Never miss an incident again with AI agents that monitor, diagnose, and resolve performance issues around the clock.
Application performance directly impacts user experience and revenue, but monitoring it effectively requires constant vigilance. Alert fatigue is a real problem: teams receive hundreds of notifications daily, most of which are false positives, and critical issues get buried in noise.
When real incidents occur, diagnosing the root cause requires correlating data from multiple sources -- logs, metrics, traces, and deployment history -- a process that takes skilled engineers hours.
The on-call burden burns out engineers and creates a single-point-of-failure dependency on whoever has the deepest system knowledge.
Agentik OS deploys monitoring agents that provide intelligent, context-aware oversight of your entire infrastructure. An Anomaly Detection agent filters signal from noise using machine learning, eliminating alert fatigue. A Diagnosis agent correlates logs, metrics, and traces to identify root causes automatically. And a Resolution agent applies predefined remediation steps or escalates with full context.
Agents understand your system's normal behavior patterns and only alert when something genuinely needs attention. When they do alert, the notification includes a diagnosis, impact assessment, and recommended action.
Incident response times drop from hours to minutes, and your engineering team is freed from reactive firefighting to focus on proactive improvements.
Agents map your infrastructure, services, dependencies, and normal performance baselines.
Agents configure metrics, logs, and traces collection across your entire stack.
Anomaly detection models are trained on your system's normal behavior to minimize false positives.
When issues are detected, agents diagnose, remediate, and document incidents automatically.
ML-powered anomaly detection eliminates alert fatigue and surfaces only genuine issues.
Root cause analysis happens in minutes, not hours, by correlating logs, metrics, and traces.
Common issues like scaling, restarts, and cache clears are resolved automatically.
Engineers are only paged for issues that truly require human judgment.
-75%
MTTR
Reduction in mean time to resolution for incidents
-90%
False Positives
Reduction in false positive alerts compared to threshold-based monitoring
99.99%
Uptime
Average uptime maintained with automated remediation
Datadog, Grafana, Prometheus, New Relic, PagerDuty, OpsGenie, and any platform with metrics and alerting APIs.
Yes. Auto-remediation can include horizontal scaling, pod restarts, cache invalidation, and failover triggers based on your runbooks.
During setup, agents observe your system for a baseline period, learning traffic patterns, resource utilization norms, and seasonal variations.
See how Agentik {OS} can automate this use case for your business.
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