AI-led AMS – Smart Ticket Triaging

In production environments, the solution has achieved up to 90% reduction in ticket volumes through noise suppression across multiple applications, significantly reducing L1 support effort and improving engineer productivity by focusing only on high-impact, actionable incidents.

The solution integrates seamlessly with leading ITSM platforms (ServiceNow, Jira, BMC Remedy) and observability tools (Datadog, Dynatrace, Splunk, CloudWatch) without disrupting existing ecosystems.

### Background:

**In most enterprise environments, AMS and IT Operations rely on a combination of observability platforms and ITSM systems to detect anomalies and manage incidents. While these tools provide visibility, traditional operations remain largely manual, reactive, and fragmented. Teams face:**

• High volumes of noise alerts, duplicate incidents, and misrouted tickets consuming L1/L2 bandwidth
• Limited cross-layer log correlation for accurate root cause identification
• Repetitive resolution patterns handled manually, inflating operational costs
• Fragmented observability across applications, infrastructure, and databases
• Inability to scale monitoring without proportional headcount growth, with significant time spent on non-value-adding activities

**This creates a clear need for an AI-led operational intelligence layer on top of existing monitoring and ITSM platforms — one that enables alert intelligence, intelligent incident triaging, automated remediation, and proactive service management.**

### Solution:

*The solution deploys a coordinated system of five specialized AI agents operating across a multi-layered architecture:*

• Observability Agent: Evaluates system health, performance, and behavior across applications and infrastructure.
• Metrics Diagnosis Agent: Analyses threshold-based alerts and identifies anomalies requiring action.
• Logs Diagnosis Agent: Correlates logs across services to detect root cause patterns
• Ticket Diagnosis Agent: Enriches alerts using historical tickets, SOPs, and contextual insights.
• Ticket Triaging Agent: Automates categorization, prioritization, assignment, and routing based on severity, workload, and on-call schedules.

**A Diagnose & Correlate layer combines deterministic rule-based triaging and SOP-driven workflows with probabilistic AI models for pattern recognition, RCA using historical trends, and cross-domain correlation linking metrics, logs, and incidents.**

### Key Capabilities:

• Alert Suppression: Filters non-relevant and downward-trending alerts to prevent unnecessary ticket creation
• Actionable Alert Handling: AI-driven categorization, prioritization, and ticket creation with recommendations
• RCA & Remediation: Cross-service log correlation for root cause detection; SOP and historical pattern-based auto-closure
• Duplicate Ticket Prevention: Detects and consolidate similar incidents to maintain ticket hygiene
• Intelligent Routing: Assigns tickets based on severity, workload, skill profiles, and on-call schedules
• Centralized Operations Dashboard: Real-time visibility in MTTR, FLR, ticket trends, and agent performance

**Customer Success Stories & Measurable Outcomes**

• Up to 90% reduction in application alert incident volumes through intelligent noise suppression
• Up to 65% reduction in MTTR for application incidents driven by reduced alert fatigue
• Up to 45% cost reduction by eliminating non-value-adding operational effort
• Up to 40% improved application availability
• 24×7 autonomous operations with human-in-the-loop oversight for critical decisions