Secure cloud runtime for AI agent deployments
A containerized, air-gapped execution environment and approval gateway that lets teams run AI agents in the cloud with strong isolation, audit trails, and resource controls—replacing desktop execution and ad-hoc cloud scripts.
The problem
Teams building AI agents face a critical tension: running agents on personal desktops or development machines introduces unacceptable security risk (credential leakage, lateral movement, data exfiltration), but deploying agents to shared cloud infrastructure without isolation, approval gates, or execution boundaries creates compliance, cost, and operational chaos. The result is either agents that never reach production, or agents deployed with weak controls that breach security or run up unexpected cloud bills.
Who has it: Mid-market AI teams (20–200 engineers) at financial services, healthcare, govcon, and software companies building autonomous agent applications requiring audit, isolation, and cost control.
Why now: AI agents are moving from research/hobby to enterprise workflows; teams need production-grade isolation and governance, not just Docker. Cloud-native agent frameworks (LangChain, Crew, ReAct) are becoming standard, but lack built-in security and cost control. Regulatory pressure (SOC 2, HIPAA, FedRAMP for govcon) is forcing compliance-first deployments.
Where this came from
2 public sources behind this idea.
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