One use case, both layers

ITSM 1.1 — Intelligent Ticket Triage & Routing

Auto-classify, prioritize, and route incoming tickets using NLP — eliminating manual triage queues.

The Problem

Tier-1 agents spend 30–40% of their time re-reading, categorizing, and reassigning tickets. Misrouting adds hours to resolution time.

AI Approach

An LLM reads ticket subject, body, and history. It classifies by category, urgency, and affected system, then assigns to the optimal queue or agent based on skill mapping and current load.

Agent Modules

• Ingestion agent (email / portal / chat)
• Classification agent
• Priority scoring agent
• Routing agent with SLA awareness

Business Value

• 40–60% reduction in manual triage time
• Consistent priority assignment
• SLA breach prevention via early flagging

POC Effort

⬤ Easy

Full Agent Architecture — 5 agents, explicit roles

From the complete 11-section detailed specification for the same use case.

Agent	Responsibility	Success Criteria
Ingestion Agent	Normalize tickets from email, portal, chat, API	Complete parsing, low latency, preserved original content
Classification Agent	Determine category, affected system, urgency	85%+ accuracy on held-out test set
Priority Scoring Agent	Score business impact and SLA urgency	Aligns with documented SLA / impact policy
Routing Agent	Assign to optimal queue/agent by skill + load	Low reassignment rate; load-balanced
Verification / Escalation Agent	Review low-confidence cases, enforce policy	Correct escalation; zero silent policy violations

Orchestration: Sequential Pipeline (Ingestion → Classification → Priority → Routing) with Router elements for load-aware assignment and a final Validation/Critique gate. Low-confidence or policy-flagged items escalate to a human with full chain-of-thought and source context.