In today’s fast-evolving IT environments, managing alerts is a constant challenge. As networks expand and become more complex, traditional alert systems often generate more noise than clarity, making it harder to focus on the issues that matter most. ThousandEyes Adaptive Alert Detection reduces alert fatigue by using machine learning (ML) to surface critical issues and filter out irrelevant alerts, helping your team concentrate on what truly affects your network’s performance and user’s digital experience.
Static Alerts Struggle To Keep Pace With Dynamic Networks
Traditional IT alert systems can quickly become tedious and burdensome. They rely on rigid thresholds that aren’t able to keep up with the ever-changing nature of your network. This results in alert overload—where “cry wolf” events, or false positives, prompt teams to address minor problems while serious incidents go unnoticed because alert conditions are too narrow or unresponsive. Instead of streamlining operations, these static systems demand constant supervision and manual tuning, adding more work to already stretched IT teams.
Adaptive Alert Detection: A Smarter Approach
ThousandEyes Adaptive Alert Detection addresses the shortcomings by employing machine learning to create dynamic baselines to trigger alerts. These baselines adjust automatically by assessing real-time and historical data to provide alerts that are more accurate and relevant to the current state of your network. By evaluating multiple factors–such as geographic spread, frequency of anomalies, and severity–Adaptive Alert Detection delivers fewer but more meaningful alerts. This smarter approach cuts through the clutter, allowing your team to respond to issues quickly and effectively.
More Effective Alerts, No Reconfiguration Needed
A key strength of Adaptive Alert Detection is its ability to adjust in real time without manual intervention. Whether you're managing a fast-moving, high-risk environment or a more stable network, the system automatically fine-tunes alert baselines to reflect current conditions.
By dynamically adjusting thresholds, alerts are triggered when there’s a strong chance of a real issue. For example, at medium sensitivity, an alert might trigger when the probability of an issue exceeds 80% and will only clear when it drops below 20%. This eliminates the problem of “flapping alerts” caused by minor fluctuations and keeps alerts open until the issue is resolved. For a detailed breakdown on how the baseline is determined, you can check out this section of our product documentation.
This flexibility helps you retain control over alert sensitivity to your network’s unique needs. As a result, your team can shift from reacting to unnecessary alerts to proactively addressing potential issues before they escalate. For example, NOC engineers overseeing complex global infrastructures or IT operations teams focused on service reliability engineering (SRE) can harness Adaptive Alert Detection to gain clear, actionable insights into their network’s health and performance. By providing precise alerts backed by up-to-date data, teams can move faster and focus on maintaining performance rather than sifting through unnecessary data.
One customer reported a significant “reduction in noise levels,” which made alerts more actionable and helped streamline the entire monitoring process.
“We have migrated our SaaS monitoring probes on the ISP router agents to the Adaptive method and are observing a great reduction in the noise levels, making alerts more actionable.” — Principal Network Engineer, Large International Technology Company (Customer Preview Program Participant)
Now Available for Cloud and Enterprise Agents
ThousandEyes Adaptive Alert Detection is now available for Cloud and Enterprise Agents. To learn how to set up and customize it, visit our documentation. Join our upcoming webinar on new product features and release highlights or contact your account manager to see how ThousandEyes Adaptive Alert Detection can transform your alerting strategy and drive greater operational efficiency.
