HabitatAgent: An End-to-End Multi-Agent System for Housing Consultation

• URL: http://arxiv.org/abs/2604.00556v1
• Date: Wed, 01 Apr 2026 06:59:50 GMT
• Title: HabitatAgent: An End-to-End Multi-Agent System for Housing Consultation
• Abstract summary: We present HabitatAgent, the first LLM-powered multi-agent architecture for end-to-end housing consultation.<n> HabitatAgent comprises four specialized agent roles: Memory, Retrieval, Generation, and Validation.<n>We evaluate HabitatAgent on 100 real user consultation scenarios under an end-to-end correctness protocol.
• Score: 8.894547553174196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Housing selection is a high-stakes and largely irreversible decision problem. We study housing consultation as a decision-support interface for housing selection. Existing housing platforms and many LLM-based assistants often reduce this process to ranking or recommendation, resulting in opaque reasoning, brittle multi-constraint handling, and limited guarantees on factuality. We present HabitatAgent, the first LLM-powered multi-agent architecture for end-to-end housing consultation. HabitatAgent comprises four specialized agent roles: Memory, Retrieval, Generation, and Validation. The Memory Agent maintains multi-layer user memory through internal stages for constraint extraction, memory fusion, and verification-gated updates; the Retrieval Agent performs hybrid vector--graph retrieval (GraphRAG); the Generation Agent produces evidence-referenced recommendations and explanations; and the Validation Agent applies multi-tier verification and targeted remediation. Together, these agents provide an auditable and reliable workflow for end-to-end housing consultation. We evaluate HabitatAgent on 100 real user consultation scenarios (300 multi-turn question--answer pairs) under an end-to-end correctness protocol. A strong single-stage baseline (Dense+Rerank) achieves 75% accuracy, while HabitatAgent reaches 95%.

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