SolarWinds Bets on AI Agents to Fix Alert Noise — Here's What Actually Stops the 2 AM Pages

SolarWinds just shipped Avior — a set of agentic AI agents that don't just tell network teams what's wrong, they execute the fix: running operational tasks, reducing alert noise, and resolving issues across on-prem and SaaS products, with administrators keeping control over what the agents are allowed to do autonomously. Interim CEO Cullen Childress framed it as the next step in a four- or five-year AI journey.

Read between the lines of that announcement and you'll find the industry's honest self-diagnosis: monitoring tools got very good at collecting telemetry and very bad at deciding what deserves a human being's attention at 2 AM. That's why "reduce alert noise" is right there in the pitch.

If you're the one carrying the pager for an internal IT team or an MSP NOC, you didn't need a Network World article to tell you that. You know exactly which server generates the 3 AM disk alerts that never matter. You know how it feels when the monitoring channel goes quiet and you realize you've trained yourself to ignore it. The question isn't whether agentic AI is coming — it's whether your alert pipeline will be worth automating by the time it arrives.

Here's the uncomfortable part: most of what AI agents promise to fix later, you can fix now. Because alert fatigue was never a volume problem. It's a signal quality problem.

The Problem: Your Monitoring Tool Sees Everything and Understands Nothing

Most legacy alerting — including plenty of alerting modules inside the big RMM platforms — was built on a simple, dumb architecture: metric crosses threshold, send email. That's it. No context about the device, no knowledge of what the metric looked like last month, no awareness that this "outage" happens every Tuesday during backup. The tool fires; a human is expected to do the understanding.

So the humans do the understanding, hundreds of times a week, until they stop. Here's how that actually plays out:

Alert storms bury the root cause. A hypervisor host reboots at 01:14. Every VM on it reports offline, every service on every VM reports down, every monitor attached to those services fires independently. One root-cause incident becomes 60+ alerts in nine minutes. The page about the actual problem — the host — is somewhere in the pile, and your on-call tech is triaging symptom #17 while the client's file server is still down.

Maintenance windows become alert windows. Patch Tuesday, 02:00, thirty servers reboot in sequence. The monitoring system screams for twenty minutes. Nothing is wrong. Do that three months in a row and your on-call tech has learned — correctly — that overnight alerts from those servers are meaningless. Then the night a file server actually dies mid-patch-window, the page gets dismissed like all the others.

Nobody deduplicates across tools. The Exchange store goes down. Your monitoring tool emails "Store service stopped." Your RMM, which has its own service monitor, opens a device ticket. A user opens a helpdesk ticket because Outlook is down. Now three systems hold three records of one incident, none linked, and your MTTR report — which requires stitching detection time from one database to resolution time from another in a spreadsheet — is fiction. If you're an MSP running NinjaOne or ConnectWise for endpoints, a standalone monitor for firewalls, and a separate PSA for tickets, you're living this three times over.

Escalation routes to a distribution list, not a person. The critical alert goes to it-alerts@. Everyone on the list assumes someone else saw it. Twenty-two minutes later someone asks in Slack, "hey, did anyone get a page about the DC?" SLA clock: already blown.

None of these are telemetry problems. You had the data in every case. The gap is context, correlation, and routing — and those gaps are architectural. Point-product tools keep alerts in one database, tickets in another, endpoint state in a third, and were never designed to connect them. The integrations that exist are webhook afterthoughts that sync status, not meaning.

The cost shows up in numbers every IT manager recognizes: minutes-to-hours of added MTTA on every incident because the first human action waits for someone to check email; duplicate ticket volume that inflates helpdesk queues by a third; SLA reports nobody trusts; and the attrition tax — experienced techs who quietly mute the monitoring channel, or leave, because being woken up for noise is worse than not being woken up at all.

What AlertMonitor Does Differently: Signal, Not Volume

AlertMonitor was designed around a specific insight: alert fatigue isn't a volume problem — it's a signal quality problem. The goal isn't fewer alerts for their own sake. It's making sure every alert that reaches a human is one that human can act on immediately.

Every alert carries full context. When AlertMonitor pages you, the alert includes the device, the client, what changed, and what healthy looks like — the baseline that device has held for weeks. Not "Disk C: 93%" but: SRV-FILE-02, Client: Contoso, C: climbed from a steady 71% baseline to 93% over four hours, trend projects full in roughly two days, with a direct link to the device and its history. The tech wakes up knowing what they're walking into before they open a single tool.

Smart deduplication collapses storms. The flapping switch and the rebooting hypervisor become one incident with an occurrence counter — not 412 emails. Downstream symptom alerts get correlated to the root-cause incident instead of firing independently, so the on-call tech works one problem, not sixty.

Maintenance window suppression that fails safe. Schedule the patch window and expected alerts — reboots, service restarts, brief agent blips — are suppressed. Anything unexpected during the window still routes normally. Quiet windows that don't hide real failures.

Multi-level on-call routing with tracked acknowledgment. Escalation policies route by client, severity, and schedule. A critical Contoso alert at 02:00 pages the level-1 tech via push and SMS; unacknowledged in five minutes, it escalates to level 2, then to the manager. Acknowledgment is logged, so "I thought you had it" is a question the platform answers automatically.

One platform, one data model. Because monitoring, RMM, helpdesk, patching, and network topology live in the same product: an alert auto-creates a linked ticket, the ticket shows the device's monitoring history and patch state, and the tech runs remediation — restart a service, execute a script, push pending updates — from the alert itself. Detection, response, and resolution timestamps live in one database, which means SLA reporting is a filter, not a spreadsheet forensic project.

