Democratizing AI: Implementing a Multimodal LLM-based Multi-Agent System with a No-Code Platform for Business Automation

Adopting advanced AI technologies, including Multi-Agent Systems (MAS) powered by LLMs, presents significant challenges for organizations due to high technical complexity and implementation costs. No-Code platforms have emerged as a promising solution, allowing the development of AI systems without requiring programming expertise. These platforms lower the barriers to AI adoption, allowing even non-technical users to use AI tools effectively. By 2025, nearly 70% of applications are expected to use Low-Code or No-Code platforms, indicating their growing role in democratizing AI technology. Additionally, LLMs have proven revolutionary in different applications, including generative AI, which creates new content such as text, images, and videos, and multimodal AI, which combines various data forms to perform tasks such as image recognition and multimodal retrieval.

The LLM-based MAS development has advanced AI capabilities by enabling multiple autonomous agents to engage in complex tasks through natural language interactions. These systems include special agents that process data from different sources, manage temporal and spatial relationships, and direct the allocation of work. Adopting multi-objective learning techniques, such as embedding spaces and attentional methods, improves the understanding of different types of data, enabling tasks such as image-to-text conversion and multi-modal search. These advances make AI systems flexible, efficient, and accessible, driving innovation in business environments while addressing implementation challenges.

Researchers from SAMSUNG SDS, Seoul, developed an LLM-based multimodal MAS using No-Code platforms to facilitate the integration of AI into business processes without requiring professional engineers. The system, developed using tools such as Flowise, includes Multimodal LLMs, image generation by Stable Diffusion, and RAG-based MAS. Tested in use cases such as image-based code generation and Q&A programs, it highlights agent interaction. Research emphasizes technology implementation, business performance, and performance evaluation, demonstrating improved efficiency and accessibility for non-professionals and SMEs. This research provides a way to measure the adoption of AI, reduce manual tasks and improve the practical use of MAS in all industries.

Implementing an LLM-based multimodal MAS using the Flowise platform includes cloud computing, securely managing API keys, and integrating external services such as OpenAI and Stable Distribution. A hybrid relational and NoSQL database system handles both structured and unstructured data well. Image Analysis Agents, RAG Search, Image Generation, and Video Generation process input types, such as text, images, and audio, to produce corresponding outputs such as text, images, and videos. These agents are integrated into an integrated workflow with a web-based user interface for seamless operation and real-time input processing.

The study discusses the implementation and results of multimodal MAS, focusing on various use cases such as image analysis, code generation, RAG-based search, image generation, and video generation. The system processes incomplete code images, generates code through agent interaction, and reviews it for quality. RAG search agents obtain answers from RAG information and from external sources if necessary. Image-generating agents create visuals with text descriptions or graphics, while video-generating agents generate videos based on text or image input. Integrating these agents into a unified system allows for seamless user interaction and execution of tasks.

In conclusion, the Study presents an LLM-based multimodal MAS built using a No-Code platform, Flowise, to facilitate the adoption of AI in businesses. It demonstrates the system's efficiency in automating tasks such as coding, image and video creation, and RAG-based query responses, reducing the need for specialized development teams. Research highlights the practical benefits of AI for business, such as improving efficiency and content production. It also provides a novel way to integrate multimodal data with No-Code platforms, although it admits limitations in customization, data management, and agent communication that require further development.

Check out Paper. All credit for this study goes to the researchers of this project. Also, don't forget to follow us Twitter and join our Telephone station again LinkedIn Grup. Don't forget to join our 60k+ ML SubReddit.

🚨 UPCOMING FREE AI WEBINAR (JAN 15, 2025): Increase LLM Accuracy with Artificial Data and Experimental Intelligence–Join this webinar for actionable insights into improving LLM model performance and accuracy while protecting data privacy.

Sana Hassan, a consulting intern at Marktechpost and a dual graduate student at IIT Madras, is passionate about using technology and AI to address real-world challenges. With a deep interest in solving real-world problems, he brings a fresh perspective to the intersection of AI and real-life solutions.