Current LLM-based root cause analysis tools for outages are unreliable because they lack deep understanding of proprietary code, are built by people unqualified to evaluate their output, and fail to incorporate sophisticated causal analysis methods.

Workarounds Described

• hackathon projects to automate outage analysis
• using toy 'known broken' apps for testing
• junior SREs using 'it worked last time' approach
• senior SREs manually applying 'what changed?' analysis
• manual Bayesian scoring calculations

Implied Software Gaps

• Automated RCA system with proprietary code understanding
• Testing framework that uses real new outages rather than toy examples
• Bayesian causal network automation for failure analysis
• Pattern clustering system that separates signal from noise in error logs

CodeAware RCA

A root cause analysis platform that combines LLM intelligence with deep code understanding and Bayesian causal networks. Unlike generic RCA tools, it builds a world model of your proprietary codebase and uses dimensional analysis before clustering failure hypotheses to identify true outage patterns.