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

2025-01-01

AI Summary

To address the high barriers to enterprise AI adoption—including strong technical dependencies and difficulties in practical deployment—this paper proposes a no-code, multimodal large language model (MLLM)-based multi-agent collaboration framework. The framework integrates retrieval-augmented generation (RAG), cross-modal alignment, instruction parsing, and a visual workflow orchestration engine, supporting industrial-grade applications such as image-based code recognition, advanced question answering, text-to-image generation, and prompt-conditioned video generation from images. It pioneers MLLM-driven autonomous agent construction, collaborative scheduling, and end-to-end deployment within a no-code environment, enabling non-technical users to independently design and deploy AI workflows. Evaluated across four industrial use cases, the framework reduces development time by over 70%, significantly improving business-unit AI adoption rates and process automation levels.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Multiagent Systems: Modeling other Agents

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Artificial Intelligence Accessibility
• High Entry Barrier
• Cost and Technical Difficulty

Innovation

• No-Code Platform
• Multi-Agent Collaboration
• Image Understanding