Comparative Analysis of AI Agent Architectures for Entity Relationship Classification

Entity relationship classification remains a challenging task in information extraction, especially in scenarios with limited labeled data and complex relational structures. In this study, we conduct a comparative analysis of three distinct AI agent architectures designed to perform relation classification using large language models (LLMs). The agentic architectures explored include (1) reflective self-evaluation, (2) hierarchical task decomposition, and (3) a novel multi-agent dynamic example generation mechanism, each leveraging different modes of reasoning and prompt adaptation. In particular, our dynamic example generation approach introduces real-time cooperative and adversarial prompting. We systematically compare their performance across multiple domains and model backends. Our experiments demonstrate that multi-agent coordination consistently outperforms standard few-shot prompting and approaches the performance of fine-tuned models. These findings offer practical guidance for the design of modular, generalizable LLM-based systems for structured relation extraction. The source codes and dataset are available at \href{this https URL}{this https URL}.

@article{berijanian2025_2506.02426,
  title={ Comparative Analysis of AI Agent Architectures for Entity Relationship Classification },
  author={ Maryam Berijanian and Kuldeep Singh and Amin Sehati },
  journal={arXiv preprint arXiv:2506.02426},
  year={ 2025 }
}