AI‑Driven ITSM Incident Triage Automation at Enterprise Scale
Case Study Details:
Industry
Legal & Compliance
Region
USA
Technology
Microsoft Agent Framework · AI Orchestration · FastAPI (Python) · MCP Server · ServiceNow (Classic Business Rules, REST API) · Docker · GitHub Actions · Azure Key Vault
The Challenge
For a large compliance‑focused enterprise, rising incident volumes turned manual triage into a critical operational constraint
- Thousands of incidents generated daily across multiple business services
- Human triage could not scale without impacting SLA and MTTR
- Inconsistent labelling degraded routing, analytics, and escalation workflows
- Introducing LLM intelligence into a legacy ITSM platform required deterministic execution, explainable decisions, and full auditability
Traditional rule‑based automation lacked intelligence; generic AI approaches lacked governance.
The enterprise needed more than automation. It needed intelligence—engineered to decide, act, and own every stage of the incident lifecycle.
Evoke’s Approach
Evoke Technologies designed and deployed Incident Manager, an autonomous AI‑driven triage agent purpose‑built for enterprise ITSM environments.
AI‑Orchestrated Incident Lifecycle
-
ServiceNow “Before Insert” Business Rules
Trigger real-time webhooks at incident creation.
-
FastAPI Orchestration Layer
Consumes events and initiates AI workflows.
-
Microsoft Agent Framework Coordination
- Incident context retrieval via MCP Server
- AI-based business service classification
- Deterministic tag and label assignment
- Explainable AI-generated work notes
-
Automated Write-Back
Classified services, tags, and rationale are written to ServiceNow before queuing.
Each incident arrives pre‑classified, pre‑labelled, and fully rationalized.
Secure, Enterprise‑Ready Design
- Dockerized services enable atomic releases and rollback
- GitHub Actions CI/CD manages environment-safe deployments
- Azure Key Vault enforces zero-secret storage in code or pipelines
- Full decision auditability across classification and updates
The Outcomes
| Metric | Before | After |
|---|---|---|
| Triage latency | Manual backlog | Real-time automation |
| Classification accuracy | Manual baseline | 80% improved |
| Incident routing | Delayed | Immediate alignment |
| Avg. time to triage | 2hrs | 40sec |
| Security posture | Credential risk | Zero-secret, vault-managed |
| Release model | Manual updates | Atomic containerized delivery |
Strategic Value Delivered
- Faster MTTR without increasing headcount
- Consistent, explainable service classification
- Compliance-ready AI adoption with full audit trails
- Scalable, resilient incident operations across environments
The organization now operates a repeatable, AI‑first ITSM triage pipeline that transforms incident handling from a manual bottleneck into a governed, automated capability—securely and at enterprise scale.
