Automatic differentiation (AD) has proven its interest in many fields of applied mathematics, but it is still not widely used. Furthermore, existing numerical methods have been developed under the hypotheses that computing program derivatives is not affordable for real size problems. Exact derivatives have therefore been avoided, or replaced by approximations computed by divided differences. The hypotheses is no longer true due to the maturity of AD added to the quick evolution of machine capacity. This encourages the development of new numerical methods that freely make use of program derivatives, and will require the definition and development of new AD strategies. AD tools must be extended to produce these new derivative programs, in such a modular way that the different sub-problems can be solved independently from one another. Flexibility assures the user to be able to generate whatever specific derivative program he needs, with at the same time the possibility to generate standard ones. This paper sketches a new model of modular, extensible and flexible AD tool that will increase tenfold the DA potential for applied mathematics. In this model, the AD tool consists of an AD kernel named KAD supported by a general program transformation platform.