Reliability, Debugging, and Observability for Distributed AI Systems: Frameworks, Challenges, and Performance Evaluation

Author(s)	Chandana Ashok Naik
Country	India
Abstract	Distributed AI systems power modern applications such as large-scale language models, recommendation engines, and autonomous platforms. However, their complexity introduces reliability risks, debugging challenges, and limited observability. This study proposes an integrated framework for improving reliability, debugging efficiency, and observability in distributed AI environments. Using experimental evaluation across simulated AI workloads, system-level metrics were collected to assess failure detection, fault isolation time, and system recovery performance. Results indicate that structured observability practices significantly improve system reliability and reduce mean time to resolution (MTTR). The findings highlight the importance of unified monitoring architectures in AI infrastructure.
Keywords	Distributed AI, Reliability Engineering, Debugging, Observability, AI Infrastructure
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-02-09

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International Journal For Multidisciplinary Research