Enhancing Efficiency and Flexibility of Rapid Prototyping for Scalable Multimodal Intelligent Agents

EasyChair Preprint 14692

Abstract

This paper explores the enhancing efficiency and architectural flexibility of rapid prototyping and scalable deployment of multimodal intelligent agents through frameworks SmartTaskAgent (i.e.AutoGen) and CollaborativeAI's (i.e. CrewAI). It highlights the frameworks' capabilities in integrating large language models (LLMs) to enhance agent functionalities and streamline multi-agent workflows. Key challenges such as role-playing capabilities, prompt robustness, hallucination mitigation, and scalability are discussed, alongside proposed solutions. The findings indicate significant advancements in AI applications across various industries, emphasizing the frameworks' potential in rap-id prototyping and scalable deployment.

Keyphrases: Agent, Generative AI, LLM, Large Language Model, Multi-agent conversation framework

@booklet{EasyChair:14692,
  author    = {Muthukumarapandian Chandrasekaran},
  title     = {Enhancing Efficiency and Flexibility of Rapid Prototyping for Scalable Multimodal Intelligent Agents},
  howpublished = {EasyChair Preprint 14692},
  year      = {EasyChair, 2024}}