Modern computer-vision workflows often involve multiple specialized agents (image classifiers, annotation tools, QA reviewers, billing trackers) working together. An LLM-based orchestrator can plan and route tasks across these agents (think cognitive orchestration) where the “master agent” uses context and SOPs to decide the next step.

This blog explains how Retrieval-Augmented Generation (RAG) can transform traditional video search by enabling users to query massive video libraries through natural language or image-based prompts. It details how video segments are analyzed, embedded, and indexed in a vector database like MongoDB Atlas Vector Search or pgvector, then retrieved using semantic similarity. Combining OpenAI embeddings, CLIP, and AWS serverless orchestration (S3, Lambda, Step Functions), this architecture delivers accurate, multimodal video retrieval that grounds AI answers in actual footage making video libraries smarter, searchable, and more useful at scale.

Modern computer-vision workflows often involve multiple specialized agents (image classifiers, annotation tools, QA reviewers, billing trackers) working together. An LLM-based orchestrator can plan and route tasks across these agents (think cognitive orchestration) where the “master agent” uses context and SOPs to decide the next step.