The big data era brought us new data processing and data management challenges to face. Existing state-of-the-art analytics tools come now close to handle ongoing challenges and provide satisfactory results with reasonable cost. But the speed at which new data is generated and the need to manage changes in data both for content and structure lead to new rising challenges. This is especially true in the context of complex systems with strong dynamics, as in for instance large scale ambient systems. One existing technology that has been shown as particularly relevant for modeling, simulating and solving problems in complex systems are Multi-Agent Systems. This article aims at exploring and describing how such a technology can be applied to big data in the form of an Adaptive Multi-Agent System providing dynamic analytics capabilities. This ongoing research has promising outcomes but will need to be discussed and validated. It is currently being applied in the neOCampus project, the ambient campus of the University of Toulouse III.