Multi-Agent AI and Network Knowledge Graphs for Change Management

Explore an innovative conference talk that addresses the limitations of traditional ticketing and testing workflows in network operations and change management. Learn how the fragmented approach of independent systems leads to delayed resolutions, repeated incidents, and stakeholder dissatisfaction. Discover a cutting-edge solution that combines natural language processing from IT Service Management (ITSM) systems with multi-agent reasoning and dynamic context from live knowledge network graphs. Examine an end-to-end architecture where natural language intents from ITSM tickets integrate seamlessly with expert AI agents for complex workflow tasks, supported by continuous network knowledge graph ingestion pipelines. Analyze a detailed production case study demonstrating how agentic reasoning combined with dynamic network knowledge graph contexts significantly enhances critical validation and workflow interactions. Review showcased results highlighting dramatic improvements in ticket resolution efficiency, network testing accuracy, and overall execution quality that deliver tangible value to both technical teams and business stakeholders.