Modern web dashboards and enterprise applications increasingly rely on complex, distributed microservices architectures. While these architectures offer scalability, they introduce significant challenges in debugging and observability. When failures occur, they often manifest as opaque error messages to the end-user such as Something went wrong. This masks the underlying root cause which may reside in browser side exceptions, API contract violations, or server side logic failures. Existing monitoring tools capture these events in isolation but fail to correlate them effectively or provide intelligible explanations to non technical users. This paper proposes a novel system for Automated Multi Source Debugging and Natural Language Error Explanation. The proposed framework automatically collects and correlates error data from disparate sources such as browser, API, server logs and validates API contracts in real time, and utilizes Large Language Models to generate natural language explanations. This approach significantly reduces Mean Time to Resolution for support engineers and improves the user experience by transforming cryptic error codes into actionable insights.

翻译：现代网络仪表盘和企业级应用日益依赖于复杂的分布式微服务架构。尽管这类架构提供了可扩展性，但也为调试与可观测性带来了显著挑战。当故障发生时，它们通常仅向终端用户呈现诸如“发生错误”之类的不透明错误信息。这掩盖了可能存在于浏览器端异常、API契约违反或服务器端逻辑故障中的根本原因。现有监控工具虽能独立捕获这些事件，却无法有效关联它们或向非技术用户提供易于理解的解释。本文提出了一种用于自动化多源调试与自然语言错误解释的新型系统。该框架自动收集并关联来自浏览器、API、服务器日志等不同来源的错误数据，实时验证API契约，并利用大型语言模型生成自然语言解释。该方法显著缩短了支持工程师的平均解决时间，并通过将晦涩的错误代码转化为可操作的见解，提升了用户体验。