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 with a clear control boundary
Bewize deploys managed AI agent runtimes on customer-controlled Linux infrastructure. Enterprise teams keep control of tenant boundaries, credentials, workspaces, storage, logs, human approval, 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.
Evaluation criteria
How to compare on-premise AI agent platforms
An on-premise label is not enough. Ask every vendor to show the supported deployment boundary, the controls used during execution, and the evidence available after a run. Bewize separates shipped platform controls from environment-specific validation and unsupported assumptions.
Deployment boundary
Identify where the agent runtime, control plane, state, storage, logs, browser sessions, and models run. Record every required external service and network path.
Permissions and approvals
Define which tools, integrations, and data each agent may use, which actions require human approval, who owns the decision, and how access is revoked.
Run history and audit trail
Require a reconstructable record of the context used, tool calls, applicable rule, approval, resulting action, and accountable owner. A generated explanation is not an audit trail.
Observability and evaluation
Separate execution evidence from outcome quality. Logs and traces show what happened; evaluations determine whether the result met the acceptance criteria.
Production reliability
Check scheduling, health signals, retries, exception routing, versioned updates, backup choices, recovery procedures, and the proof used to verify the final state.
Claim and model boundary
Confirm what is shipped, what must be validated in the target environment, and what is unsupported. On-premise agent infrastructure does not automatically include a privately hosted LLM.
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.
Can people approve an agent action before it runs?
Yes. Approval boundaries can be defined for consequential actions, with a responsible person reviewing the relevant context before execution. The exact approval path and supported integration must be validated for the target workflow.
What should an AI agent run history contain?
A useful run record should let an operator reconstruct the request, relevant context, tool calls, rule in force, human intervention, resulting action, final state, and unresolved exception. Logs alone show events; they do not automatically prove that the business outcome was correct.
How are enterprise integrations handled?
Integrations should be mapped one by one. Record the external system, data and credential boundary, permitted actions, required approval, retained evidence, and safe failure path. Bewize does not treat an integration name as proof that every action is supported in every environment.
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.
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