The adoption of advanced ai technologies, including LLM-powered multi-agent systems (MAS), 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 artificial intelligence systems without the need for programming experience. These platforms lower the barriers to ai adoption, allowing even non-technical users to leverage ai tools efficiently. By 2025, almost 70% of applications are expected to use Low-Code or No-Code platforms, demonstrating their growing role in the democratization of ai technologies. Additionally, LLMs have proven to be transformative in a variety of applications, including generative ai, which creates new content such as text, images, and videos, and multimodal ai, which integrates various forms of data for tasks such as image recognition and cross-modal retrieval. .
The development of LLM-based MAS has further advanced ai capabilities by enabling multiple autonomous agents to collaborate on complex tasks through natural language interactions. These systems integrate specialized agents that process data from different modalities, manage temporal and spatial relationships, and coordinate the assignment of tasks. Adopting multimodal learning techniques, such as gap embedding and cross-attention mechanisms, improves understanding of various types of data, enabling tasks such as image-to-text transformation and cross-modal search. These advances make ai systems more flexible, efficient, and accessible, driving innovation in enterprise environments while addressing implementation challenges.
Researchers at SAMSUNG SDS, Seoul, developed an LLM-based multimodal MAS using No-Code platforms to simplify the integration of ai into business processes without the need for professional developers. The system, built with tools such as Flowise, integrates multimodal LLM, stable diffusion imaging, and RAG-based MAS. Evaluated through use cases such as image-based code generation and question-and-answer systems, it highlights the synergies of collaborative agents. The study emphasizes technical implementation, commercial applicability and performance evaluation, showing greater efficiency and accessibility for non-experts and SMEs. The research offers a scalable methodology for ai adoption, reducing manual tasks and promoting practical use of MAS across industries.
Deploying an LLM-based multimodal MAS using the Flowise platform involves setting it up in the cloud, securely managing API keys, and integrating external services such as OpenAI and Stable Diffusion. A hybrid relational and NoSQL database system efficiently handles structured and unstructured data. Agents for image analysis, RAG search, image generation, and video generation process input types, such as text, images, and audio, to produce corresponding results such as text, photos, and videos. These agents are integrated into a unified workflow with a web-based user interface for seamless functionality and real-time input processing.
The study analyzes the implementation and results of a 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 collaboration, and reviews its quality. RAG search agents obtain answers from RAG knowledge and from external sources when necessary. Image generation agents create images from text descriptions or sketches, while video generation agents produce videos based on text or image inputs. Integrating these agents into a unified system enables seamless user interaction and task execution.
In conclusion, the study presents an LLM-based multimodal MAS built using a no-code platform, Flowise, to simplify ai adoption in enterprises. It demonstrates the system's effectiveness in automating tasks such as code generation, image and video creation, and RAG-based query responses, reducing the need for specialized development teams. The research highlights the practical benefits of ai in business, such as improving efficiency and generating content. It also offers a novel methodology for integrating multimodal data with No-Code platforms, although it recognizes limitations in personalization, data management and communication with agents that require further refinement.
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Sana Hassan, a consulting intern at Marktechpost and a dual degree student at IIT Madras, is passionate about applying technology and artificial intelligence to address real-world challenges. With a strong interest in solving practical problems, he brings a new perspective to the intersection of ai and real-life solutions.
