SafeCommit Certifies Agent Actions to Prevent Premature Commitments.

Long-horizon AI agents that use persistent memory and tools to interact with the external world often suffer from premature commitment. This occurs when an agent acts before confirming that its memory grounding is accurate, complete, or consistent, leading to potentially unsafe side effects. To address this critical failure mode, the SafeCommit framework has been developed. SafeCommit functions as a risk-controlled layer positioned between an agent's reasoning process and its external execution. It operates by constructing a calibrated set of plausible "latent worlds" based on the agent's memory, observations, tool outputs, and policy constraints. An action is only permitted if a "conformal action certificate" confirms its safety across all retained plausible worlds. If an action cannot be certified as safe, SafeCommit either selects a low-side-effect probe to gather more information targeting the uncertainties, or it defaults to a conservative fallback action. This approach, demonstrated with a controlled simulator, allows for a clear safety-utility tradeoff, ensuring that the probability of an unsafe certified commit remains below a target level, even with imperfect world proposals.