Kernel-Level Isolation for AI Agents
Give each AI agent its own Linux kernel with full GPU access and strong sandboxing. No virtualization layer between the agent and hardware. No nested virtualization required. Works inside any standard cloud VM.
The Problem
AI agents need isolation, GPU access, and fast lifecycle management. Today's options force a tradeoff.
Containers
Fast to start but share a kernel. A rogue agent can escape. GPU passthrough works but isolation is weak. Not sufficient for untrusted code execution.
Virtual Machines
Strong isolation but heavyweight. GPU passthrough requires SR-IOV or vGPU. Nested virtualization in cloud VMs adds significant performance overhead.
Multikernel Enclaves
Each agent gets its own kernel with direct GPU access. Kernel-level isolation without virtualization. Native performance inside any cloud VM.
Built for AI Workloads
Full GPU Access
No virtualization layer between the agent and GPU hardware. Direct access to CUDA, ROCm, and other GPU frameworks at native performance.
Strong Sandboxing
Each agent runs in its own kernel. A compromised agent cannot access other agents' memory, devices, or kernel state.
Fast Checkpoint/Restore
Lightweight kernel state enables rapid snapshots. Save, restore, and clone agent environments in milliseconds.
No Nested Virtualization
Runs inside any standard cloud VM on AWS, GCP, or Azure. No special instance types. No hypervisor overhead.
Shared Model Weights
DAXFS enables zero-copy sharing of model weights across agent enclaves. One copy in memory serves all agents.
Docker Compatible
Deploy agents using your existing Docker images and workflows. No new packaging format, no new APIs.