Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform

TL;DR

This paper presents a No-Code platform enabling non-programmers to build multimodal LLM-powered multi-agent systems, facilitating AI adoption in enterprises through practical, scalable, and accessible solutions.

Contribution

It introduces a No-Code framework for creating multimodal LLM-based multi-agent systems, lowering technical barriers and demonstrating diverse real-world business applications.

Findings

Validated use cases including code generation, question-answering, and media creation.

Demonstrated scalability and accessibility of No-Code AI systems.

Empowered non-technical users to adopt AI in enterprise settings.

Abstract

This study proposes the design and implementation of a multimodal LLM-based Multi-Agent System (MAS) leveraging a No-Code platform to address the practical constraints and significant entry barriers associated with AI adoption in enterprises. Advanced AI technologies, such as Large Language Models (LLMs), often pose challenges due to their technical complexity and high implementation costs, making them difficult for many organizations to adopt. To overcome these limitations, this research develops a No-Code-based Multi-Agent System designed to enable users without programming knowledge to easily build and manage AI systems. The study examines various use cases to validate the applicability of AI in business processes, including code generation from image-based notes, Advanced RAG-based question-answering systems, text-based image generation, and video generation using images and prompts. These systems lower the barriers to AI adoption, empowering not only professional developers but also general users to harness AI for significantly improved productivity and efficiency. By demonstrating the scalability and accessibility of No-Code platforms, this study advances the democratization of AI technologies within enterprises and validates the practical applicability of Multi-Agent Systems, ultimately contributing to the widespread adoption of AI across various industries.