AI-led AMS – Smart Ticket Triaging

Brillio’s Smart Ticket Triaging solution brings agentic intelligence to end-to-end alert triaging for enterprise IT operations. IT teams today are overwhelmed by high alert volumes and redundant incidents, leading to increased ticket loads, support fatigue, and rising operational costs — while resolution times continue to lag.

Powered by ADAM (Agentic Data & Applications Management) AI Agents and built on AWS services — including Amazon Bedrock, Amazon CloudWatch, Amazon Kendra, AWS Lambda, Amazon EKS, and Amazon DynamoDB — the solution autonomously processes metrics and log-based alerts by suppressing non-relevant signals, preventing duplicate tickets, and enabling intelligent ticket creation, prioritization, and routing. It performs cross-service log correlation for root cause analysis and leverages SOPs and historical resolutions to recommend or automate remediation.

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.

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.

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.

• 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