cPacket Service Assurance: MTTR Reduction

When service disruptions or connection issues impact key applications, speed of diagnosis is everything. This session highlighted how cPacket enables real-time monitoring, anomaly detection, and triage using packet-level data. It showcased how IT teams can use LLM-powered interaction, Observability AI baselining, and SIEM integration to accelerate resolution, reduce MTTI/MTTR, and deliver a better user experience across distributed infrastructure and business-critical workflows. Erik Rudin, Field CTO, set the stage by describing a reactive scenario where users are experiencing application issues, and the network appears normal initially. Ron Nevo, CTO, presented a real-world example from a large bank where a specific branch experienced intermittent remote desktop access failures due to a WAN acceleration device adding significant latency. This underscored the challenge of pinpointing issues in complex, multi-hop network paths without pervasive monitoring.

cPacket’s approach to reducing MTTR involves enhancing the user experience through AI-powered interaction. Instead of manually sifting through logs and dashboards, network operators can “chat” with the system, asking natural language questions to gain insights into service performance. The LLM (Large Language Model), in conjunction with AI agents and the MCP (Model Context Protocol), helps to process and contextualize data. A crucial aspect is Observability AI baselining, where cPacket’s machine learning pipeline automatically establishes baselines for various network metrics, accounting for service, time of day, and day of week. This allows the system to identify deviations from normal behavior, even if not immediately surfaced as an alert, and visually present these anomalies against the baseline to the user.

While acknowledging that advanced network engineering knowledge is still valuable, the aim is to simplify the troubleshooting process. The system can identify logical and physical network topologies and pinpoint where latency or other issues reside within the path. This AI-assisted workflow accelerates triage by providing relevant data and insights, shortening the time to detect, understand context, and identify the responsible component or team. cPacket emphasizes that this integration with existing IT workflows–including SIEM, ticketing systems like ServiceNow, and communication platforms like Slack–is critical for achieving measurable outcomes and continuous improvement in service delivery. The ultimate goal is to empower human operators with intelligent tools that streamline diagnostics and decision-making, rather than completely automating the resolution process.