Evaluating Multi-Agent AI Systems for Decision Support in the Operational Readiness of Heavy Industrial Facilities

This structured integrative review synthesizes evidence across five interconnected areas and develops an integrated framework for evaluating multi-agent AI in the operational readiness of heavy industrial facilities. The review examines multi-agent architectures and coordination, operational-readiness decision support, digital twins and virtual validation, safety and system assurance, and human–agent interaction and governance. From this synthesis, the study develops a reference architecture, a six-dimensional evaluation framework covering technical performance and reliability, coordination and context integrity, operational-readiness value, safety and assurance, human factors and governance, and implementation viability, scalability, and interoperability, together with a five-level maturity model. The framework considers both whether multi-agent AI can support assessment of facility readiness and whether the AI-supported decision system itself is sufficiently reliable, assured, governed, and mature for that role. Industrial evidence from Saudi Aramco and Bosch applications across Bosch and SICK manufacturing settings indicates practical value in maintenance planning, fault diagnosis, information synthesis, and production support. However, the available evidence is stronger for operational benefit and implementation feasibility than for coordination reliability, safety assurance, and lifecycle governance. Current systems are therefore best characterized as bounded, human-supervised decision-support tools rather than autonomous readiness authorities. Progress toward governed operational use will require stronger industrial benchmarks, traceable engineering evidence, reliable coordination, effective safeguards, lifecycle assurance, and clearly defined human decision authority.