In today’s data-driven world, organizations increasingly rely on video to capture critical information, yet extracting meaningful, real-time insights from massive amounts of footage remains a challenge. NVIDIA Metropolis Blueprint for video search and summarization (VSS) overcomes this hurdle by transforming millions of live video streams or hours of recorded video into instantly searchable, actionable intelligence.
VSS brings a reference architecture for building video analytics AI agents that perceive, reason, and act in real-time on massive volumes of live video streams and recorded data. It uses accelerated vision-based microservices, vision-language models (VLMs), large language models (LLMs), and retrievers for real-time video intelligence, agentic search, and automated reporting. VSS helps enterprises monitor operations, detect trends, and make informed decisions faster than ever. The latest version of VSS brings a new modular design, advanced fusion search capability and a set of skills to easily integrate with autonomous agents.
In this post you will learn how to use the new VSS skills with coding agents to automate VSS deployment and integration into custom applications, followed by a deep dive into the technology behind VSS 3. Continue reading to learn how to use VSS skills with coding agents for building autonomous video analytics AI Agents.
