Ordinal essay grading, an essential task in educational domain and natural language processing (NLP), involves categorising essays based on quality, such as grading scale levels. This is crucial in automated assessment systems that evaluate student writing and provide feedback on aspects like coherence, argumentation, and language proficiency. However, challenges arise from limited data resources, such as when a new assessment is introduced and no data is available to train algorithms, as well as the complexity of essay structures in real-world grading scenarios. This research explores the use of few-shot learning, a technique that learns from a limited number of labeled examples, to address these challenges in ordinal essay grading. By leveraging few-shot learning’s ability to identify class representations from minimal examples, we aim to mitigate data scarcity in essay grading. With the rise of Large Language Models (LLMs), we seek to improve few-shot prompting performance by introducing novel strategies for example selection, enhancing class representation in the demonstrations provided to prompts. Finally, we aim to apply prototypical methods to agents architectures for agent selection on the basis of similarity weighted by ordinal class knowledge.