Run RL rollouts at scale
Enable your training loop to launch thousands of isolated sandboxes in parallel, each a fresh microVM with its own kernel. Sandboxes start from a template in about 90 ms, so rollout throughput is bound by your model, not your infrastructure.
Build agent environments
Automatically provision realistic web and desktop environments for agent training and evaluation. Every episode gets a real Chrome session or a full Linux desktop with a live screen, restored from the same snapshot so each run starts from identical state.
Reset and fork from any state
Enable your agents to snapshot a sandbox or desktop at any step and fork it into independent workers for retries, branching rollouts and counterfactual runs. Environments restore from memory snapshots in under a second, so resets cost almost nothing.
Evaluate agents reproducibly
Automatically run your benchmark suites against the same prepared environment every time. Templates and persistent volumes pin the image, files and tooling, and browser runs replay click by click, so scores stay comparable across checkpoints.
Execute model-generated code safely
Enable your agents to run untrusted, model-generated code and test suites inside hardware-isolated microVMs. Every run gets a dedicated kernel and is destroyed on completion, so a runaway or malicious program never touches your infrastructure or another run.
Collect and verify trajectories
Automatically gather browsing trajectories from the real web and verify outcomes at scale. Stealth browsers bypass bot detection and record every session, while sandboxes grade the results, so training data scales without a fleet to maintain.
