Show HN: Runeward: Sandboxing AI agents with policy gates (runewardd.github.io)

Runeward, a newly unveiled tool, provides a framework for sandboxing AI agents while emphasizing governance over mere isolation. It establishes controlled environments—using Docker or Kubernetes—that are designed with a deny-by-default approach to network access and a comprehensive audit ledger. This governance layer prevents potentially harmful actions from AI agents, such as deleting critical files or incurring excessive costs through repeated errors. Runeward also features human-in-the-loop approval processes, so operators can review and approve actions at critical junctures. The significance of Runeward lies in its structured approach to AI governance, addressing frequent shortcomings in conventional agent sandboxes, which often lack comprehensive security measures and oversight. With functionalities like tamper-evident audit trails, cost and execution limits, and flexible multi-user control, Runeward offers a robust solution for organizations aiming to deploy AI safely and efficiently. By facilitating secure, governed execution of AI tasks, Runeward not only aids in mitigating risks but also enhances accountability—critical elements as AI technologies continue to evolve and integrate into various applications.