Intelligent workflows for smart manufacturing reliability and maintenance (RxM) are transitioning from static, sequential multi-agent systems toward autonomous, dynamic decision-making. However, a critical gap persists between agent coordination and the holistic co-optimization required across predictive performance, computational resources, and domain-specific business logic. To address this, we propose AgiSphere-CORE, a novel framework centered on a Co-Optimization Core (COC). The COC maintains a unified Collaborative State Vector and solves a constrained optimization problem to maximize the Collaborative Utility J_co, dynamically orchestrating specialized agents for resources, models, and logic. This enables a paradigm shift from sequential to co-optimization-driven autonomy. Extensive evaluation on multiple manufacturing tasks, including classification, regression, and anomaly detection, demonstrates that AgiSphere-CORE consistently outperforms baselines, achieving superior predictive performance (e.g., 97.3% accuracy, 0.902 F1-score), improved resource efficiency (15% faster workflow time), and higher estimated domain cost savings ($148.7K). Ablation studies confirm the critical role of the COC and the adaptive learning mechanism. A detailed case study further illustrates the framework’s transparent, utility-driven trade-offs. We conclude that AgiSphere-CORE provides a robust, holistic foundation for next-generation autonomous RxM systems in smart manufacturing.