Content analysis of digital trade agreements is essential for translating legal text into economic variables, enabling quantitative analysis of cross-border digital trade cooperation and global digital governance. Existing approaches rely heavily on human labeling, which is costly, difficult to scale, and dependent on researchers’ implicit judgments, making the process opaque and errors hard to trace. This paper proposes a multi-agent framework to automate content analysis by aligning with the human analysis process: evidence identification, legal interpretation, and structured mapping. We evaluate the framework using an expert-coded dataset of 467 agreements and an adjudicated subset that includes labels as well as the full reasoning process. Compared to large language model classification, the multi-agent approach achieves higher accuracy and greater robustness in complex tasks, while also improving interpretability. Our results show that multi-agent systems can make content analysis of complex legal and institutional texts more reliable and scalable.