SVCE is the execution environment that isolates, controls, and verifies at the infrastructure level so AI agents on the Clevi-X platform run safely. Who did what, and when — everything is logged automatically.
Fits environments that must meet regulation, operate integrated complex systems, or directly control their own infrastructure.
Environments where regulation and audit response are mandatory
Integrated operation of complex systems
Direct control over own infrastructure
Container workload operations
SVCE is not a standalone solution. It's the layer that controls every agent execution between customer infrastructure and the Clevi-X platform.
Running agents directly inside the server is lightweight but risky. VMs are safe but heavy. SVCE fills the gap with lightweight containers.
| Comparison | On-PC agent | VM-based | SVCE |
|---|---|---|---|
| Isolation level | Low | High | High (lightweight) |
| Permission control | In-code implementation | Requires configuration | Built-in at runtime level |
| Audit log | Build it yourself | Build it yourself | Append-only built in |
| Per-task isolation | Hard | Inefficient | Automatic per instance |
| Create · dispose speed | N/A | Slow | Fast |
| Inter-workload networking | N/A | Build it yourself | Auto node-to-node networking |
| Service autoscale | Not possible | Custom design required | Automatic horizontal scaling |
| AI agent boundary | None | Custom design required | Isolated |
Local agents ship fast, but because they run close to your files, browser, and credentials, isolation and control are hard to enforce.
When an agent runs as in-server logic, a single task's error, malicious code, or external attack can propagate to the entire service.
Risk → Potential full-service outage
When an agent runs with the user's PC permissions, it can reach local files, browser sessions, API keys, and credentials.
Risk → Limits on sensitive-data and API access control
Without a fixed execution environment, the same task can run differently — making reproduction, audit, and result verification difficult.
Risk → No reproducibility, audit, or verificationAgents are treated not as a mere response system but as first-class work principals with security boundaries and execution permissions.
AI agent execution space is isolated in containers, preventing malicious code, anomalous commands, and external attacks from spreading across the system. When a task ends, the container is disposed of — reducing the risk of residual data.
Execution is bound entirely within a tight boundary
Agents don't decide their own permissions. The Command Mediator and Orchestrator grant and restrict permissions per task — so Research, Code, or Executor roles get different scopes for files, network, APIs, system commands, and Secrets.
What can and cannot be done is controlled at the infrastructure layer
Lightweight container-based — faster than VMs, and containers are only created when work needs them. CPU, memory, I/O, and GPU resources are limited and allocated per task, hitting cost and performance at the same time.
Resources used only as much as needed
Many users, tasks, and agents run simultaneously. Containers scale up automatically as requests grow, and failures stay isolated per task. Agents can be operated separately by role.
Scales reliably across many users and many agentsRequests don't execute immediately. Every request first passes the Command Mediator gate for policy, permission, and approval — then runs inside an isolated container.
Orchestrator decides in which container, under which permission and network policies, with how much CPU/memory, and for how long the task runs.
Reach out anytime you're curious about adoption.
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