I know this might sound crazy, even crazier if you consider that for the last few decades every single network team has been trying to find the best way to reduce the number of alerts received on their ticketing/incident tool.

But reality is most of these teams focus on the wrong side of this issue, because when you are focused on reducing the signals that the network sends when telling you there is a problem on the network, you will end up missing critical issues.

The Visibility Gap Behind the “Alert Fatigue” Story

If you ask NetOps leaders what their #1 challenge is, they often say, “It takes too long to solve problems, we have too many alerts and it’s difficult to know what is important and what is not.”

But when you dig deeper, the real problem is not the quantity of alerts—it is the lack of resources to triage all those alerts, and because of that these teams spend a lot of time fine tuning alerts to reduce the number of incidents created.

With this approach many real incidents never surface at all because those fine-tuned thresholds are too conservative, systems are not monitored, or events are automatically filtered out before they reach a human. In networks, reducing the alert count often means trading signal for silence. The team ends up “less firefighting” on paper, but unaware of important problems that are already affecting the business.

So, the core need is not to reduce alerts but increase visibility and automate the alert validation process.

The more signals we get from the network, the fewer incidents we will miss

The Human Cost of “Good Enough” Visibility

Here’s the catch: if you scale up visibility, you also scale up the work required to validate those alerts. For each new alert, someone is asking: Is this real? Is it impacting users? Is it a one‑off blip or the start of a real incident?

This validation work eats up hours in dashboards, runbooks, and manual cross‑checks. It is exhausting and error‑prone. The answer from teams is to reduce “noise”, even if that noise contains vital signals to detect critical issues.

A Better Goal: Generate More Signals and Validate Automatically

NetOps does not need to reduce alerts, it needs:

1. Generate more signals and alerts – cast a wider net so that potential problems are surfaced early, not hidden by aggressive filtering.
2. Automatically validate whether an alert is real or not – examine every single alert to confirm the impact and severity.

But how can we validate more alerts when our teams are becoming smaller?

Automate signals based on threshold-less AI models (TSPulse)

AI for Networks: Automate Validation Through Agents

Think about this: imagine we had an “unlimited” number of engineers that could review, one-by-one, the alerts that we received, and validate or discard them accordingly based on their analysis.

That is what AI agents can do for us, the same job that a network engineer would do: review topology, look at traffic patterns, compare current behaviour with the baseline, check online for similar symptoms… All this can be replicated with AI agents that use network-native pretrained models that are able to this inferencing for us in seconds.

For example, an AI agent can see that a spike in errors on a specific interface is part of a scheduled maintenance window and discard that alert, while flagging a subtle but sustained increase in latency on a core link as a likely real incident.

With these agents, the more signals/alerts you get from the network, the fewer critical events you will miss. And all this without the need of expanding your network team.

Alert validated automatically by AI agents

Putting It All Together: A Practical Vision for NetOps

Taken together, these four points describe a more mature operating model for NetOps:

• Stop optimizing for “fewer alerts.” Optimizing purely for alert count leads to hidden incidents and a false sense of stability.
• Start optimizing for “full visibility.” You want to see all meaningful events, not just the ones your rules were clever enough to surface.
• Automate validation, not just suppression. Use AI agents to check analytics, topology, inventory, past incidents… and automate to tell you which alerts are real and which are not, so humans can focus on the real incidents.
• Use correlation to group, not to hide. Grouping by root cause helps teams remediate faster and understand the impact pathway, but it should not erase important signals from the record.

Alert correlation is not used to filter out alert, but to group incidents based on the same root cause