59 2026 DIRECTORY | CHANNELVISION MSPs and enterprise IT teams face constant pressure to maintain uptime, performance and security across increasingly complex environments. Despite investing heavily in monitoring tools, many organizations still struggle with limited visibility, inconsistent alerting and decisions built on assumptions rather than verified data. The underlying issue isn’t a lack of information. After all, modern networks generate massive volumes of telemetry across devices, circuits, applications and cloud services. The challenge is interpreting data quickly and determining what requires action. Here’s a closer look at why alert fatigue persists, and how Netverge helps security teams cut through the noise and focus on what matters most. The Cost of Noise & Sprawl In many environments, different systems will produce alerts independently. Warnings, informational messages and critical failures all arrive through the same channels, often without correlation or prioritization — leaving engineers to sift through noise to identify what matters. As a result, critical events are frequently buried among non-critical alerts. Response times may be slow, or issues will go unnoticed until a customer reports a problem. What begins as a monitoring challenge quickly becomes a service issue. When teams are consistently overloaded, confidence in monitoring systems erodes and responsiveness declines, even when the underlying data is accurate. Alert fatigue is compounded by tool and data sprawl. Monitoring platforms, ticketing systems, email inboxes, chat applications, dashboards and system logs each hold fragments of context. Engineers must manually piece together information before meaningful troubleshooting can begin. Before a single diagnostic step is taken, valuable time is spent answering basic questions: What is impacted? Are multiple alerts related? Has this happened before? Who is already working on it? In complex environments, assumptions replace certainty, increasing risk and delaying resolution. Different Model for Monitoring Netverge was built specifically to address these challenges by unifying monitoring, ticketing, automation and AI into a single operational platform. Rather than treating alerts, tickets and diagnostics as separate functions, Netverge connects them into one continuous workflow. “Most ticketing systems were built to log problems, not to help teams work through them together,” explained Netverge CTO and co-founder Hamed Zolghadri. “Netverge was designed to give engineers clarity from the moment an issue appears, not after hours of investigation.” Within Netverge, tickets are not static records. They function as live collaboration workspaces where engineers, managers and automated systems operate together. Events, assets, services, contacts and historical context are automatically linked, ensuring that everyone works from the same understanding of scope and impact. Ticketing Built on a Knowledge Graph At the foundation of Netverge’s approach is a unified client knowledge graph. Services, assets, locations, vendors and dependencies are mapped and anchored directly to each issue. “Traditional ticketing systems are linear,” Zolghadri added. “They capture conversations, but they do not capture context. Netverge starts with context, so teams are not forced to reconstruct it.” Because tickets are grounded in a knowledge graph, both engineers and AI systems operate from verified facts rather than assumptions. Telemetry, historical behavior, related assets and recent changes are immediately visible. This shared context allows AI to interpret diagnostic data more accurately and surface meaningful insights. Netverge uses AI-powered agents to continuously gather telemetry, validate conditions and check for resolution. Collector agents ingest data. Diagnostic agents analyze patterns. Troubleshooting agents execute structured checks. Overseeing this activity is an AI-driven meta-agent that coordinates actions across the ticket lifecycle. As evidence is collected, the meta-agent evaluates results, adds context and determines whether additional checks are required or human intervention is needed. “The meta-agent creates structure,” Zolghadri explained. “It ensures that everyone, including the AI, is working from the same ground truth and that effort is coordinated rather than duplicated.” By learning from prior incidents and outcomes, Netverge’s AI improves over time, reducing noise and accelerating resolution. MSPs using Netverge report reduced alert volume, faster resolution and more consistent collaboration. Engineers spend less time debating what the problem is and more time solving it. Handoffs are smoother because actions are recorded against shared context. For clients, this translates into clearer communication and greater confidence. When customers see accurate updates based on real data, trust increases. Netverge helps MSPs become the source of truth, not just a support desk. In 2026, Netverge will expand its platform with additional AI agents, deeper API integrations and more advanced automation. The focus remains on helping MSPs move away from reactive operations and toward coordinated, transparent service delivery. “The industry is shifting from managing noise to managing understanding,” Zolghadri concluded. “Netverge was built to support that shift.” o Jim Gurol is CEO and co-founder of Netverge Turning Down the Noise Netverge’s AI-assisted monitoring system reduces alert fatigue and supercharges productivity By Jim Gurol
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