Traditional incident management systems struggle to manage today's increasingly complex network environments. Reliance on manual processes cause delays in incident resolution and prolonged downtime, along with higher operational costs and limited scalability. Such service disruption reduces customer trust and increases churn. Yet in today’s hypercompetitive market CSPs must increasingly differentiate themselves through superior service reliability and rapid issue resolution. This Catalyst will therefore address the rising demand for highly automated incident management solutions. Building on the success of the Incident Co-Pilot developed in Phase I, the project introduces a more comprehensive multi-agent system incorporating advanced generative AI (genAI). The new multi-agent system is designed to automate diagnosis, resolution, optimization, and learning processes. This will bring the solution to Level 4 autonomy while contributing to TM Forum best practices and autonomous network standards. The project goals are to significantly reduce mean-time-to-repair (MTTR), measurably enhance customer satisfaction, and reduce operational costs. The specialized agents include: • Incident Agent to diagnose issues using real-time and historical data • Healing Agent to manage tickets and corrective actions • Optimization Agent to maintains network performance during incidents • Learning Agent to use past incidents to enhance future responses • Risk Agent to identify and mitigate potential issues proactively These enhanced agents will automate the incident lifecycle, reducing manual interventions and providing the capacity needed for large-scale deployments. Extensive demonstrations over the course of the project will highlight actionable insights, illustrating the system's scalability and potential real-world impact. Success metrics include MTTR reduction, automation rate, incident resolution accuracy, reduction in field dispatches, user satisfaction and trust assessed through NOC engineer feedback.