Telecom operators are racing to advance toward AN4-level autonomous networks, yet they face severe challenges: delayed user perception assurance, where passive manual responses fail to predict issues in advance, and slow complaint handling continues to erode customer satisfaction; imprecise fault analysis, making it difficult to reasonably prioritize critical issues amid redundant work orders; and inefficient fault handling, with low accuracy in root cause identification, siloed systems, and unvalidated solutions. These not only prolong the Mean Time to Repair (MTTR) but also threaten network stability. Addressing these challenges will deliver significant business value: drastically reducing operational costs by minimizing manual operations, enhancing network reliability through faster and more accurate fault resolution, and strengthening competitive advantages by upgrading service quality. For users, this means fewer network disruptions, quicker problem resolution, and consistently superior experiences—turning dissatisfaction into trust. This project integrates signaling analysis large models, spatiotemporal analysis large models, multi-agent collaboration, and digital twin technology, shifting the focus of operations from "network-centric" to "customer and business-centric." It enables proactive issue prevention, automated end-to-end cross-domain process closure, and risk-controllable solutions, reshaping the operation model to bring "ultimate user perception" to both operators and customers. The core value of operators' transition to L4 autonomous networks lies in achieving proactive and automated operations. However, the current passive, incident-driven O&M model keeps customer complaints high, with three key issues: 1. Lagging user perception assurance: Inadequate optimization of service quality improvement processes and weak ability to locate quality issues result in reactive operations that fail to "identify problems before users". Meanwhile, manual, lengthy complaint handling with bottlenecks leads to inefficiency and reduced customer satisfaction. 2. Inaccurate fault analysis: Massive alarms and work orders lack metrics for assessing impacts on services and user perception, treating all equally. The phenomenon of "one fault generating multiple orders" also exists, failing to prioritize critical fault handling. 3. Inefficient fault disposal: Low accuracy in root cause identification, over-reliance on expert experience for solutions, lack of automatic collaboration between cross-domain systems, and absence of simulation verification not only waste time and effort but also cause misjudgments. This leads to long Mean Time to Repair (MTTR), uncontrollable network operation risks, and impacts on network quality and customer service guarantee. These issues not only increase O&M costs but also directly affect customer retention. For instance, the churn rate of high-value users has risen year-on-year due to undetected service quality issues. Addressing these challenges will reshape the competitiveness of Communication Service Providers (CSPs): Signaling and spatiotemporal analysis models can enhance problem prediction accuracy; multi-agent collaboration enables "minute-level" closed-loop handling of cross-domain faults; digital twin verification reduces operational risks. For operators, users' demand for a "seamless network experience"—consistently stable and smooth service—has become core. Traditional "firefighting" O&M not only consumes resources but also erodes user trust, a fatal flaw in the digital era. For vertical industries like finance and healthcare, a more stable network will accelerate the implementation of their digital services, ultimately achieving value co-creation between CSPs and industry clients.