Nowadays, with the widespread use of CCTV cameras everywhere, traditional video retrieval systems face significant challenges in managing and retrieving video content from a huge number of CCTV cameras. These systems often have difficulty understanding the semantics of content recorded from cameras and have problems processing large volumes of CCTV video data in real-time. In particular, accurately identifying specific events, objects, or actions is usually time-consuming and may sometimes lead to missing important information. To address these issues, in this paper, we propose an AI-agent-powered video object contextualization and retrieval system that leverages the capabilities of distributed AI agents and edge computing. Our proposed architecture deploys video object contextualization machines on edge devices to perform real-time object detection and classification and then transfer the data to a central server through text-based content. This process extracts rich, semantically meaningful metadata directly from CCTV cameras, bringing frame content into contextual information that we can search using human natural language with the support of NLP agents. By embedding the concept of video object contextualization at the edge, the system significantly reduces data transmission to the central server, enhances processing efficiency, and enables rapid video retrieval. In particular, we present the entire architecture, components, data flows, and implementation of the system in a real-world scenario. The experimental results indicate that the architecture and system are efficient in managing CCTV cameras and supporting traditional CCTV management systems.