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A discussion on how teams handle AI agents making real commitments without human approval, seeking exceptions and insights on liability and legal friction.
A discussion thread seeking input on how to handle authority and permissions for AI agents that take real actions, including audit trails and scope of permissions.
This paper introduces a runtime execution model for autonomous agents that enforces 'Reconstructive Authority'—actions are only permitted if authority can be constructed from current state. It includes dynamic dependency resolution, a halt state for uncertainty, and a recovery loop integrating drift detection.
This article argues that the AI safety debate is misdirected, focusing on model alignment and internal controls instead of the critical boundary: external admission authority over agent execution. It warns that systems capable of self-authorizing high-impact actions (e.g., deploying code, moving money) pose a fundamental risk that logging and monitoring cannot mitigate.