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
On-premise AI agents under enterprise operations control
On-premise AI agents in Bewize are managed tenant runtimes operated inside infrastructure the enterprise controls. Teams keep ownership of the agent runtime boundary, credentials, storage, logs, and network placement while model hosting remains a separate architecture decision.
Answer
What are on-premise AI agents in Bewize?
They are company-controlled agent runtimes for private cloud or data-center operation. Bewize focuses on the control plane and runtime layer: tenant identity, lifecycle, policy, secrets, workspace, storage, schedules, adapters, logs, and access state. It does not promise a certification, absolute security outcome, or bundled private LLM server.
Enterprise boundary
Control plane, tenant runtime, secrets, workspace, logs, and adapters stay under operations ownership.
Private host
Tenant runtimeManaged secretsWorkspaceShared storageLogsAdapters
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 controlSecurity and governance
Tenant isolation, managed secrets, policy APIs, access controls, redacted metrics, and browser boundaries.
Review controlPlatform architecture
WorkCord, Hermes Hub, and Wize Browser connected by one managed agent identity across controlled work surfaces.
Review controlOn-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. Public Bewize copy names concrete controls such as tenant isolation, managed secrets, policy APIs, and redacted metrics, but avoids 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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