This Catalyst advances Level 4 autonomous networks through agent-led, zero-touch workflows that enable fully automated, end-to-end operations. As CSPs scale digital infrastructure, complexity and operational demand increase. Intelligent agents can address this challenge by orchestrating tasks with minimal intervention, reducing cost and accelerating resolution. The solution consists of two use cases. The first is a fault handling agent which provides real-time network situational awareness and automated remediation. By processing high-volume data in context, it identifies issues early, executes corrective actions, and significantly reduces mean time to repair. The second, a wireless network optimization agent, adapts to live network and user conditions, executing precision adjustments that improve signal quality and reduce manual tuning. These agents operate as an execution framework layered with AI, task planning, and domain knowledge. Unlike standalone AI models, agents manage goal-setting, resource selection, and tool orchestration across closed-loop processes. This 'lights-out factory' approach transforms network operations—removing routine human input while maintaining transparency and control. The project applies and contributes to TM Forum assets, including the Autonomous Networks (AN) series. Agent-led workflows enable faster, more accurate fault resolution, reduce service interruptions, and improve network efficiency. They also support targeted, localized optimization at scale—enhancing user experience without increasing operational burden. Performance will be assessed through KPIs such as reduced MTTR, improved coverage, increased throughput, and lower complaint volumes. By deploying AI agents as decision-making and execution tools, this Catalyst demonstrates a viable path to scalable Level 4 autonomy—unlocking a new standard for operational agility, service reliability, and network intelligence.