AI Agents Emit 4-5x the Telemetry — Stop Paying Per Gigabyte to Watch Your Own Network | AlertMonitor

Cisco Splunk's State of Observability 2026 report just put hard numbers on something IT teams have been feeling all year. They surveyed 2,050 IT operations and engineering professionals across 10 countries. Three findings should make every IT manager open their billing portal today:

57% of organizations now pay more for observability than the cost of the infrastructure being monitored.
60% say unexpected billing spikes cause more operational strain than consistently high costs.
55% cannot accurately control their AI expenses.

Kamal Hathi, senior vice president at Splunk, named the culprit plainly: "A production agent emits 4-5x more data as telemetry."

If you run infrastructure for a living, you already know how this movie ends. The invoice lands. The CFO forwards it with a single question mark. And when you dig in, you find the AI pilot nobody scoped is generating five times the telemetry — while the switch uplink it saturates every night at 2am was never onboarded to monitoring in the first place. Your observability platform billed you per gigabyte for every token of that agent traffic, and still didn't page anyone when the link fell over.

That's the dysfunction in one sentence: you're paying more than the infrastructure costs to watch it — and the watch doesn't work.

The Problem in Depth: Why Agents Break Observability Economics

AI agents aren't one request — they're a loop. A single agent run means plan → tool call → retrieval → inference → retry → summarize. Every hop emits logs: prompts, responses, token counts, latencies, tool payloads, error traces. Multiply that by concurrent sessions, users, and scheduled triggers, and you get the 4-5x. Worse, agent telemetry carries high-cardinality dimensions — session ID, task ID, model version, step number — and cardinality is exactly what usage-based pricing bills hardest.

The pricing models were already hostile before agents showed up. Per-GB ingest, per-host plus per-GB logs, per-custom-metric samples. Teams were already writing ingestion filters and sampling logs just to keep bills survivable. Agents removed whatever slack was left.

Spikes are worse than steady costs — 60% of orgs said so, and they're right. A predictable line item is a budget entry. A spike is an emergency meeting with finance, a chargeback fight with whatever team ran the pilot, and a panicked decision to drop data sources to control the burn. Which means teams are sampling and filtering exactly when workloads become more dynamic — and intermittent agent-induced congestion is precisely the kind of event that survives sampling badly. You go blind on purpose, at the worst possible moment.

And underneath all of this sits the problem nobody wants to admit: most teams can't see their own network. Run this scenario in your head:

Legal pilots an AI contract-review agent. Every night it chunks hundreds of PDFs to an inference server two floors up. A 1Gb uplink saturates at 2am. That wing's Wi-Fi starts stuttering. Over three weeks, the helpdesk collects 30 tickets that all say some version of "the internet is slow up here." The switch was installed by a contractor in 2019 and never onboarded to any monitoring tool, so uplink utilization has never been a signal anyone could see. You eventually find the cause by walking the floor with a laptop. And that month's observability invoice quietly included the agent's own traces — expensive telemetry that produced exactly zero alerts.

MSPs get the same movie with worse margins. You bill a client flat for monitoring. Their internal dev team deploys an agent fleet. Ingest triples, your per-GB tooling cost erases the contract margin, and you can't reprice mid-term. So you eat it — or you quietly lower collection fidelity, which resurfaces in six months as a missed alert and a much worse conversation.

The morale cost is real too. Engineers who got into this field to keep systems running now spend their afternoons writing ingestion filters and cost-allocation queries. "We pay more to watch the infrastructure than the infrastructure costs" is a brutal sentence to read in a budget review — and at 57% of organizations, it is literally true.

How AlertMonitor Fixes This

The principle is simple: you can't control a telemetry bill for a network you can't see — and you shouldn't pay per gigabyte to see the network you own. AlertMonitor attacks both halves of that sentence.

Continuous discovery, not quarterly scans. AlertMonitor discovers and maps every device on the network — switches, firewalls, access points, printers, IP cameras, unmanaged endpoints — using SNMP, ARP, and active scanning. That inference server from the scenario? It triggers a new-device alert the day it appears, with its IP, MAC, switch port, and location on the map. You find out about the AI pilot from a discovery alert, not from a finance meeting.

A topology map that reflects reality right now. When a switch goes offline or a link drops, an alert fires instantly with full context: which device, which uplink, which downstream clients are affected. No stale Visio diagrams, no "can someone confirm which floor this switch is even on."

Monitor the network layer of AI traffic — not every token. You do not need to ingest every LLM prompt and response to know that an inference cluster is saturating uplink Gi1/0/24. Interface utilization, errors, discards, and latency on the paths carrying agent traffic tell you where the problem is; the application team can figure out why. That's the cheapest, highest-signal telemetry available — and in AlertMonitor it doesn't scale with agent volume, because there's no per-GB pricing to scale against.

Flat licensing. No ingest-based billing. No spikes. Your cost in July is your cost in January, whether you're running two agents or two hundred. For the 60% of orgs who fear spikes more than steady costs, this is the entire ballgame: a monitoring line item you can actually put in a budget.

One platform instead of five. Monitoring, RMM, helpdesk, patch management, and topology live in one product with one license. No per-host pricing stacked across four vendors, no chargeback ambiguity about which tool billed what.

The workflow difference:

Old way: user ticket ("Wi-Fi is slow") → duplicate tickets pile up → three tools open → someone discovers an undocumented switch → manual CLI checks over SSH → fix → nothing links the ticket, the outage, and the device. Total: three weeks of complaints.

