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This Moonshot Catalyst brings operational evidence, telecom knowledge and evidence-checked reasoning together for traceable RCA. Singtel reports reducing diagnosis from about one hour to five minutes.
Turning fragmented network evidence into faster, explainable recovery
Mobile Core services depend on both network functions and the cloud infrastructure beneath them. When a service issue occurs, the initiating fault may sit in either domain, while its effects can spread through failover, topology and service dependencies. Engineers must correlate performance indicators, alarms, logs, configuration information and operational knowledge before they can determine what happened and what should happen next. Because an incorrect change in the Mobile Core can disrupt critical connectivity for many subscribers, Phase 1 deliberately keeps corrective action under operator control.
That investigation is often slowed by fragmented tooling and evidence. Engineers may need to assemble data from several systems before they can test a likely cause, increasing investigation effort and delaying recovery.
The Moonshot Catalyst project, Knowledge-driven root cause analysis for faster recovery, is championed by Singtel, Optus, NTT DOCOMO and Telkomsel and delivered by Ericsson, Red Hat and Makman. The filmed demonstration uses anonymised Singtel operational data to replay a PDU session establishment failure on a core node, with a corroborating check of the hosting cloud infrastructure. The approach was separately reviewed by Singtel Mobile Core engineers using operational incidents from November 2025 and March 2026, followed by calibration on live data. Phase 1 covers Awareness and Analysis: identifying the supported cause, showing its evidence trace and presenting the recommended action, which the operator authorizes and performs.
A DataOps platform ingests and normalizes performance measurements, alarms, logs and relevant configuration data from the 5G Core and cloud environment. Inventory information can provide authoritative topology context. Machine-learning models detect abnormal KPI behaviour and publish an enriched anomaly identifying the affected entities and time window. This gives the RCA agents a defined starting point for investigation. They use the ontology-driven Knowledge Plane to retrieve relevant evidence, examine network topology and service dependencies, and test candidate causes against engineering rules. The workflow then produces an evidence-backed root-cause assessment, with a traceable reasoning path, or returns an inconclusive result if the available evidence is insufficient.
An ontology-driven Knowledge Plane links live operational evidence with network topology, service dependencies, product documentation, historical incidents, policies and engineering rules. The ontology defines what network functions, interfaces, services and KPIs mean and how they relate (TBox), while the knowledge graph connects these concepts to actual network entities and dependencies (ABox). This allows RCA agents to follow relevant KPI and dependency paths and apply consistent meaning across network and cloud domains. However, topology and graph relationships provide context rather than proof of causality: that requires supporting, time-stamped evidence from alarms, logs and other operational data. Specialist root cause analysis (RCA) agents retrieve this bounded evidence and use an explicit, versioned decision function to validate candidate causes through a governed workflow:
A sovereign MaaS/LLM layer supports natural-language requests and summarizes the findings within the operator environment, but it does not make the final RCA decision. Ericsson contributes the DataOps, Knowledge Plane and agent architecture; Red Hat provides the OpenShift-based sovereign platform and AI infrastructure; and Makman, the consulting partner for autonomy and TM Forum standards, defines the autonomy statement task by task against the Core Network Fault Management scenario (GB1523B, scored per IG1252), positions the solution in the Autonomous Networks reference architecture (IG1251) and shapes the Knowledge Plane as a contribution candidate to the TM Forum Autonomous Networks projec
The project uses the following TM Forum assets:
Singtel Mobile Core professionals reviewed the results using operational incidents from November 2025 and March 2026, followed by calibration on live data. Diagnosis time fell from about 60 minutes to 5 minutes, while recovery time decreased from 2 to 3 hours to 60 minutes, with the operator still deciding and executing the corrective action. The solution has run in controlled production at Singtel since September 2026, with one authorised user and session on limited data. In addition to reported diagnosis and recovery times, the team is evaluating RCA accuracy, engineer effort and evidence coverage as adoption expands.
“This Catalyst solution is valuable because it shows how fragmented network signals can be transformed into connected, reusable knowledge for faster and more explainable fault investigation. By combining telecom ontology, a semantic knowledge graph and deterministic agentic reasoning, it can help operations teams move from manually assembling evidence towards a traceable diagnosis in minutes rather than hours,” said Louis Lai, Senior Director, Group Strategy, Engineering & Innovation at Singtel, and Mohit Mathur, Director, Autonomous Networks at Singtel Optus Pty Limited.
For communications service providers, faster and more consistent fault isolation could shorten service disruption and improve the resilience of critical connectivity. For the wider industry, the Catalyst demonstrates a method grounded in TM Forum standards for converting fragmented network data into connected, reusable knowledge. The Knowledge Plane and telecom ontology are designed to extend across network domains and assurance use cases, reducing duplicated point solutions and supporting progressive autonomy.
Phase 1 keeps corrective action under operator control: the recommended action is presented, and the operator authorises and performs it. Phase 2 aims to add impact assessment, intent and delta detection where relevant, remediation proposals, policy and risk evaluation, and automated actuation. Any future actuation would require policy checks, approval where needed, verification of the result and a rollback or escalation path. Reusing the same data and knowledge foundation, this staged approach provides a pathway towards trusted closed-loop operations without presenting future capabilities as current results.
Meet the team at Innovate Asia in Bangkok, 17-19 November 2026, at the Autonomous Networks innovation zone kiosk.
