A new arXiv preprint, Runtime Control for AI Agents: Closing the Evaluation–Execution Gap in Operational Workflows, highlights a growing risk as language-model agents take part in workflows that create real-world commitments. The abstract notes that these agents increasingly handle approvals, binds, and payments, where timing and context matter.
The core problem is that an approval can become invalid after it is issued. If relevant facts or evidence change between evaluation and execution, the original authorization may no longer justify the action. The paper argues for runtime control mechanisms to close this gap, rather than relying only on pre-execution evaluation.
Because the abstract is truncated, the specific control methods are not detailed in the available text. Still, the work points to a practical concern: AI agents operating in dynamic environments need safeguards that keep decisions aligned with current conditions.