Private host or on-prem
Run agent infrastructure in the environment selected for sensitive operations, including private cloud or data-center patterns.
Private AI agent deployment
AI agents on premise, under enterprise control
Bewize deploys managed AI agent runtimes on customer-controlled Linux infrastructure. Enterprise teams keep control of tenant boundaries, credentials, workspaces, storage, logs, updates, recovery, and network placement. Model hosting remains a separate architecture decision, so an on-premise agent runtime does not automatically mean a privately hosted LLM.
Answer
What does it mean to deploy AI agents on premise?
It means the agent runtime and its operating state run inside infrastructure your company controls, whether that is a private cloud or data center. Bewize manages the control-plane and runtime layer: tenant identity, lifecycle, policy, credentials, workspaces, storage, schedules, adapters, logs, updates, recovery, and access state. It does not promise a certification, an absolute security outcome, or a bundled private LLM server.
Enterprise boundary
Control plane, tenant runtime, secrets, workspace, logs, and adapters stay under operations ownership.
Private host
- Tenant runtime
- Managed secrets
- Workspace
- Shared storage
- Logs
- Adapters
Tenant runtime isolation
Separate tenant state through runtime identity, workspace, secrets, policy, and launch controls.
Credential and storage control
Keep secrets, workspaces, shared storage, and logs within governed infrastructure and operator procedures.
Model hosting boundary
Bewize governs agents and the control plane; private or self-hosted LLM choices are handled in deployment architecture.
Operational outcome
Platform, IT, security, and engineering teams can adopt AI agents without giving up deployment ownership.
Related operating controls
Use these pages to evaluate the deployment, security, and platform layers behind private agent infrastructure.
Enterprise deployment
Host layout, tenant users, releases, storage, secrets, schedules, adapters, logs, and access state.
Review control →Security and governance
Tenant isolation, managed secrets, policy APIs, access controls, redacted metrics, and browser boundaries.
Review control →Platform architecture
Roomcord, Bewize Hub, and Wize Browser connected by one managed agent identity across controlled work surfaces.
Review control →On-premise architecture
Map the customer-controlled Linux host, tenant services, state, storage, gateway, and hosted-model boundary.
Review control →Deployment quality checklist
Verify ownership, isolation, networking, secrets, operations, updates, backup, and recovery before a pilot.
Review control →Platform comparison
Compare vendors by operational evidence rather than broad on-premise labels.
Review control →On-premise AI agent questions
Short answers for enterprise teams comparing private agent infrastructure options.
Does Bewize require the company to host its own LLM?
No. Bewize is positioned around the managed agent runtime and control plane. The model hosting choice, including private or self-hosted LLMs, is a deployment architecture decision.
What stays under enterprise control?
The deployment model can keep the agent runtime boundary, tenant state, credentials, storage, logs, schedules, adapters, and access state under enterprise operations ownership. See the deployment model for the broader operations surface.
Is this a security certification or compliance guarantee?
No. Bewize names concrete controls such as tenant isolation, managed secrets, policy APIs, and redacted metrics, but does not offer unsupported certifications or blanket security guarantees. See security and governance for the control boundary.
Plan a private agent deployment
Talk with Bewize about the host model, tenant isolation, secrets, storage, logging, model boundary, and rollout path for on-premise AI agents.
Architecture conversation
Tell us what your team needs to control
Share your deployment boundary, number of agents, work surfaces, and governance requirements. We will reply by email to arrange a focused technical discussion.
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