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Learn how to dramatically reduce Mean Time to Resolution (MTTR) for network service disruptions through this 19-minute conference presentation from Networking Field Day 38. Discover cPacket's innovative approach to real-time network monitoring and troubleshooting that combines packet-level data analysis with AI-powered tools to accelerate issue diagnosis and resolution. Explore how IT teams can leverage LLM-powered natural language interactions to query network performance data, eliminating the need to manually sift through complex logs and dashboards. Understand the implementation of Observability AI baselining, where machine learning algorithms automatically establish performance baselines accounting for service patterns, time of day, and day of week variations to identify anomalies that might not trigger immediate alerts. Examine a real-world case study from a large bank where intermittent remote desktop access failures at a specific branch were traced to WAN acceleration device latency issues, demonstrating the challenges of pinpointing problems in complex multi-hop network paths. See how the system identifies logical and physical network topologies to precisely locate where latency and other issues occur within network paths, streamlining the triage process. Learn about integration capabilities with existing IT workflows including SIEM systems, ServiceNow ticketing platforms, and Slack communication tools to ensure seamless adoption and measurable outcomes. Gain insights into how AI agents and the Model Context Protocol (MCP) work together to process and contextualize network data, empowering human operators with intelligent diagnostic tools while maintaining the value of advanced network engineering expertise in the resolution process.
