Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), have accelerated the development of autonomous agents capable of performing complex tasks such as data analysis, decision-making, and workflow automation. Existing research demonstrates strong progress in isolated domains, including data-driven analytics and task execution systems. However, a critical gap remains in the development of fully integrated, end-to-end autonomous agents that seamlessly combine analytical reasoning with real-world task execution across diverse platforms. This study presents a conceptual framework and gap-driven analysis for next-generation AI-powered autonomous agents that unify data processing, insight generation, and action execution within a single system. Drawing on a comprehensive literature review, the research identifies key limitations in current approaches, including fragmented system architectures, limited cross-platform interaction capabilities, lack of domain generalization, and insufficient real-world deployment readiness. Additionally, persistent challenges related to security, interpretability, and user trust, such as prompt injection vulnerabilities and data privacy risks, are examined. The proposed framework emphasizes the integration of LLM-based reasoning with tool-enabled environments, enabling agents to interact with spreadsheets, dashboards, web applications, and external APIs in a coordinated manner. It also highlights the importance of incorporating robust security mechanisms and adaptive learning strategies to support reliable performance across multiple domains. The findings contribute to the growing body of knowledge on AI-driven automation by outlining a pathway toward scalable, secure, and adaptable autonomous systems. This research provides practical insights for academia and industry, supporting the development of intelligent agents capable of enhancing productivity, decision-making, and operational efficiency in real-world environments.