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.

Reliable transformation of unstructured person and address text into structured data remains a key challenge in large-scale information systems. Traditional rule-based and probabilistic approaches perform well on clean inputs but fail under noisy or multilingual conditions, while neural and large language models (LLMs) often lack deterministic control and reproducibility. This paper introduces a prompt-driven, validation-centered framework that converts free-text records into a consistent 17-field schema without fine-tuning. The method integrates input normalisation, structured prompting, constrained decoding, and strict rule-based validation under fixed experimental settings to ensure reproducibility. Evaluations on heterogeneous real-world address data show high field-level accuracy, strong schema adherence, and stable confidence calibration. The results demonstrate that combining deterministic validation with generative prompting provides a robust, interpretable, and scalable solution for structured information extraction, offering a practical alternative to training-heavy or domain-specific models.

Census-style address records contain heterogeneous structures that must be decomposed into fine-grained fields before they can support linkage, geocoding, and administrative processing. This paper describes the design and implementation of a three-stage Planner–Manager–Worker pipeline using a locally deployed open-weight language model. The prototype includes address-specific extraction, same-model semantic consistency review, formatting, record-preserving chunking, and an exploratory bounded retry pathway. Although the prototype also implements workers for names, phone numbers, email addresses, Social Security numbers, and dates of birth, the quantitative evaluation is limited to address-component parsing. We conduct a controlled pilot using 700 author-generated synthetic records distributed across seven address categories. Each record was first created as a structured object and then rendered into text; the original structured object served as the evaluation reference. Across three repeated executions on the same fixed benchmark, the complete hierarchical configuration obtained a mean micro-averaged address-component exact-match score of 95.7%, compared with 80.9% for the evaluated monolithic LLM reference; the deterministic rule-based reference obtained 52.3%. The fixed prompts were not matched, and realized inference calls, dynamic token allocation, original batching, and reported-run segmentation could not be verified from retained artifacts. No component ablations were conducted. Consequently, the numerical difference cannot be attributed uniquely to planning, task coordination, specialization, semantic review, feedback, chunking, or multi-agent organization. The reference labels were not independently annotated, the benchmark does not estimate performance on naturally occurring records, and the retry pathway was invoked only three times. The contribution is therefore the specification and controlled pilot characterization of an on-premise address-parsing pipeline, rather than evidence of architectural causality, production readiness, broad PII-extraction accuracy, or real-record robustness.

Census-style records contain sensitive personal information that must be transformed into structured fields before it can support record linkage, geocoding, duplicate detection, identity verification, and statistical processing. This task is challenging because addresses appear in many forms, including standard street addresses, apartment and unit addresses, university addresses, military APO/FPO/DPO addresses, rural routes, highway addresses, and attention-line records. Rule-based parsers are efficient and transparent but often fail on non-standard formats. Single-prompt Large Language Model (LLM) approaches improve generalization but can suffer from record skipping, field conflation, and long-context degradation when processing heterogeneous documents. In addition, privacy and governance requirements limit the use of cloud-hosted models for sensitive census-style data. This paper presents a three-stage hierarchical multi-agent architecture for structured knowledge extraction from census-style records using a locally deployed open-weight LLM. A Planner Agent analyzes the input document and formulates an extraction strategy. A Manager Agent converts this strategy into a dependency-aware task graph. A fleet of eight specialized Worker Agents performs extraction, validation, and formatting, while a bounded feedback loop supports limited autonomous recovery from extraction failures. The system runs on gpt-oss-20b, a 21-billion-parameter open-weight model deployed on local infrastructure, so input records do not need to be transmitted to an external model provider. The system is evaluated on 700 synthetically generated records across seven address categories. It achieves 95.7% component-level exact match accuracy, compared with 52.3% for a rule-based baseline and 80.9% for a single-prompt LLM baseline using the same model. The largest improvements occur on challenging non-standard categories, including highway addresses (93% vs. 11% rule-based), military addresses (91% vs. 28%), and attention-line records (90% vs. 22%). The results suggest that multi-agent decomposition can improve the robustness and completeness of open-weight LLM extraction while preserving the privacy advantages of on-premise deployment. The study should be interpreted as a prototype evaluation on synthetic data rather than a production-readiness claim.