DNS Resolution Insight System (DRIS): An AI-Augmented Approach for Root Cause Analysis and Live Site Debugging

Abstract

The Domain Name System (DNS) is a vital part of the modern internet infrastructure, yet DNS outages tend to appear as severe outages that percolate through distributed application topologies. Existing DNS debugging work is dependent on separate tools, including packet captures, DNS query replay, and log searches, that offer only a woefully inadequate basis to be able to debug complicated, multi-tier infrastructure failure events. The DNS Resolution Insight System (DRIS) addresses these challenges by aggregating heterogeneous log sources from recursive resolvers, forwarding layers, proxy services, and failover systems into unified analytical objects. The system provides standardized commands for investigating resolution failures, timeout anomalies, and domain-specific issues while maintaining operational security boundaries. DRIS extends beyond conventional log correlation through Model Context Protocol integration, enabling AI-augmented debugging workflows that support natural language query interfaces. This conversational debugging capability transforms manual log correlation processes into intuitive, interactive sessions that reduce cognitive burden during high-pressure operational incidents. The platform accommodates diverse deployment models from localized development environments to enterprise-grade multi-tenant services supporting distributed engineering teams. Performance validation demonstrates DRIS's effectiveness across various scenarios, including incident triage and regression detection, establishing the system as a comprehensive solution for DNS infrastructure debugging and proactive monitoring capabilities.