The development of computational sciences has fostered major advances in life sciences, but also led to reproducibility and reliability issues, which become a crucial stake when simulations are aimed at assessing control measures, as in epidemiology. A broad use of software development methods is a useful remediation to reduce those problems, but preventive approaches, targeting not only implementation but also model design, are essential to sustainable enhancements. Among them, AI techniques, based on the separation between declarative and procedural concerns, and on knowledge engineering, offer promising solutions. Especially, multilevel multi-agent systems, deeply rooted in that culture, provide a generic way to integrate several epidemiological modeling paradigms within a homogeneous interface. We explain in this paper how this approach is used for building more generic, reliable and sustainable simulations, illustrated by real-case applications in cattle epidemiology.