Beyond Correlation: Finding Root-Causes using a network digital twin graph and agentic AI

When your network fails, finding the root cause usually takes hours of investigations, going through correlated alarms that often lead to symptoms rather than the actual problem. Root-cause analysis (RCA) systems are often built on hardcoded rules, static thresholds, and pre-defined patterns that work great until they don’t. Whether you’re troubleshooting network-level outages or service-level degradations, those rigid rule sets can’t adapt to cascading failures and complex interdependencies.