Isolated Execution Environments for Running Tools
Keep the agent harness outside the sandbox and create isolated sandboxes only when a tool needs risky or heavy execution. This is the pattern for code interpreters, browser helpers, and tool-calling agents that should not run untrusted code inside the harness itself.
Infrastructure for RL Environments
Snapshot a prepared environment and reuse it across workers.
Keep files, packages, processes, and seeds consistent across runs.
Snapshot a prepared environment and reuse it across workers.
Choose the CPU, memory, GPU, and image each environment needs.
Built for workloads that hit disks
Tensorlake is the Agentic Compute Runtime the durable serverless platform that runs Agents at scale.
Bring Tensorlake Into Your Cloud
Run sandboxes and applications in your own cloud or private environment when you need lower egress, stricter network boundaries, dedicated capacity, or more predictable performance.
Keep code and data inside your preferred cloud boundary when shared SaaS deployment is no longer acceptable.
Keep compute closer to your data and tighten the runtime behavior for latency-sensitive agent workloads.
Move from usage-based hosted infrastructure to capacity you can plan, reserve, and operate more predictably.
Full traces of every function and tool call — with logs, timing, and structured execution paths.
Tool calls run in isolated sandboxes, making them safe for LLM-generated code.
Each agent harness executes inside an isolated sandbox to keep sessions safe and independent.
Secure by default for PHI, PII, and sensitive documents.
Each project’s data lives in its own isolated bucket with full audit trails and strong RBAC controls.
