Building Real-Time Video Agents with VAST Data Engine

Most video AI demos stop at simple playback or offline analysis. Real-time video intelligence at scale requires ingesting streams, processing content, and retrieving meaningful insights instantly. WHAT YOU'LL BUILD A working real-time video agent powered by VAST DataEngine. You'll implement a full pipeline: from ingesting video streams to generating summaries, detecting events, and retrieving relevant moments using embeddings. By the end, you'll have a system you can run, tweak, and take back to your team, capable of processing video in real time, flagging key events, and integrating with downstream tools like Slack. Your pipeline will: Ingest video via event-driven triggers (S3 buckets) Generate LLM-powered video summaries Detect events from video streams Create video embeddings for semantic search Retrieve relevant video segments using vector search Send automated notifications for key events KEY TOPICS Event-driven architectures for video processing Building with VAST DataEngine for AI pipelines LLM-based video summarisation Video embeddings and vector search Designing scalable, real-time video pipelines Translating prototypes into production systems AGENDA 4:00 PM — Doors Open: Welcome & Check-In Security check-in - elevator to 7th floor - grab a coffee/water/soda 4:30 PM — Framing & Vision: What We’re Building and Why 4:45 PM — Live Demo: End-to-End Video Agent in Action 5:00 PM — Guided Build Part 1: Core DataEngine Foundations (Connect to VAST lab, trigger functions, LLM integration) 6:00 PM — Break 6:10 PM — Guided Build Part 2: Production Features (Video embeddings, vector queries, user-facing applications) 6:55 PM — Production Wrap-Up: Scaling to Real-World Systems 7:10 PM — Q&A & Next Steps 7:25 PM — Networking with Peers and the VAST Team 8:00 PM — Event Close LEARNING OUTCOMES By the end, you'll be able to: Explain how VLM-powered video agents work in real-time production environments Use VAST DataEngine to build scalable pipelines for video ingestion and processing Implement an end-to-end workflow: ingest → process → summarise → embed → retrieve Apply vector search to surface relevant insights from large-scale video data Design event-driven architectures for automating video intelligence systems Understand how to take a prototype and extend it into a production-ready setup Confidently adapt and reuse the starter repo for real-world use cases WHO SHOULD ATTEND Intermediate to senior developers, ML/AI engineers, agent builders, and data engineers. Industries: AI, Media & Entertainment, Financial Services PREREQUISITES Required: Laptop Comfortable coding in Python Familiarity with APIs and basic ML workflows Helpful (not required): Experience with LLMs, embeddings, or event-driven systems Setup: You'll connect to the VAST lab environment (no local setup required). Instructions sent 3-5 days before the workshop. Seats are limited: register now to secure your spot!