We propose to use Artificial Intelligence techniques to monitor and control complex processing chains of software components. We consider software systems that run in an evolving environment and thus may require adaptation at run time. Our approach relies on knowledge representations of both structural and dynamic aspects of components and processing chains. The paper concentrates more precisely on run time adaptation to cope with context changes. Run time policies are expressed by means of inference rules. At run time an inference engine uses these rules to orchestrate the component chain, in particular to achieve run time adaptations, such as component parameter tuning or re-assembly of the processing chain. We describe the general evaluation-repair mechanism which involves to evaluate environment changes and execution results and then to trigger the suitable reconfigurations.