Keep the power of AI automation, with stronger infrastructure control.
SVCE is the execution environment that keeps the Clevi-X platform’s AI agents operating safely by isolating, governing, and validating them at the infrastructure layer. Who did what, and when, is all recorded automatically.
Built for environments like yours
SVCE fits organisations that must meet regulatory requirements, operate complex integrated systems, or maintain direct control of their infrastructure.
Financial services · Public sector
Environments where regulation and audit are mandatory
Enterprise IT
Unified operation of complex systems
On-premises
Direct control of your own infrastructure
Cloud native
Containerised workload operations
How SVCE works within the Clevi-X platform
SVCE is not a standalone solution. It is the control layer between your infrastructure and the Clevi-X platform that governs every agent execution.
Customer infrastructure
On-premises · Private cloud · Multicloud
↓
SVCE · Infrastructure control layer
Isolation · Access · Audit · Execution control
↓
Clevi-X platform
AI agents · Business automation workflows
How does SVCE compare with conventional approaches?
Running agents directly on a server is lightweight but risky; virtual machines are secure but resource-intensive. SVCE closes the gap with lightweight containers.
Comparison
Server-side agent
VM-based
SVCE
Isolation
Low
High
High (lightweight)
Access control
Implemented in application code
Requires configuration
Built into the runtime
Per-task isolation
Difficult
Inefficient
Automatic per container
Provisioning · teardown speed
Fast
Slow
Fast
Cost efficiency
High
Low
High
Audit logs
Custom build required
Custom build required
Built-in append-only logs
AI agent boundaries
Custom design required
Custom design required
Standard policies included
The risks of running agents directly inside a server
Embedding an agent directly in a server may be quick to build, but isolation is weak and control depends on application code.
One failure can spread across the service
When agents run as internal server logic, an error, malicious code, or external attack in one task can propagate across the entire service.
Risk — Potential service-wide outage
Access control depends on application code
When permissions live in application code, it is difficult to enforce consistent control over sensitive data and API access.
Risk — Limited control over sensitive data and APIs
Results are difficult to verify
Without a fixed execution environment, the same task may run differently, making reproduction, auditing, and result verification difficult.
Risk — No reliable reproduction, audit, or verification
The four foundations of SVCE
Treat agents not as simple response systems, but as production-grade workers with security boundaries and explicit execution privileges.
Isolation
Container-based isolation prevents malicious code, abnormal commands, and external attacks from spreading beyond an AI agent's execution space. The container is destroyed after each task to reduce residual-data risk.
Keep every execution inside a secure boundary
Control
Agents never choose their own permissions. The Command Mediator and Orchestrator grant and restrict access per task, applying different limits to files, networks, APIs, system commands, and secrets for roles such as Research, Code, and Executor.
Control what agents can and cannot do at the infrastructure layer
Efficiency
Lightweight containers start faster than VMs and are created only when work is ready. CPU, memory, I/O, and GPU resources are limited and allocated per task to balance cost and performance.
Use only the resources each task needs
Scale
Run many users, tasks, and agents at once. Containers scale automatically as requests increase, failures remain isolated to individual tasks, and agents can be separated by role.
Scale reliably across many users and agents
Requests are never executed immediately. Every request goes through the Command Mediator gate, where policy, permission, and approval checks must pass before execution in an isolated container.
User / Workflow
AI or operator submits a task
→
Command Mediator
Check policy, permissions, and approvals before execution
→
Isolated agent container
Run work as an independent execution unit
→
Tools · API · Data
Access only what the policy allows
↑ Every request passes through
Policy validation
Permission check
Command transformation
Approval flow
Orchestrator determines the container, permissions, network policy, CPU and memory allocation, and maximum execution time for each task.
Audit log · Example live operations record
2026-05-14 02:31:04Requestresearch-agent → Request access to the production database for payment-log analysis
2026-05-14 02:31:05Deniedcommand-mediator → Policy violation · Direct production DB access blocked · Approval required: req_8c4a
2026-05-14 02:33:18Approved@kim.ops → approve(req_8c4a) · Restricted to read-only access
