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Knowledge-driven root cause analysis for faster recovery

URN C26.5.1004

Topics AI (Artificial Intelligence), Autonomous networks, Fault management

Moonshot Catalyst - Singtel is pioneering knowledge-driven, agentic Mobile Core Network RCA, demonstrating how operational data, network knowledge and AI agents can work together to move fault resolution from hours to minutes.

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From Data to Resolution: Agentic AI and the Network showcases how communications service providers can move beyond traditional monitoring and manual fault investigation to a new era of intelligent, knowledge-driven operations. As networks become increasingly complex, resolving issues quickly and accurately remains heavily dependent on specialist expertise, resulting in slower response times and inconsistent outcomes. This Catalyst addresses that challenge by transforming operational data into actionable intelligence, enabling faster root cause analysis (RCA), improved service resilience, and more consistent customer experiences. At the core of the solution is an innovative knowledge plane that continuously captures alarms, topology data, service intent, incident records, and operational expertise into a semantic knowledge graph. By connecting previously fragmented data sources across network and IT domains, the platform creates a shared source of operational intelligence. Agentic AI-powered RCA assistants can then reason over this knowledge in near real time, identifying causal relationships, assessing service impact, and recommending the most effective resolution actions based on historical experience and current network conditions. What makes the Catalyst truly innovative is its combination of ontology-driven reasoning, knowledge management, and agentic AI orchestration. Instead of analysing faults in isolation, the system leverages reusable organisational knowledge, known remediation playbooks, and contextual network intelligence to accelerate diagnosis and recovery. The result is an explainable and trustworthy approach to automation, where engineers can understand the reasoning behind recommendations while progressively increasing the proportion of faults that can be resolved with minimal human intervention. The business value is substantial. By reducing Mean Time to Detect (MTTD) and Mean Time to Repair (MTTR), CSPs can minimise service disruption, protect revenue, and reduce the operational costs associated with major incidents. Improved RCA accuracy lowers the risk of repeat faults and misdiagnosis, while cross-domain visibility removes silos between RAN, core, transport, OSS/BSS, and IT environments. Aligned with TM Forum standards and designed to remain vendor-agnostic, the Catalyst provides a practical pathway from data-driven network operations to autonomous, scalable, and knowledge-powered fault resolution.