Code documents workspace-limited file access, a tool picker, temporary session permissions, and terminal sandboxing; its security guidance also points to sandboxing or a dev container as ways to limit impact. Visual Studio Code: Secure AI-assisted development in VS Code.
- Review changes: Require appropriate human review before code is merged or consequential actions are taken.
- Set operational controls: Establish patching, monitoring, log retention, incident response, and permission review for every component.
For GitHub Copilot cloud-agent workflows specifically, GitHub recommends planning policies and guardrails, reviewing GITHUB_TOKEN permissions, and using GitHub-hosted runners or ephemeral self-hosted runners where applicable. Those are recommendations for that cloud-agent workflow; they do not make the deployment on-premises. GitHub Docs: Building guardrails for GitHub Copilot cloud agent.
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Not necessarily. The label does not establish a hardware requirement. What an organization needs depends on which components it chooses to host, along with the model, expected throughput, concurrency, and operating constraints. There is no universal minimum specification implied by “on-premises.”
Quick Recap
Questions to settle before choosing a deployment
- Where does the agent process run, and who administers it?
- Where does model inference happen, and what code or prompts are sent to that endpoint?
- Which repositories, retrieval services, MCP servers, terminals, APIs, and package registries can the agent access?
- What data is logged or retained, where is it stored, and is it used for training?
- Which credentials and network destinations are available to the agent, and how are permissions limited?
- What isolation, human review, audit, patching, and incident-response controls apply to each component?
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