Trust Is Not a Score: Runtime Assurance Contracts for High-Risk AI Agents

New research introduces the concept of a Runtime Assurance Contract (RAC) to bridge the 'assurance-transition gap' in high-risk AI agents. This gap refers to the missing framework for how observed evidence should dynamically alter an AI agent's operational authority during critical tasks. RACs propose a formal policy-level schema that defines autonomy boundaries, component eligibility, evidence states, transition policies, human review capacities, and non-compensatory gates, ensuring that aggregate performance does not unilaterally authorize action.

The assurance-transition gap presents a critical challenge for the safe and responsible deployment of AI in high-stakes environments. Addressing this gap through formal contracts like RACs is vital for managing AI risk, building public trust, and ensuring regulatory compliance. It enables organizations to confidently integrate advanced AI systems into operations where failures could have severe consequences.

A significant 'assurance-transition gap' exists, where current benchmarks, audits, and protocols fail to define how real-time evidence should modify an AI agent's authority in consequential tasks.

Forward consideration, not a verified fact.