With the rapid development of edge computing technology, achieving efficient resource scheduling in environments with limited and dynamically changing resources has become a major challenge. This paper proposes an automated resource scheduling algorithm based on a lightweight Agent to optimize resource allocation in edge computing environments. Experimental results demonstrate that the algorithm has significant advantages in high-load scenarios. Compared with the traditional priority scheduling algorithm, the response time is reduced by about 40%, resource utilization is improved by 5%, and the task completion rate is improved by 3%. By introducing a lightweight Agent, the system enables real-time monitoring and adaptive scheduling of edge nodes with low resource consumption. The experiment verifies the effectiveness of the algorithm in edge computing environments and proves its superiority in dynamic resource management and task scheduling. Additionally, this paper discusses potential future optimization directions, such as combining deep learning technology to enhance the intelligence of the scheduling algorithm and its potential for broader application scenarios.