Object detection is a pivotal task in computer vision, with applications spanning from autonomous driving to surveillance. Traditionally, methods like Non-Maximum Suppression (NMS) and its variants have been used to refine object detection outputs. Fusing predictions from different object detection models using confidence scores to average overlapping bounding boxes from multiple detection models has demonstrated superior performance over conventional methods. In this work, we employ multiple agents, each responsible for handling individual bounding boxes, to generate an improved fused prediction. This agent-based adaptation aims to leverage decentralized processing to potentially increase the system's efficiency and adaptability across various object detection scenarios, particularly in autonomous vehicle (AV) perception systems. We develop two distinct behaviors for the bounding box agents: one replicating the state-of-the-art Weighted Boxes Fusion (WBF) method in a decentralized manner, and the other introducing competitive behavior where agents interact based on Intersection over Union (IoU) and confidence values. We evaluate the performance of our approach using the COCO dataset, demonstrating the flexibility and potential of integrating MAS into object detection workflows including those for AV perception systems.