Engineering out alert fatigue with adaptive baselining
Alert fatigue isn’t just frustrating, it’s dangerous. In complex, fast-moving environments like capital markets and payments systems, engineering teams are flooded with noise from static thresholds. The result? Missed anomalies, slower responses, and unnecessary firefighting.
Adaptive baselining offers a smarter path forward, combining real-time learning and automation to help engineers cut through the noise and focus on real issues.
Why alert fatigue persists in modern systems
Legacy monitoring setups assume systems are static. Static thresholds, hardcoded upper and lower limits, are brittle in the face of dynamic workloads.
This creates three pain points engineers know too well:
False positives during routine workload shifts (month-end settlements, flash trading surges).
Missed subtle anomalies as systems evolve and baselines drift.
Manual threshold tuning that’s impossible to scale across thousands of metrics.
A 2024 study found 33% of organizations cite alert fatigue as the #1 barrier to faster incident response. For engineers on the front lines, a better approach is overdue.
How adaptive baselining works (under the hood)
ITRS Geneos 7 Dynamic Thresholds use seasonal models that continuously learn from historical data to establish “normal” behavior for each metric. These models can operate at granular levels, like 5-minute windows, ensuring fidelity in high-volume environments.
By adjusting in real time, adaptive baselining surfaces only meaningful deviations and reduces irrelevant noise. Here’s how engineers are applying it in practice.
Real-world engineering use cases
Detecting sudden change
An operations team monitoring trade prices noticed that static thresholds often missed abrupt spikes or drops. By enabling upper and lower adaptive thresholds and configuring a 30-minute sliding window baseline, they could detect deviations in either direction.
Adding an alert delay of 5 minutes meant only persistent anomalies triggered alerts, dramatically cutting false positives. When a sudden drop occurred in one time series, the system flagged it early, giving the team critical lead time to act.
Reducing noise from existing rules
Another team was overwhelmed by an analytics rule triggering too many alerts. Using Dynamic Thresholds, they:
Tuned sensitivity sliders to adjust detection thresholds.
Applied a rolling window model to smooth out noise in the underlying metric.
Added an alert delay so only sustained breaches would trigger notifications.
This multi-pronged approach slashed unnecessary alerts, helping engineers focus on real issues and dramatically reducing alert fatigue.
Smart default monitoring at scale
For a global API monitoring setup, response times varied widely across regions. Manually configuring thresholds for each API was not feasible. Dynamic Thresholds enabled the team to:
Apply smart defaults that learned each API’s normal behavior.
Adjust thresholds dynamically as network conditions evolved.
Simulate new configurations to preview alert volumes before rollout.
This reduced manual effort and improved monitoring accuracy across a sprawling, dynamic estate.
Detecting early deviations from expected behavior
In a trading environment, engineers used a daily seasonal model over 7 days with 5-minute granularity to establish normal transaction patterns, including four distinct intraday peaks.
With a single configuration, the system adapted to client-specific baselines and surfaced deviations in near real-time. This gave teams the lead time to investigate and resolve potential issues before they became incidents.
Benefits for engineering teams
Noise Reduction: Eliminate thousands of spurious alerts.
Continuous Self-Tuning: Adaptive thresholds evolve without manual intervention.
Rapid Configuration: Apply models across complex estates quickly.
Early Detection: Spot emerging issues before they cascade into outages.
The outcome? Fewer distractions, faster mean time to detect (MTTD), and reduced mean time to resolve (MTTR).
Why it matters in mission-critical environments
In capital markets, milliseconds can mean millions. In payments infrastructure, downtime erodes customer trust instantly.
Dynamic Thresholds empower engineers to:
Detect anomalies earlier
Prioritize critical issues
A resilient systems that adapt at the pace of modern workloads
Build resilient systems with less noise
Adaptive baselining isn’t just about fewer alerts, it’s about enabling engineers to focus on innovation instead of firefighting.
Learn more about Dynamic Thresholds in ITRS Analytics