DualLane: Fast and Reliable LLM Agents for Interactive AIOps via Dual-Path Planning

Abstract

Ticket support in cloud services involves technical engineers using expert tools to resolve customer queries—% a process closely resembling information integration. To enhance both operational efficiency and resolution accuracy, we propose DualLane, a novel parallel dual-path planning architecture designed for AI agents. This framework adaptively manages the highly skewed frequency distribution characteristic of real-world user queries. For low-frequency long-tail scenarios, the slow path employs a two-stage planning mechanism that decouples task decomposition from parameter generation, effectively optimizing dependency propagation and reducing context complexity. In contrast, for high-frequency routine scenarios, the fast path bypasses expensive LLM-based full-plan generation by utilizing pre-validated execution templates, thereby improving response accuracy and reducing latency. After more than one year of extensive deployment in Alibaba Cloud's ECS production environment, DualLane has demonstrated remarkable robustness and stability. Offline benchmarks indicate a high accuracy rate of 96.5%, accompanied by superior latency performance. Crucially, online metrics reveal a median plan-execution latency of merely 4.2 seconds, with an agent-induced error rate maintained at a low 7.1%. These results underscore the practical viability and effectiveness of adaptive dual-path planning in large-scale, interactive AIOps systems.