Efficient and Sound Probabilistic Verification for AI Agents

AI agents operating in enterprise environments — browsing the web, calling APIs, reading files — must be constrained by security policies. Prior work on policy enforcement assumed those policies were deterministic, but real tools like PII detectors or content classifiers have inherent failure probabilities. This paper introduces a framework grounded in distributionally robust optimization that provides provable upper bounds on the probability of a policy violation, even when component failures are correlated in unknown ways. Applications include compliance-critical deployments in finance, healthcare, and legal services, where regulators increasingly expect quantifiable security guarantees rather than best-effort defenses.