Concurrency Anomalies Introduced by AI-Mediated Transaction Routing Layers

Abstract

The integration of AI-mediated routing layers into transactional systems has introduced a new class of concurrency challenges that extend beyond traditional database behavior. While these intelligent routing mechanisms improve system responsiveness and load distribution by dynamically directing transactions, they also disrupt deterministic execution ordering, leading to increased contention and anomaly occurrence. Existing research largely emphasizes performance gains from AI-driven optimization, leaving a gap in understanding its impact on transactional correctness and consistency guarantees. This study addresses that gap by systematically analyzing how adaptive routing influences transaction ordering, conflict rates, and anomaly propagation under varying system loads. A simulation-based framework is developed to compare AI-mediated routing with conventional static routing, revealing that conflict rates escalate nonlinearly under high-load conditions due to loss of temporal locality and increased overlap in transaction execution. The results demonstrate a clear trade-off between performance optimization and concurrency stability, emphasizing the need for integrating concurrency-awareness into AI routing strategies. The study concludes that future transactional architectures must co-design routing intelligence and consistency mechanisms to ensure both scalability and correctness in distributed environments.