OptAgent: an Agentic AI framework for Intelligent Building Operations
Zixin Jiang, Weili Xu, Bing Dong

TL;DR
This paper introduces OptAgent, an agentic AI framework utilizing physics-informed machine learning for scalable, intelligent building energy management, demonstrating multi-agent coordination and benchmarking performance in real-world scenarios.
Contribution
It presents a novel modular AI environment with specialist agents and tools for comprehensive building energy modeling, control, and automation, advancing autonomous building operations.
Findings
Multi-domain, multi-agent coordination improves energy efficiency and comfort.
Benchmarking reveals impacts of model size and task complexity on performance.
Systematic evaluation guides future development of agentic AI in buildings.
Abstract
The urgent need for building decarbonization calls for a paradigm shift in future autonomous building energy operation, from human-intensive engineering workflows toward intelligent agents that interact with physics-grounded digital environments. This study proposes an end-to-end agentic AI-enabled Physics-Informed Machine Learning (PIML) environment for scalable building energy modeling, simulation, control, and automation. The framework consists of (1) a modular and physics-consistent PIML digital environment spanning building thermal dynamics, Heating, Ventilation, and Air Conditioning (HVAC), and distributed energy resources (DER) for grid-interactive energy management; and (2) an agentic AI layer with 11 specialist agents and 72 Model Context Protocol (MCP) tools that enable end-to-end execution of multi-step energy analytics. A representative case study demonstrates multi-domain,…
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Taxonomy
TopicsBuilding Energy and Comfort Optimization · Integrated Energy Systems Optimization · Smart Grid Energy Management
