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This paper explores the integration of Agentic AI with Large Language Models (LLMs) through the use of personas, aiming to enhance AI responsiveness and adaptability. Agentic AI, which emphasizes goal-directed behavior and autonomy, is combined with LLMs’ vast knowledge bases to create more dynamic and context-aware systems. Personas serve as intermediaries, encapsulating user preferences, behaviors, and cultural nuances, enabling AI to tailor interactions effectively. We discuss the methodologies for designing and implementing these personas, the synergistic benefits of this fusion, and the evaluation metrics for assessing performance. The results indicate significant improvements in user satisfaction, task completion rates, and AI adaptability across various domains. This research paves the way for more personalized and efficient AI applications.

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Agentic AI & LLM Incorporation with Personas

  • 会议论文

  • 发表日期：

    2026年

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  • Arpita Bhowmick,
  • Atif Farid Mohammad,
  • Shravya Kalva

摘要

This paper explores the integration of Agentic AI with Large Language Models (LLMs) through the use of personas, aiming to enhance AI responsiveness and adaptability. Agentic AI, which emphasizes goal-directed behavior and autonomy, is combined with LLMs’ vast knowledge bases to create more dynamic and context-aware systems. Personas serve as intermediaries, encapsulating user preferences, behaviors, and cultural nuances, enabling AI to tailor interactions effectively. We discuss the methodologies for designing and implementing these personas, the synergistic benefits of this fusion, and the evaluation metrics for assessing performance. The results indicate significant improvements in user satisfaction, task completion rates, and AI adaptability across various domains. This research paves the way for more personalized and efficient AI applications.