Business Automation
Sandboxed LLM code execution
LLM-driven Python execution inside isolated containers — the engine behind internal agent workflows that need to read data, run code, and produce audit-ready output.
Python Docker gVisor LLM agents
The problem. Giving an LLM a Python REPL against production data is terrifying. Giving it nothing is useless. You need something in between.
What we build. A sandboxing layer where every agent run lives in its own short-lived container with scoped filesystem, network, and resource limits. Each run is fully replayable from its transcript.
Scope of past engagements
- Sub-second cold-start for agent containers
- Per-run quotas on CPU, RAM, network egress, and wall-clock time
- Full input/output capture for compliance-grade audit trails
This engagement is described in generic terms. Specific clients, metrics, and internal system names are intentionally withheld.