Agentic AI Applications
Agentic AI represents a significant advancement in artificial intelligence, characterized by autonomous, goal-oriented behavior and adaptability across diverse environments. Unlike traditional AI systems that operate within predefined parameters, Agentic AI demonstrates the ability to set and pursue complex objectives independently, adapting to changing conditions with minimal human intervention.
At its core, Agentic AI integrates advanced decision-making capabilities, environmental awareness, and self-directed learning. These systems leverage reinforcement learning, goal-oriented architectures, and adaptive control mechanisms to navigate complex, multi-objective tasks. Key technical foundations include meta-learning for rapid adaptation, transfer learning for generalization across domains, and hierarchical reinforcement learning for managing long-term goals. The implementation of Agentic AI often involves multi-agent systems, where individual agents collaborate or compete to achieve overarching objectives, and incorporates explainable AI (XAI) techniques to enhance transparency and accountability. As Agentic AI continues to evolve, it presents both transformative potential and significant challenges in areas such as ethical alignment, scalability, and integration with existing systems, necessitating ongoing research in goal alignment, adaptive moral frameworks, and robust governance structures.
Required Reading and Listening
Listen to the podcast:
Read the following:
- Textbook: Michael Albada, Building Applications with AI Agents, , 1st ed., Published by O’Reilly Media, Inc., ISBN-13 978-1098176495. This book is available in print and digital on O’Reilly Media. GSU Library Link
- Paper: Deepak Bhaskar Acharya, Karthigeyan Kuppan, B. Divya, Agentic AI: Autonomous Intelligence for Complex Goals—A Comprehensive Survey PDF
More resources can be found on the resource page Agentic AI Related Texts