A new technique for Retrieval-Augmented Generation streamlines AI performance by pruning agent context down to only the essential information needed to formulate an answer. This approach significantly reduces the volume of data processed by the model. Remarkably, it achieves this efficiency while preserving 96% of the original recall accuracy.