Our work focuses on Extreme Sensitive Robotic that is on multi-robot applications that are in strong interaction with humans and their integration in a highly connected world. Because human-robots interactions have to be as natural as possible, we propose an approach where robots Learn from Demonstrations, memorize contexts of learning and self-organize their parts to adapt themselves to new contexts. To deal with Extreme Sensitive Robotic, we propose to use both an Adaptive Multi-Agent System (AMAS) approach and a Context-Learning pattern in order to build a multi-agent system ALEX (Adaptive Learner by Experiments) for contextual learning from demonstrations.