Determining the data provenance, that is, the process that led to those data, is vital in many areas, especially when it is essential that the results or actions be reliable. With the increasing number of applications based on artificial intelligence, the need has been created to make them capable of explaining their behavior and be responsive to their decisions. This is a challenge especially if the applications are distributed, and composed of multiple autonomous agents, forming a Multiagent System (MAS). A key way of making such systems explicable is to track the agent s behavior, that is, to record the source of their actions and reasoning, as in an omniscient debugging. Although the idea of provenance has already been explored in some contexts, it has not been extensively explored in the context of MAS, leaving many questions to be understood and addressed. Our objective in this work is to justify the importance of the data provenance to MAS, discussing which questions can be answered regarding the behavior of MAS using the provenance and illustrating, through application scenarios, to demonstrate the benefits that provenance provides to reply to these questions. This study involves the creation of a software framework, called FProvW3C, which supports the collects and stores the provenance of the data produced by the MAS, which was integrated with the platform BDI4JADE (41), forming what we call Prov-BDI4JADE. Through this platform, using examples of autonomous systems, we have rigorously demonstrated that the use of data provenance in MAS is a solid solution to make the agent’s reasoning and action process transparent.