Incident Triage & Auto-Resolution

Enterprise IT organizations handle thousands to tens of thousands of incidents monthly. L1/L2 service desk teams spend 60-70% of their time on repetitive tasks—password resets, account unlocks, software installation requests, and basic troubleshooting. Traditional ServiceNow workflows rely on manual ticket review, categorization, assignment, and resolution, creating backlogs, poor user experience, and high operational costs. Meanwhile, 40-60% of tickets could be auto-resolved with proper context and reasoning.

Workflow goal

Intelligently triage incoming incidents, auto-resolve repetitive requests, and route complex issues to the right resolver—reducing L1 workload by 50-70% while improving mean time to resolution (MTTR).

  • VP of IT Operations

  • Service Desk Manager

  • Incident Management Team

  • End Users

End-to-End Agentic Flow
1

Ticket Intake

  • Ingest incident from ServiceNow (email, portal, chat, phone)

  • Parse description, urgency, affected user/service

2

Intent Classification

  • Identify true issue (password reset, access issue, application error, hardware problem)

  • Determine if auto-resolvable or requires human intervention

  • Extract relevant context (user role, location, affected systems)

3

Context Assembly

  • Pull user profile, recent tickets, asset information

  • Check knowledge base for similar resolved incidents

  • Query monitoring systems for related alerts or outages

4

Auto-Resolution Attempt

  • Execute standard remediation (reset password, unlock account, restart service)

  • Validate resolution through automated checks

  • Update user with solution and close ticket

5

Intelligent Routing (if not auto-resolved)

  • Categorize by technical domain (network, application, infrastructure)

  • Assign to appropriate resolver group based on skills and availability

  • Enrich ticket with context and suggested troubleshooting steps

6

Knowledge Capture

  • Identify novel resolution patterns

  • Flag incidents for knowledge article creation

  • Update resolution playbooks

  • Natural language understanding for ticket parsing

  • Multi-system context gathering

  • Automated remediation with validation

  • Intelligent routing logic

  • Confidence threshold for auto-resolution (typically 85%+)

  • Approval required for high-impact actions

  • User confirmation for password resets and account changes

  • Read/write access scoped to specific ServiceNow operations

  • Auto-resolution rate (target: 40-60%)

  • Mean time to resolution (MTTR)

  • First contact resolution (FCR) rate

  • L1 deflection percentage

  • User satisfaction scores (CSAT)

  • Manual intervention rate

Change Risk Assessment & Approval Automation