Survey on Multi-Agent and Multimodal Architectures for Intelligent Task Automation (AVIA Project)

Abstract

The rapid evolution of Large Language Models (LLMs) and multi-agent systems (MAS) has paved the way for advanced Virtual Personal Assistants (VPAs) capable of performing complex, real-world tasks beyond simple, single- query responses. Traditional AI assistants are often limited in scope, lacking the deep integration, persistent memory, and adaptability required for cross-platform workflows, forcing users to rely on multiple tools. This survey examines the architectural and methodological shift toward Intelligent Task Automation, focusing on systems that leverage multimodal and multi-agent frameworks, exemplified by the AVIA (Autonomous Virtual Intelligent Assistant) project. We analyze core components in- cluding specialized agents orchestrated by a central planner (like n8n), the integration of LLMs for sophisticated Natural Language Understanding (NLU), and the use of multimodal capabilities (voice/image input, text/audio output). We explore key technical concepts, including the Transformer architecture and Retrieval-Augmented Generation (RAG) for conversational memory. The findings highlight the significant potential of multi- agent and multimodal systems to provide a unified, efficient, and context-aware solution for digital task automation, improving productivity and moving toward a more versatile Agentic AI future.

Introduction

The rapid evolution of Large Language Models (LLMs) and multi-agent systems is transforming Virtual Personal Assistants (VPAs) from basic query responders into autonomous, context-aware digital workers. Traditional assistants are limited by single-query responses, lack of persistent memory, and poor integration with external tools, whereas modern VPAs leverage multimodal inputs, agentic AI, and memory-augmented architectures to understand user goals, decompose complex tasks, and execute workflows autonomously. Multi-agent systems enable specialization and parallel task execution, while orchestration platforms like n8n integrate VPAs with APIs and productivity tools, enhancing automation reliability and cross-platform functionality.

Memory systems, particularly Retrieval-Augmented Generation (RAG), allow VPAs to retain long-term context, personalize interactions, and improve decision-making. Despite these advancements, challenges remain in multimodal understanding, real-time performance, integration with third-party applications, security, and accessibility. Future directions focus on multilingual capabilities, scalable and efficient architectures, context-aware intelligence, and explainable AI to build robust, user-centric assistants capable of proactively managing complex digital workflows.

Conclusion

The rapid evolution of Large Language Models (LLMs), multimodal processing, and multi-agent architectures has transformed the landscape of Virtual Personal Assistants (VPAs). This survey examined key advancements from early rule-based dialogue systems to modern agentic AI frameworks capable of reasoning, planning, and automating complex dig- ital workflows. While LLM-powered assistants significantly outperform traditional approaches in language understanding and contextual reasoning, they still face several limitations in scalability, reliability, and real-time decision-making across diverse platforms. A major gap identified across current research is the limited ability of many VPAs to serve as fully autonomous, end-to-end task executors. Most systems excel at isolated functions—such as question answering, scheduling, or document analysis—but struggle to integrate these abilities into cohesive, long-duration workflows. Challenges such as latency in multi-agent coordi- nation, inconsistent memory retrieval, tool integration failures, and dependency on stable internet connectivity continue to hinder robust real-world deployment. Additionally, multimodal processing, while powerful, remains sensitive to noisy environ- ments, ambiguous images, and shifting user context. Key areas such as long-term personalization, proactive task initiation, secure data handling, and cross-platform automation require further enhancement. Future progress will depend on developing more efficient LLM variants, optimizing multi- agent pipelines, and strengthening memory systems using advanced Retrieval-Augmented Generation (RAG) techniques. Improved orchestration frameworks, hybrid cloud–edge exe- cution models, and standardized interfaces will also play a critical role in enabling seamless automation. Ultimately, the future of VPAs like AVIA lies in combining technological innovation with user-centered design to create assistants that are not only intelligent and autonomous but also trustworthy, accessible, and adaptable to everyday digital environments.

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Copyright

Copyright © 2025 Munaf Irfan Shaikh , Ayaan Badshah Khan, Hitesh Vinod Dadlani, Prof. Jayshri Kandekar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.