For an AI coding agent, on-premises generally means an organization hosts and administers the relevant components on infrastructure it controls. The label alone does not tell you whether the agent runs locally, where its AI model processes requests, or whether code and other data reach external services. Check each part of the system separately.
What “on-premises” means in practice
There is no universal cross-vendor definition that settles every component’s location. In practical terms, an organization is describing who hosts and controls some part of the deployment—not necessarily the entire path from a developer’s prompt to a code change.
For example, an agent interface can run on a developer’s workstation while sending prompts to a remotely hosted model. The agent might also call external tools or services. Those components have different locations and data flows, so “the agent is local” does not by itself mean inference is local or that data stays within the organization.
Visual Studio Code distinguishes local agents, which it describes as running and processing data on a developer’s machine, from cloud agents running on GitHub infrastructure. These are product-specific descriptions, not a definition that applies to every vendor. Visual Studio Code’s enterprise AI settings documentation explains that distinction.
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Which parts should you check?
Ask where each component runs, who operates it, and what information it receives. A deployment may mix organization-controlled components with provider-hosted services.
| Component or question | What to establish |
|---|---|
| Agent execution | Does the agent process run on a developer workstation, organization-managed infrastructure, or a provider’s cloud? |
| Model inference | Does a local or organization-managed model service process requests, or does the agent call a remote provider endpoint? |
| Data flows | Where do source code, prompts, retrieved context, logs, and telemetry go? What is retained, and under what terms? |
| Tools and network access | Can the agent reach repositories, terminals, MCP servers, APIs, package registries, or other destinations? Which credentials and outbound connections are permitted? |
| Operations and control | Who patches and monitors components, sets policies, retains logs, and responds to incidents? Controls vary by product. |
| Isolation and review | What limits workspace access and tool permissions? Are terminal actions sandboxed, execution environments temporary, and changes reviewed by a person? |
Request a component diagram and written answers on data retention, use of data for training, residency, and administrative controls. The answer needs to describe the particular product and configuration; the term “on-premises” cannot supply those details.
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Does on-prem mean code never leaves your network?
Not necessarily. That depends on the agent’s model endpoint, connected tools, repositories, logging and telemetry, and other services. If a local agent sends code or prompt context to a remote model, that information leaves the local environment. An agent can also make external tool requests even when its main process runs on infrastructure the organization controls.
Trace the full path: the IDE or agent host, model endpoint, repository and retrieval services, MCP or other tools, shell and build environment, logs and telemetry, identity and secrets, and outbound network access. Confirm what crosses the organization’s boundary and the applicable retention and processing terms.
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Can the agent run locally while the model runs in the cloud?
Yes. Agent execution and model inference are separate architectural choices. A locally running IDE agent can call a remote model endpoint; a provider-hosted cloud agent is a different arrangement. Verify both locations instead of inferring one from the other. Visual Studio Code’s documentation distinguishes local agents from cloud agents by execution location and data handling: Manage AI settings in enterprise environments.
Cloud agents may also perform work asynchronously on provider infrastructure. GitHub describes its cloud agent as able to work from an issue or prompt, make changes, and create a pull request. That workflow is not equivalent to an agent working solely in a developer’s local environment. GitHub’s documentation on third-party coding agents also says generated code from those agents is scanned for security issues before a pull request is finalized. That product safeguard is not a guarantee that generated code is safe, and it should not be assumed for other agents.
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What security and operating controls matter?
Hosting components yourself does not automatically make a deployment secure or isolated. An agent may read code, run commands, or interact with external systems, so its permissions and network reach should be deliberately limited.
VS Code documents controls including workspace-limited file access, a tool picker, temporary session permissions, and terminal sandboxing. Its guidance notes that sandboxing or a development container can help limit the impact of consequential tool actions. See Secure AI-assisted development in VS Code.
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For GitHub Copilot cloud-agent workflows, GitHub recommends establishing policies and guardrails, reviewing GITHUB_TOKEN permissions, and using GitHub-hosted runners or ephemeral self-hosted runners where applicable. These recommendations address that cloud-agent workflow; using a self-hosted runner does not, on its own, make the overall system on-premises. See Building guardrails for GitHub Copilot cloud agent.
Does on-premises deployment require a dedicated server or GPU?
Not from the label alone. Hardware depends on which components the organization chooses to host, including whether it hosts model inference. The product documentation cited here does not establish a universal minimum specification. To assess hardware, first identify the model, expected throughput and concurrency, and operating constraints.
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