Frequent toolchain updates and the expanding diversity of instruction set architectures (ISAs) have made large-scale system-level software package repair a critical task. Diagnosing and repairing build failures remains challenging due to heterogeneous failure evidence, complex dependency constraints, and architecture-specific build conventions. While recent LLM-based repair methods have shown promise for project-level source code fixes, they struggle with system-level repair where failures involve multi-language artifacts (e.g., build recipes, scripts, and source archives) and require iterative validation through external build services. In this paper, we first conduct a systematic empirical study of real-world system-level build failures. Our findings reveal that 72% of failures stem from dependency and environment misconfigurations rather than isolated code defects, suggesting that effective repair must prioritize packaging logic and iterative feedback. Motivated by these insights, we propose EvidenT, an evidence-preserving repair framework that decouples iteration-aware evidence management from tool execution. EvidenT comprises (1) an external Build Service for reproducible build execution and feedback; (2) an Evidence-Preserving Repair Controller that performs cross-modal fusion of repair history, knowledge context, and build artifacts; and (3) an automated Repair Orchestrator that executes a suite of modular tools for failure localization and system-level repair actions within a closed-loop validation environment. We evaluate EvidenT on a benchmark of 219 real-world RISC-V package build failures. EvidenT successfully repairs 118 packages (53.88%), substantially outperforming state-of-the-art agentic baselines (20.55%) and direct LLM-based repair (1.83%). To demonstrate its architectural generality, we extend EvidenT to legacy ISAs by updating only ISA-specific knowledge context. In preliminary experiments, it achieves success rates of 41.77% on aarch64 and 46.99% on x86_64, showcasing its robustness across diverse hardware ecosystems.