Modern telecom networks face persistent challenges in fault detection and resolution due to fragmented operational data and loosely coupled system dependencies across heterogeneous domains. Traditional monitoring systems, reliant on inventory data traversing multiple intermediaries, suffer from latency, inconsistency, and contextual loss—hindering effective impact analysis. Challenges with heterogenous domain: * Fragmented Operational Data: Data related to devices, services, and customers is scattered across multiple systems, making it hard to maintain consistency and context. * Limited Cross-Domain Visibility: Faults that span across network domains are difficult to analyze due to loosely coupled telemetry and topology awareness. * Ineffective Impact Analysis: Without unified and real-time context, assessing the impact of faults becomes unreliable and slow. * Manual Resolution Bottlenecks: Lack of automation and intelligent correlation leads to longer mean time to repair (MTTR) and reduced service reliability. This Catalyst focuses on a transformative Agentic AI framework for intelligent fault correlation and autonomous resolution. The solution uses A2A/ACP,Model Context Protocol etc.. which enable dynamic synchronization of network inventory and topology across agents. This ensures real-time, context-rich fault and impact analysis and significantly enhances precision and responsiveness. The architecture adheres to the TM Forum Open Digital Architecture (ODA) , leverages AI to correlate cross-domain events and automate remediation, the solution aims to reduce Mean Time to Repair (MTTR) by ~ 25%-30% and elevates service reliability. "Our network spans multiple domains - Optical, IP, Power - each with its own tools and data silos. When issues arise, faults often cross these boundaries, making root cause isolation inefficient. By investing in cross-domain, AI-driven service assurance, we can cut false incidents and reduce mean time to repair by up to 50%, directly improving customer experience, and helping to improve Autonomous Network rating" – Utsav Jain, Senior Manager – Network Monitoring at BT The heterogenous nature of modern Fixed Network architectures has the consequence of making Service Assurance reactive, with lots of false positives and reduced Quality of Experience (QoE). Further complications result from siloed and static rule-based fault detections that lack service context. Finally, the fault resolution journey is based on decisions taken manually by network operators following sometimes outdated runbooks. The solution to the above problems lies in a holistic approach that can understand and leverage User Intent and provides AI-driven Unified Assurance with Multi-Domain Fault Correlation, real-time Network Insights, and Autonomous Resolution.