A k nearest neighbors (kNN) query finds k closest points-of-interest (POIs) from an agent's location. In this paper, we study a natural extension of the kNN query for multiple agents, namely, the Aggregate k Nearest Neighbors (AkNN) query. An AkNN query retrieves k POIs with the smallest aggregate distances where the aggregate distance of a POI is obtained by aggregating its distances from the multiple agents (e.g., sum of its distances from each agent). We propose a novel data structure COLT (Compacted Object-Landmark Tree) which enables efficient hierarchical graph traversal and utilize it to efficiently answer AkNN queries. Our experiments on real-world and synthetic data sets show that our techniques outperform existing approaches by more than an order of magnitude in almost all settings.