You need to find one moment in 500 hours of video: a demo where someone mentions a pricing change while showing a dashboard. The old way extracts frames, runs OCR, transcribes the audio, builds separate text and image search indexes, then queries across all of them and hopes the timestamps line up. That is not retrieval, that is suffering. The problem is the architecture. Treating video as separate streams means you take it apart before you can search it, and the glue that holds those parts together becomes the most fragile piece you own. Models that understand video directly skip all of that: they read what is shown, said, and on screen at once. You see a video search agent run against the old multi-tool pipeline, replacing a 200-line orchestration script with a single call, plus an honest look at when the old decomposition approach is still the better choice. All code is open source.

Outline: • The 500-Hour Problem • Why Decomposition Fails • The Multimodal Shift • Building the Video Agent • When to Use What and Resources