AI Agent Execution Sandbox

A safety framework that implements fail-closed execution gates for AI agents, preventing unintended actions before they happen. Targets teams deploying autonomous AI systems who need guardrails and sandboxing to control agent behavior.

Autonomous agent deployments are accelerating sharply in 2024-2025 as teams move from LLM demos to production systems that actually take actions — browse the web, write files, call APIs — and the failure modes are becoming painfully visible. The closest competitor is E2B, which offers sandboxed code execution, though it focuses more on code running than generalized action gating across agent behavior. The $5k–20k MRR band is realistic for a developer infrastructure tool sold to engineering teams, but it implies a fairly small number of mid-market contracts rather than a self-serve volume play, which requires a real enterprise sales motion from day one. The biggest risk is that the major agent frameworks — LangGraph, AutoGen, CrewAI — build native policy and guardrail layers themselves, commoditizing the core value before a standalone vendor can establish switching costs.