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

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 including 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.