Agentic AI: A Comprehensive Review of Evolution and Applications
Key takeaways
- Agentic AI is a rapidly advancing field with significant potential across various domains.
- The review covers the evolution, architecture, working principles, and applications of Agentic AI.
- It identifies current challenges and outlines future research directions for the technology.
- A proposed framework helps understand stakeholder intentions and adoption factors for Agentic AI.
Who benefits
Summary
A new comprehensive review explores the evolution, working principles, architecture, and real-world applications of Agentic AI. It also identifies current challenges, future research directions, and proposes a framework for understanding adoption factors.
Why it matters
For professionals navigating the AI landscape, this review offers a foundational understanding of Agentic AI, its capabilities, and its potential impact. It helps in identifying strategic opportunities and challenges for integrating agentic systems into business operations.
How to implement this in your domain
- 1Educate your team on the core principles and architectures of Agentic AI to foster a common understanding.
- 2Identify potential use cases within your organization where Agentic AI could automate complex tasks or enhance decision-making.
- 3Evaluate existing AI infrastructure to determine its readiness for integrating agentic systems.
- 4Develop a roadmap for piloting Agentic AI solutions, starting with low-risk, high-impact applications.
- 5Stay informed about ongoing research and best practices in Agentic AI to adapt strategies as the field evolves.
Original post by AKM Bahalul Haque, Al Amin Islam Ridoy, Mohammad Rayhan, Ivan Porres
"arXiv:2608.18110v1 Announce Type: new Abstract: Agentic AI is gaining new insights and advancements in the field of Artificial Intelligence, fostering significant potential to enable rapid transformation across various domains.This rapid advancement and the potential to revolutio…"
View on XOriginally posted by AKM Bahalul Haque, Al Amin Islam Ridoy, Mohammad Rayhan, Ivan Porres on X · view source
Want to go deeper?
Turn these trends into skills with Learnijoy's hands-on AI & tech courses.
Explore coursesMore in AI News & Tools
FedLNS Mitigates Adversarial Attacks in Federated LLMs
This paper introduces FedLNS, a server-side framework that uses LayerNorm signatures to screen malicious client updates in federated large language models (LLMs). FedLNS effectively mitigates adversarial manipulation without requiring extra client-side data or labeled attack examples, improving model robustness.
Deep Learning Projects Europe Will Miss 2030 Climate Target
This research uses deep learning to project that the EU27 will miss its 2030 greenhouse gas emission reduction target by 35%, with mobility being a major lagging sector. The findings suggest significant additional intervention is required beyond current trends.
Scalable Geospatial ML for Power-Line Asset Risk Management
This study presents a modular and explainable geospatial machine learning framework for assessing power-line asset failure risk from lightning and vegetation. It integrates multi-source remote sensing data and utility records, offering a computationally efficient and extensible solution for climate-resilient network operations.