Call us at:
888 477 5580
Call us at:
888 477 5580
By David Alves · September 14, 2026
At 2:14 a.m., a database starts responding slowly. Within minutes, every service that depends on it breaches its thresholds. The on-call engineer's phone shows hundreds of alerts: slow checkouts, failed logins, queue backlogs, CPU spikes. Only one of them is the actual problem.
This is alert fatigue, and it's dangerous in a quiet way. When people learn that most alerts don't matter, they start treating all of them that way.
A quick word on terms. APM is application performance monitoring. An SLO (service level objective) is a target for how reliable a service should be, such as 99.9% of requests succeeding. The error budget is the small amount of failure that target allows. OpenTelemetry is an open standard for collecting traces, metrics and logs from applications. AIOps (AI for IT operations) is Gartner's term for tools that use data and machine learning to support IT operations.
Alerts watch causes instead of symptoms. Google's Site Reliability Engineering book distinguishes between what's broken (the symptom users feel) and why (the cause). It recommends watching four “golden signals”: latency, traffic, errors and saturation. Paging on every possible cause produces many alerts for one incident.
Every tool alerts on its own. The database, the application, the load balancer and the cloud platform each raise their own alarms about the same event.
Nobody removes the alerts nobody acts on. Google's guidance is blunt: every page should be urgent, actionable and affect users. Pages that only get a routine response should be automated or removed.
Google's SRE Workbook recommends alerting on how fast the error budget is being used, its burn rate. For example, page someone if the budget is burning 14.4 times faster than sustainable over an hour; open a ticket for slow, steady burns. That ties alerts directly to user impact.
Notice the language the vendors themselves use: “most probable cause,” “hypotheses.” An AI suggestion is a starting point for an engineer, not a verdict. And fewer alerts isn't automatically safer. The goal is the right alerts.
GQP has deployed and run application performance monitoring for years. Our Fractional Agentic AI Team builds on that through AI-Powered Application Performance Monitoring: symptom-based alerts tied to SLOs, alert grouping and dependency-aware investigation, built on open standards such as OpenTelemetry. Your team confirms the cause and decides the fix. See also our Application Performance Management service.
Start with a 15-minute conversation. We'll ask how alerting works today, then tell you honestly whether AI would help.
Prefer the phone? Call us at 888-477-5580, or 888-GQP-5580. Alternatively, complete our Contact Us form here.
Sources: Google, Site Reliability Engineering, “Monitoring Distributed Systems,” and The Site Reliability Workbook, “Alerting on SLOs.” OpenTelemetry documentation, “Signals.” Dynatrace documentation, root cause analysis concepts. PagerDuty Support, “Alert Grouping” and “Intelligent Alert Grouping.” Datadog documentation, Bits AI SRE. Gartner glossary, “AIOps (Artificial Intelligence for IT Operations).”