AlertMonitor way: threshold alert fires on the uplink at 70% sustained utilization → topology map highlights the exact link and downstream devices affected → tech verifies the device flagged by last week's new-device alert → remediates through integrated remote management → ticket auto-links the alert, device, and resolution. Total: about 15 minutes, with a paper trail.

Practical Steps You Can Take Today

1. Inventory reality before you renegotiate any observability contract

You cannot scope a monitoring bill for a network you can't enumerate. Sweep your VLANs and join the results with your ARP table so you know not just what is live but who made it:

PowerShell

# Requires PowerShell 7+ for -Parallel. Sweep a /24 and return live IPs.
$subnet = "192.168.10"
$live = 1..254 | ForEach-Object -ThrottleLimit 64 -Parallel {
    $ip = "$using:subnet.$_"
    if (Test-Connection -ComputerName $ip -Count 1 -Quiet) { $ip }
}

# Join with the local ARP table — the MAC OUI prefix tells you the vendor
Get-NetNeighbor |
    Where-Object { $_.IPAddress -in $live -and $_.LinkLayerAddress } |
    Select-Object IPAddress, LinkLayerAddress, State |
    Sort-Object { [int]($_.IPAddress.Split('.')[-1]) } |
    Format-Table -AutoSize

Linux equivalent, no agent required:

Bash / Shell

# Find every live host on the VLAN (nmap: apt install nmap)
sudo nmap -sn 192.168.10.0/24 -oG - | awk '/Up$/{print $2}'

Anything you didn't expect is either a candidate for monitoring or a candidate for removal. Both are answers.

2. Find the port your AI traffic actually rides

Before you blame the agent, prove where the bytes flow. Sample 64-bit interface counters on your core switch (needs net-snmp installed):

Bash / Shell

#!/usr/bin/env bash
# Sample ifHC counters twice, 10 seconds apart, to get real throughput
COMMUNITY="public"     # your SNMP community string (or use v3 credentials)
SWITCH="10.0.0.2"      # core switch IP
IFINDEX="24"           # ifIndex of the uplink you want to check

IN1=$(snmpget -v2c -c "$COMMUNITY" -Ovq "$SWITCH" "IF-MIB::ifHCInOctets.$IFINDEX")
OUT1=$(snmpget -v2c -c "$COMMUNITY" -Ovq "$SWITCH" "IF-MIB::ifHCOutOctets.$IFINDEX")
sleep 10
IN2=$(snmpget -v2c -c "$COMMUNITY" -Ovq "$SWITCH" "IF-MIB::ifHCInOctets.$IFINDEX")
OUT2=$(snmpget -v2c -c "$COMMUNITY" -Ovq "$SWITCH" "IF-MIB::ifHCOutOctets.$IFINDEX")

echo "In:  $(( (IN2 - IN1) * 8 / 10 / 1000000 )) Mbps"
echo "Out: $(( (OUT2 - OUT1) * 8 / 10 / 1000000 )) Mbps"

Run this on every uplink between the AI workload and its data source. The port pegged near line rate at 2am is your story for the CFO — and the exact threshold you configure in AlertMonitor.

3. Identify the chatty process on a suspect host

If a device is generating abnormal traffic, find the process behind it. A runaway agent runtime shows up immediately as a pile of connections to one endpoint:

PowerShell

# Top 10 processes by established TCP connections, with their remote endpoints
Get-NetTCPConnection -State Established |
    Group-Object OwningProcess |
    ForEach-Object {
        [pscustomobject]@{
            Process     = (Get-Process -Id $_.Name -ErrorAction SilentlyContinue).ProcessName
            Connections = $_.Count
            RemoteIPs   = ($_.Group.RemoteAddress |
                            Where-Object { $_ -notmatch '^(127\.|::1|fe80)' } |
                            Sort-Object -Unique) -join ', '
        }
    } |
    Sort-Object Connections -Descending |
    Select-Object -First 10

A process holding dozens of established connections to a single internal IP is polling or streaming — exactly the behavior that becomes next month's telemetry spike.

4. Make sure the network gear is actually monitorable

Half of "we had no visibility" is really "SNMP was never enabled." Verify it across the Windows boxes your monitoring polls:

PowerShell

# Confirm the SNMP service is present and running on polled servers
$servers = "DC01","FS01","SQL01","HV02"
Invoke-Command -ComputerName $servers -ScriptBlock {
    Get-Service SNMP -ErrorAction SilentlyContinue |
        Select-Object @{n='Computer';e={$env:COMPUTERNAME}}, Name, Status, StartType
}

If it's missing, install it before someone discovers the gap during an outage:

PowerShell

Install-WindowsFeature SNMP-Service -ComputerName FS01

5. Baseline now, before the next AI pilot

This is the step that turns the Splunk findings from a warning into a plan:

Schedule continuous discovery in AlertMonitor so every new device — including the next unsanctioned inference server — fires a new-device alert with full network context.
Set uplink utilization thresholds (for example, alert at 70% sustained for 5 minutes) so saturation pages your team, not your end users.
Record today's baseline utilization per uplink. When a department asks to deploy agents, you can show them exactly what their pilot does to the network — and exactly what it won't do to your monitoring bill, because it won't move one.

The Bottom Line

The Splunk numbers aren't an argument against AI agents — they're an argument against paying usage-based prices to watch them. 57% of organizations paying more for observability than infrastructure isn't a technology problem; it's a purchasing model problem stacked on top of a visibility problem. Fix the visibility with continuous discovery and a live topology map. Price the monitoring flat. Then the 4-5x telemetry multiplier stops being your budget's problem and becomes the agent vendor's engineering challenge — where it belongs.