Every team that works with video knows the feeling: someone asks for “the highlights that capture the essence of the video” or “which audience profile does this fit best?” Hours disappear into scrubbing, skimming transcripts, and exporting guesses.
Agentic AI changes that rhythm. Instead of you doing the hunting, a video-aware agent plans the steps, retrieves the right moments with multimodal evidence (visuals, speech, and audio), and then acts. It returns answers with timecodes, clip candidates, and a ready-to-ship video to text metadata like captions and transcripts. That is the promise of Retrieval Augmented Generation (RAG) applied to video, often called Video RAG, and it’s why conversation-native tools are quietly redefining video work.
What “agentic” really means for video work
Agentic AI, especially multimodal agentic video AI, accelerates the loop people already perform: plan, retrieve and act. The agent takes your prompt, understands the content and available tools, retrieves and acts based on full comprehension of content assets and tools that manipulate it.
For video, an agent typically:
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Plans a search strategy from your plain-language prompt
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Retrieves time-coded evidence based on everything it sees and hears in the video frames, objects, people, scenes, speech, emotions and sound
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Act with tools – assembling highlight sets, exporting captions, or compiling reports—so outcomes aren’t just text, they’re assets
This is the leap from answering questions about video to completing real work on videos.
Video RAG – How it works
Retrieval-Augmented Generation (RAG) describes how large language and vision-language models interpret a user’s intent, choose a retrieval strategy, and then generate answers grounded in evidence. Traditional RAG extracts facts from documents. Video RAG pulls moments from footage and respects structure and timecodes.
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Index. Build a multimodal index: extract visual features and descriptions, categorise and summarise content, convert speech and visual descriptions into time-coded text, and analyse affective/auditory cues—with timestamps.
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Retrieve. A multimodal search index ranks relevant moments across visuals, speech and audio – not just lines in a transcript. A proper retrieval engine returns high quality time coded video segments that are instantly ready to be clipped out and reused as they are perfectly timed answers to any query.
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Generate. A model composes an answer that matches the user’s intent, and cites timecodes where useful.
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Act. The agent triggers tools to save clips, export edits/metadata, or publish a report.
Result: semantic video search that goes beyond a clip bin. It can create a compliance note, summary, assessment, analytical insight or any other deliverable just like a capable human assistant would.
Agentic video AI vs. human process
Before (human work): Your team filters transcripts for brand mentions, then opens timelines to see if the visuals match. You export a handful of shots and hope you didn’t miss a better one. Spent time is significant.
After (agentic): You ask, “Find every scene where our brand appears and explain the context. Build a 30-second teaser.” The agent returns time-coded hits with short summaries, proposes a rough cut from the strongest segments, and lets you tweak the order before export.
Five tasks that shine with a video RAG system
1) Brand and context analytics
Search for logos, on-screen placements, and spoken mentions across a campaign. Get scene-level details and a set of clips ready to export.
2) Compliance, safety and moderation
Flag sensitive imagery and risky language with citations to exact moments. Profile video for age rating backed with evidence found inside the videos.
3) Promotional highlights
Describe the tone (“funniest moments”, “high-energy crowd shots”, “key soundbites”). The agent proposes candidates across multiple videos, so editors start from a stronger first cut.
4) Insightful content assets, reports and summaries
For each upload, generate chapters, summaries, keywords, entities, transcripts and captions. The video to text assets you need to drive downstream actions.
5) Knowledge mining across libraries
Ask, “Where does the spokesperson contradict last year’s claim?” and receive an answer with linked timecodes and optional exportable clips.
What defines a multi-purpose agentic video system?
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Multimodal depth: Does it understand visuals and speech/audio with timestamps, or only transcripts?
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Tools for concrete actions and content workflows: Can it deliver outcomes and results in variety of formats? Clips, captions and content reports deliver broad-based utility over plain chat responses.
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Governance: Are tool actions and results understandable, verifiable and compliant? Can a user take over and use content tools independenty of the agentic conversation?
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Integrations and exports: Can it deliver versatile exports to MAM/NLE, storage and downstream systems with versatile export options?
Valossa Assistant: built on Video RAG
Video RAG (Video Retrieval-Augmented Generation) applies RAG to audiovisual content so LLMs (large language models), LVLMs (large vision-language models) and ensembles of small audiovisual models can work together to understand questions and answer using information inside the video. Both large and efficient multimodal models can be used where appropriate.
Valossa Assistant was built on the ask, search and act paradigm for real footage: you can chat with multiple videos, run semantic video search across visuals, speech, and sound, then save and export highlight clips or pull a full AI Content Report with chapters, summaries, keywords, entities, transcripts, and captions—all in one place. It’s conversation-native, time-accurate, and outcome-oriented, and also gives direct access to the tools where users can search, inspect, clip and export without the Assistant.
This focus sets it apart from creator-first editors and API-only platforms, which either stop at transcripts or require heavy system development and integration to feel conversational.
Try it:
Get engaged with the Valossa Assistant™ beta and run your toughest query – see how quickly you move from questions to clips, captions, and deep content insight.
Editor’s note on terminology
We use agentic AI, retrieval augmented generation (RAG), and Video RAG deliberately. These describe systems that don’t just log your media and leave the hard work for you. Agentic video systems understand user requests, retrieve evidence and deliver work results using natural language on videos. If your current stack still feels like hunting through transcripts, it’s time to let an agent take the first pass.