@article{Zaitsev2026, 
author = {Artem Zaitsev and Tatiana Kisel},
title = {Digital autonomation of residential construction project lifecycle management via agentic-AI-driven PLM system},
year = {2026},
journal = {Journal of Intelligent Construction},
volume = {4},
number = {2},
pages = {9180130},
keywords = {multi-agent system, PLM system, secure multi-protocol orchestration, digital autonomation, residential construction, agent-to-agent protocol, model context protocol},
url = {https://www.sciopen.com/article/10.26599/JIC.2026.9180130},
doi = {10.26599/JIC.2026.9180130},
abstract = {Residential construction projects are characterized by high complexity, fragmented communication, and vulnerability to delays and budget overruns due to inefficient manual coordination. This paper presents Civil2PM, an adaptive, on-premises multi-agent project lifecycle management (PLM) system built to autonomously oversee the entire life cycle of residential construction projects. The system is designed to ensure robust cyber-security, maintain cost-efficiency, and seamlessly integrate with existing building information modeling (BIM) and enterprise resource planning (ERP) infrastructure. It employs compact yet high-performance large language models (LLMs), Phi-4-14B, and Qwen3-30B-A3B, running locally to ensure both speed and data sovereignty. These models are orchestrated through the LangChain framework, enhanced with agentic retrieval-augmented generation (ARAG), and supported by a dual-protocol architecture comprising the agent-to-agent (A2A) and model context protocol (MCP). The A2A protocol enables secure, structured communication among specialized artificial intelligence (AI) agents (initiation, tracking, and reporting), while MCP provides standardized and isolated access to enterprise data sources. Civil2PM was trained and contextualized on 3500 real-world residential construction project cards, enabling it to autonomously generate project plans, track progress, issue communications, and update databases. By automating these processes, the system significantly reduces human errors in project coordination. A multi-phase evaluation with 11 medium and large construction enterprises demonstrated the system’s capability to reduce coordination latency, improve schedule adherence, and eliminate dependence on costly cloud application programming interface (APIs). This work contributes a novel agentic-AI-driven architecture for residential construction sector, merging compact LLMs, multi-agent coordination, and secure local deployment to address pressing economic and operational challenges.}
}