Authors - Romildo Silva, Maria Tavares, Filipa Silva, Carlos Lopes Abstract - This paper investigates the use of AI agents as consumers of tokenized real-world asset (RWA) data in financial environments. A Python-based agent was developed to automatically retrieve, process, and analyze financial information from publicly accessible APIs for selected traditional and tokenized assets, including SPY, QQQ, PAXG, and ONDO. The proposed framework evaluates data quality through quantitative metrics such as latency, completeness, null rate, and data volume, complemented by descriptive statistical analysis and Shapiro-Wilk normality testing. The results indicate that traditional financial assets exhibit higher informational stability, while tokenized assets present greater variability and non-normal behavior. The study demonstrates that autonomous AI systems can effectively consume heterogeneous financial data sources and highlights the growing importance of information quality and consistency in AIdriven financial ecosystems.
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