Put together, the before/after is stark. Before: monitoring email → someone notices 40 minutes later → open the RMM in a second tab → open the helpdesk in a third → create a ticket manually → connect → fix → update three systems. With AlertMonitor: contextual alert in under 90 seconds → one click opens device, ticket, and remediation together → fix → everything already recorded.

What You Can Do This Week

You don't need to wait for autonomous agents to shrink your alert volume. Here's where to start, whether you run AlertMonitor or anything else.

1. Find your noisiest alert sources and deduplicate them first. On Windows, the event log tells you exactly who's spamming you:

# Top repeat offenders in the System event log, last 7 days
Get-WinEvent -FilterHashtable @{LogName='System'; Level=1,2; StartTime=(Get-Date).AddDays(-7)} |
  Group-Object -Property ProviderName, Id |
  Sort-Object Count -Descending |
  Select-Object -First 10 Count, Name

Anything firing dozens of times per week is a deduplication candidate. In AlertMonitor, point a dedup window at it and one incident with an occurrence counter replaces the pile.

2. Stop blanket-alerting on stopped services. Half the "Automatic" services that are stopped on any given server are stopped by design. A blanket rule means guaranteed noise. Find the real ones:

# Automatic services that are not running — triage before you alert on them
Get-Service |
  Where-Object { $_.StartType -eq 'Automatic' -and $_.Status -ne 'Running' } |
  Select-Object MachineName, Name, DisplayName, Status

Build monitors only for the services that actually matter, with recovery checks — that's exactly what AlertMonitor's per-service monitors are for — instead of alerting on everything and trusting nothing.

3. Watch disk growth trends, not static thresholds. The classic 2 AM disk page is almost always preventable — the disk was screaming its trajectory for weeks. Get ahead of it:

# Disk headroom across all domain servers — find the next outage before it pages you
$servers = Get-ADComputer -Filter 'OperatingSystem -like "*Server*"' -Properties OperatingSystem |
  Select-Object -ExpandProperty Name

Invoke-Command -ComputerName $servers -ScriptBlock {
  Get-Volume | Where-Object { $_.DriveLetter } | ForEach-Object {
    [PSCustomObject]@{
      Server  = $env:COMPUTERNAME
      Drive   = $_.DriveLetter
      FreeGB  = [math]::Round($_.SizeRemaining/1GB, 1)
      PctUsed = [math]::Round(($_.Size - $_.SizeRemaining)/$_.Size*100, 1)
    }
  }
} | Sort-Object PctUsed -Descending | Format-Table -AutoSize

In AlertMonitor, the same check becomes a trend-based alert: warn when the growth curve projects a full disk inside seven days — weeks before any static threshold would fire.

4. Sweep your Linux estate before on-call handoff. A thirty-second sweep beats discovering a failed unit at 3 AM:

#!/bin/bash
# Pre-handoff health sweep for a Linux server estate
while read -r host; do
  echo "=== $host ==="
  ssh -o ConnectTimeout=5 "$host" \
    "uptime; df -h / | tail -1; echo 'Failed units:'; systemctl --failed --no-legend"
done < /etc/monitoring/servers.txt

Empty output under "Failed units" is what healthy looks like. In AlertMonitor these same checks run continuously, per device, against baselines — so the sweep becomes the tool's job, not a manual ritual.

5. Design an escalation policy that routes to people, with a clock. If your escalation "policy" is a distribution list, write down what it should be. In AlertMonitor it looks conceptually like this:

# Illustrative shape of a multi-level escalation policy
policy: contoso-production-critical
scope:
  client: contoso
  severity: [critical]
  schedule: 24x7
levels:

• notify: [j.rivera] # level 1 on-call ack_within: 5m channels: [push, sms]
• notify: [m.chen] # level 2 ack_within: 10m channels: [push, sms, voice]
• notify: [it-manager] channels: [voice] suppression: maintenance_windows: honor # quiet during planned reboots dedup_window: 15m # collapse flapping into one incident

Every field there maps to a decision you should be able to make in whatever tool you own today. If your current tool can't express "page a specific person, escalate if unacknowledged, suppress during maintenance but not for unexpected failures" — that's not an alerting tool, it's an email sender with a server icon.

**6. Put maintenance windows around every planned change.** Patch Tuesday, firmware updates, planned failovers — window them. The test: during your next patch cycle, the on-call phone should stay silent unless something actually went wrong. If it doesn't, your suppression or dedup needs work.

## The Bottom Line

SolarWinds putting autonomous agents behind alert noise reduction tells you the industry knows exactly where the pain is: not in collecting data, but in deciding what deserves a human. Agentic AI will get there — carefully, with administrators keeping override control, as Avior's design emphasizes.

But you don't need to bet your on-call rotation on a roadmap. Context-rich alerts, deduplication that actually collapses storms, maintenance windows that fail safe, and escalation that puts a named person on a clock will remove most of your 2 AM noise this quarter — and they happen to be exactly the foundation any future automation layer needs. Agents can only act on signal. Build the signal first.

## Related Resources

[AlertMonitor Alert Management & On-Call Operations](https://alertmonitor.ai/platform/alert-management)
[AlertMonitor Platform Overview](https://alertmonitor.ai/platform)
[Book a Demo](https://alertmonitor.ai/contact)
[Alert Management & On-Call Operations Resources](https://alertmonitor.ai/blog/alert-management)