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

With the recent advancements in Large Language Models (LLMs) and Agentic AI, Context Engineering (CE) has emerged as a novel research area. CE aims to fill the prompts for LLM Agents with relevant contextual knowledge required to perform complex tasks, where the quality of this context is paramount for reliability. Knowledge Graphs (KGs) offer a promising approach to integrate diverse contextual knowledge based on Semantic Web and Knowledge Representation approaches. In this paper, we study current approaches to identify challenges and opportunities for utilising KGs in CE and explore their limitations and strategic future research directions. The findings illustrate inconsistencies in methodologies and limited understanding of scalability and quality assurance challenges, which slow down the development of robust, context-aware AI systems capable of dealing with real-world complexity and multi-domain reasoning tasks.