Abstract
The proposed system, “Multipurpose AI Agents”, is an integrated AI-driven platform designed to provide intelligent solutions across multiple real-world domains through independent, specialized agents. The system combines machine learning, deep learning, and recommendation algorithms to perform tasks such as cryptocurrency price prediction, disease prediction, movie and music recommendation, semester paper prediction, research assistance, and business advisory. Each agent is optimized for its specific task and operates through a unified web interface for ease of use. The platform implements state-of-the-art models including time-series forecasting models for crypto markets, classification models for disease detection, similarity-based recommender systems for entertainment, and LLM-powered agents for research and business insights. All agents are deployed using Python and Streamlit with seamless integration enabling fast inference and interactive visualization. The system achieves high performance across modules, with average prediction and recommendation accuracy ranging from 82% to 92%, depending on the model and dataset used. A modular architecture ensures smooth data processing, preprocessing, prediction, and result rendering for each agent. Users can access outputs such as prediction graphs, recommendation lists, confidence scores, and decision insights. The platform is scalable and can be extended to additional domains like education support, smart analytics, or autonomous decision tools. Evaluation is conducted using metrics such as accuracy, precision, recall, RMSE, and user feedback scores. Overall, the project demonstrates an effective multi-agent AI system capable of solving diverse tasks through a unified platform, highlighting the power of machine learning and intelligent automation in real-world applications.