RACER: Remediation Assurance Contracts for Evidence-Gated, Risk-Bounded Autonomous Recovery in Cloud Systems

AI agents can diagnose cloud incidents, synthesize operational commands, and invoke state-changing APIs, but a plausible remediation is not necessarily safe to execute. This study presents RACER, a runtime-assurance mechanism that treats every AI-generated repair as an untrusted proposal until it is bound to a machine-checkable Remediation Assurance Contract. The contract specifies preconditions, protected invariants, exact resource scope, a risk and blast-radius budget, evidence requirements, verification predicates, rollback obligations, and a validity interval. RACER combines these contracts with a risk-bounded authorization policy that selects denial, human escalation, staged canary execution, or direct bounded execution while separating the proposing model from authorization, actuation, and post-action verification. Conditional properties are derived for policy confinement, declared blast-radius enforcement, and bounded violation duration under explicit mediation and rollback assumptions. A reproducible Monte Carlo decision study evaluates 1,000,000 simulated incident-remediation pairs across 20 independent seeds. Under the stated synthetic model, unsafe global actuation is 20.14% for ungated execution, 5.48% for fixed canary execution, and 1.33% for RACER; RACER recovers 82.21% of simulated incidents while autonomously handling 80.57%. Ablations attribute the modeled improvement to risk-based escalation, staged execution, and independent verification. The results are mechanism-level simulation evidence, not production-cloud evidence, and a Kubernetes/AIOpsLab validation protocol is specified for deployment-grade testing.