Difficulty-Routed Control Improves AI Customer Service Reliability

As autonomous AI agents increasingly handle operational customer service tasks like refunds and order modifications, ensuring accuracy and preventing errors becomes critical. This paper introduces a "difficulty-routed service-control architecture" designed to optimize reliability in these operations. The core idea is to differentiate between routine service requests and those with operational complexity or potential conflicts. A lightweight router directs routine sessions along a low-cost baseline path, while "operationally coupled" sessions are routed to an escalated workflow. This escalated path employs conflict-aware communication and "write-triggered reconsideration," concentrating deliberation and safeguards specifically before consequential backend writes. This avoids applying uniform, costly control to all service interactions. Evaluated on human-verified retail and airline tasks from the $\tau^{2}$-bench, the method consistently improved reliability for service requests involving operational conflict in retail. Routing evidence confirmed that stronger control was applied precisely to conflicted requests. Analysis of dialogue and tool-use profiles showed gains stemmed from evidence gathering, write separation, and pre-write reconsideration, rather than indiscriminate interaction expansion. The escalated workflow effectively preserved fallback plans, bound records to correct actions, sequenced writes, and decomposed multi-entity requests, with similar logic extending to airline reservation operations.