This Snowflake-sponsored master class explores how telecom and enterprise organizations can reimagine the Network Operations Center (NOC) through agentic AI moving from reactive operations toward intelligent, coordinated systems that detect, reason, remediate, and continuously learn.
The session will cover the operational challenges facing today’s NOCs, the emerging standards and frameworks enabling agentic operations, and the practical building blocks required to create a multi-vendor Agentic NOC using Snowflake Intelligence and Cortex. Participants will also see how specialized agents can collaborate around business intent, incident response, root cause analysis, and customer experience outcomes—ultimately reducing unnecessary truck rolls and improving operational efficiency.
Agenda:
1. Reimagining the NOC: Why Change is Needed
- Current challenges in network operations
- Operational complexity across vendors, tools, and domains
- Why traditional NOC models struggle to scale
2. Standards, Frameworks, and Foundations for Agentic Operations
- Industry standards shaping AI-driven network operations
- AI frameworks, artifacts, and APIs required for deployment
- Interoperability across multi-vendor environments
3. Defining the Building Blocks: Agents, Tools, and Skills
- Specialized agents required in a multi-vendor NOC
- Tooling and skills artifacts needed for orchestration
- Human oversight and expert guidance models
4. Hands-On Build: Creating an Agentic NOC on Snowflake
Using Snowflake Intelligence + Cortex to enable:
- Specifying business intents
- Coordinating specialized NOC agents
- Detecting and analyzing incidents
- Performing root cause analysis (RCA)
- Driving remediation workflows
- Learning from historical incidents to improve future response
5. Live Demonstration: From CX Incident to Resolution
- Walkthrough of a customer experience-impacting network incident
- How agents collaborate to respond in real time
- Reducing truck rolls through intelligent decisioning and targeted action
- 6. Q&A
Register Here