Who is the most suitable one? Compliance review method based on multi-agent routing

Project compliance review serves as a critical component in ensuring that project submissions meet regulatory and procedural standards while filtering out non-compliant proposals. However, the rapid increase in project applications has rendered traditional manual review mechanisms inefficient and unsustainable. Existing automated approaches—based on either semantic vector matching or direct use of large language models (LLMs)—often struggle with heterogeneous document structures, limited robustness, and high computational cost. To overcome these challenges, this study proposes an automated compliance review framework based on multi-agent routing . The framework integrates multiple expert agents with distinct reasoning paradigms and employs a classification agent to dynamically route each task to the most suitable expert, thereby enabling data-adaptive decision-making. Through adaptive routing, the system effectively reduces redundant computation by invoking complex reasoning only when necessary. Experimental results on real-world datasets from the power industry demonstrate that our method achieves superior accuracy and 48% reduction in token consumption compared to the best-performing single-agent baseline, achieving a balanced trade-off between effectiveness and efficiency. Furthermore, an online review platform has been developed and successfully deployed in large-scale power project evaluation scenarios, validating the practicality and scalability of the proposed approach.