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Chapter 4 · 2026

Agentic AI, Context Engineering and Knowledge Graphs: Current Approaches, Challenges and Opportunities

Niraj Karki, Manjila Pandey, Sanju Tiwari

Abstract

With the recent advancements in Large Language Models (LLMs) and Agentic AI, Context Engineering (CE) has emerged as a novel research area. Knowledge Graphs (KGs) offer a promising approach to integrate diverse contextual knowledge based on Semantic Web and Knowledge Representation approaches. This paper studies current approaches to identify challenges and opportunities for utilising KGs in CE.

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 directly addresses one of the core structural challenges in Zone III deployments. The research on Context Engineering, Knowledge Graphs, Large Language Models provides evidence-based foundations that enterprise architects cannot ignore when designing long-horizon autonomous workflows. The findings challenge the assumption that a base language model — however capable — can handle the complexity of durable, governed, multi-step execution without explicit architectural intervention. For Zone III practitioners, this paper belongs in the required reading list.

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

Context EngineeringKnowledge GraphsLarge Language ModelsAgentic AI