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Chapter 3 · 2025

A Research Landscape of Agentic AI and Large Language Models: Applications, Challenges and Future Directions

Sarfraz Brohi, Qurat-ul-ain Mastoi, N. Z. Jhanjhi

Abstract

Agentic AI and Large Language Models (LLMs) are transforming how language is understood and generated while reshaping decision-making, automation, and research practices. LLMs provide underlying reasoning capabilities, and Agentic AI systems use them to perform tasks through interactions with external tools, services, and Application Programming Interfaces (APIs). This study identifies that core challenges of LLMs, relating to security, privacy and trust, misinformation, misuse and bias, energy consumption, transparency and explainability, and value alignment, can propagate into Agentic AI.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

This paper contributes useful building blocks for Zone III architecture through its work on Agentic AI, LLMs, context management. While not exclusively focused on enterprise deployment, the insights translate directly to the challenges of long-horizon agentic workflows. The key lesson for Zone III practitioners: the problems identified here do not disappear at scale — they compound. Understanding them at the research level is prerequisite to solving them in production.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Topics

Agentic AILLMscontext managementmulti-agent systemssecurityprivacy