How MultiAgent AI Is Revolutionizing E‑commerce Video Production

Introduction

With the rapid development of artificial intelligence, AIGC (AI‑Generated Content) is reshaping video creation. Traditional video production is slow and costly, while AIGC tools have boosted short‑video adoption from less than 5% in 2022 to 35% in 2025, prompting e‑commerce search to explore automated video production.

Early Project Evolution and Problems

Initially the project generated storyboard scripts with large models and retrieved image assets, while other elements (titles, layout, sound, effects) were selected by rule‑based methods. Two major issues emerged:

Rule‑based generation led to highly templated, homogeneous videos.

Most video material consisted of voice‑over plus static images, resulting in a stiff and unattractive experience.

MultiAgent Video Generation System

The solution upgraded the rule‑based pipeline to a MultiAgent collaborative video generation system that dynamically schedules video elements to maximize both prior and posterior metrics.

Key upgrades include:

Enriching material supply with diverse video and chart assets, greatly increasing the proportion of high‑definition video content.

Implementing a MultiAgent workflow: storyboard script generation → multi‑type material generation → end‑to‑end large‑model editing.

Script Generation Agent

To improve script accuracy, the system sources information primarily from a high‑precision e‑commerce knowledge graph, supplemented by high‑quality third‑party video copy. This raises script usability dramatically.

To enhance script appeal, a collection of narrative styles is built, allowing dynamic selection of script structure and tone based on the query.

Multi‑type Material Generation

Two challenges for e‑commerce video material are scarcity of raw footage and low retrieval accuracy of generic video search. The solution introduces:

Automated generation of 30+ generic chart templates via large‑model code generation, achieving 92% usable rate.

Multi‑modal video understanding (e.g., Qwen2.5‑VL‑32B) to ensure entity consistency and high clarity, filtering out blurry clips.

Large‑Model Editing

Multi‑round planning inference selects optimal assets, layout, motion, and sound, producing the final edited video.

Future Directions

End‑to‑end script generation to unify objectives across agents.

Fully generative AIGC video creation to overcome material shortages, while addressing current limitations such as garbled text and entity errors.