Aatif Muhammad Althaf

Entity resolution in real-world datasets remains a persistent challenge, particularly for identifying households and detecting co-residence patterns within noisy and incomplete data. While Large Language Models (LLMs) show promise, monolithic approaches often suffer from limited scalability and interpretability. This study introduces a multi-agent Retrieval-Augmented Generation (RAG) framework that decomposes household entity resolution into coordinated, task-specialized agents implemented using LangGraph. The system includes four agents responsible for direct matching, transitive linkage, household clustering, and residential movement detection, combining rule-based preprocessing with LLM-guided reasoning. Evaluation on synthetic S12PX dataset segments containing 200–300 records demonstrates 94.3% accuracy on name variation matching and a 61% reduction in API calls compared to single-LLM baselines, while maintaining transparent and traceable decision processes. These results indicate that coordinated multi-agent specialization improves efficiency and interpretability, providing a structured and extensible approach for entity resolution in census, healthcare, and other administrative data domains.

Entity resolution in real-world datasets remains a persistent challenge, particularly in identifying households and detecting co-residence patterns within inconsistent and incomplete data. Recent advances using Large Language Models (LLMs) show promise but continue to struggle with scalability, interpretability, and task complexity when applied as single, monolithic systems. This study introduces a multi-agent Retrieval-Augmented Generation (RAG) framework that decomposes household entity resolution into coordinated and specialized agents. The system, implemented using LangGraph, includes four agents: a Direct Agent for name-based matching, an Indirect Agent for transitive linkage, a Household Agent for address-based clustering, and a Household Moves Agent for tracking residential relocations. Each agent employs a task-specific RAG retrieval strategy and a hybrid data cleaning pipeline that integrates rule-based and LLM-powered parsing. Evaluated on synthetic S12PX dataset segments containing 200–300 records with extensive duplicates and data quality issues, the framework achieved 94.3\% accuracy on name variations, complete decision transparency, and a 61\% reduction in API calls compared to single-LLM approaches. These results demonstrate that coordinated agent specialization enhances accuracy, efficiency, and interpretability, establishing a scalable paradigm for entity resolution applicable to census operations, healthcare, and other structured data domains.