DemandAgent: Towards an Embodied Agent Harness for Everyday Human Demands

Real-world embodied assistance often begins with a high-level human demand rather than a fully specified task. Existing embodied navigation settings typically ask agents either to reach a specified object goal or to navigate by following detailed route instructions. We argue that satisfying real-world human demands goes beyond localizing relevant targets and instead requires transforming high-level demands into executable embodied behaviors while considering inherent task dependencies. We introduce DEMANDNAV, a dependency-aware task formulation that represents a human demand as functional requirements and their prerequisite relations. To address this task, we present DEMANDAGENT, an embodied agent harness built around dedicated MCP tools for embodied interaction and persistent task-state and spatial-state management, guided by specialized embodied agent skills. We further introduce a top-down data construction pipeline and instantiate DEMAND-BENCH with 200 episodes from distinct ProcTHOR scenes. On DEMAND-BENCH, DEMANDAGENT achieves a 52.5% success rate, substantially outperforming adapted navigation baselines with static LLM-based demand decomposition and goal translation. Further evaluation on the public EB-Navigation benchmark shows that an adapted DEMANDAGENT achieves the best performance among the evaluated baselines, with a 65.7% average success rate, demonstrating the cross-benchmark adaptability of the proposed embodied agent harness. Code and data will be made publicly available.