Abstract: The in-depth application of industrial AI agents, while enhancing efficiency, is confronted with severe challenges in safety governance. Based on this, taking the framework of artificial intelligence safety, trust, and responsibility (AI STR) proposed by the world digital technology institute (WDTA) as the core theory, in response to the three major pain points unique to industrial scenarios, namely the data-control island effect, the lagging behavior-risk cognition, and the lack of decision-responsibility traceability, three practical safety governance paths are systematically proposed:build a full-chain risk control tower, adopt multi-modal behavioral causal reasoning (STAR Framework), establish a verifiable decision traceability mechanism, and verify the effectiveness of the above governance paths through the scenarios of chemical production and logistics warehousing. In addition, three targeted countermeasures, namely protocol adaptive conversion, cross-chain authentication, and dynamic insurance pricing, are further proposed, aiming to provide theoretical support and practical paradigms for building a certified industrial artificial intelligence entity security ecosystem.