Transform Video Into Instantly Searchable, Actionable Intelligence with AI Agents and Skills

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. You can also join us live on Wednesday, May 13, at 9 am PT, to learn how to build a video analytics AI agent with VSS skills. Build a video AI agent with VSS skills and coding agents In the past, developers had to manually configure, deploy and integrate the rich set of microservices VSS provides for video management, search, summarization and more to build video analytic applications. Today, it’s possible to use coding agents augmented with VSS skills to automate the deployment, usage and integration of VSS all through a simple agentic chat interface. VSS skills are hosted on the VSS GitHub Repository and follow the agent skills specification, allowing them to be used with a wide variety of agents. A prerequisite to utilizing these skills is to have a system that is set up to run VSS and an agent compatible with skills such as Codex, Claude Code, OpenClaw, or NemoClaw. First we will show an example of how to add VSS skills to Codex and use it to deploy the VSS search profile. Then, we will show how to add VSS skills to OpenClaw, which will allow us to interact with our VSS deployment through nearly any chat interface to search and analyze large volumes of video. Setting up the VSS pre-requisites The first step is to prepare a system to run VSS. The easiest way to do this is to use the NVIDIA Brev Launchable for VSS. Go to the VSS launchable documentation page and click the “Launch Blueprint” button and then “Deploy Launchable.” Once deployed click the Open Notebook button and navigate to the /video-search-and-summarization/scripts/deploy_vss_launchable.ipynb notebook. Paste in your NGC_CLI_API_KEY from NGC in the first cell and then execute the entire notebook including the tear-down section. This will ensure the system is fully set up…