RAG-Assisted AI NOC Operator
Runbook RAG · 5-Agent Governance · ITSM · Learning Loop
Assisted Operations Daniel Mazzini
RAG-assisted multi-agent NOC workflow

RAG-Assisted AI NOC Operator

5-agent operations workflow that retrieves runbook guidance, correlates noisy alarms, classifies incidents, applies remediation governance, creates ITSM tickets, tracks outcomes, and generates post-incident learning for the next shift.

247
Raw events · last 60 min
4.2 hrs
Current MTTR
0
Suppressed / deduped events
P1
Impact risk
Raw Event Stream — Before Correlation & Suppression
Provider
Incident scenario
0Event ingestion & suppressionNMS · EMS · SNMP · telemetry
1RAG-grounded correlationRunbooks + symptoms to root cause
2ClassificationConfidence · severity · impact
3Governance approvalSafety gate · rollback · policy
4ITSM ticketingServiceNow · Remedy · owners
5Outcome trackingExecuted · failed · escalated
6Post-incident learningRunbook update · detection rule
7Shift turnoverOwners · SLA · next action
Stage 0 — Event Ingestion, Suppression & Impact Context
Normalizes raw alarms, suppresses duplicate/flapping events, and builds service-impact context before AI correlation.
Normalized sources
Service impact summary
Change / maintenance check
Agent 1 — Alarm Correlation Agent
Correlates raw NOC alarms into a root cause and actionable incident groups.
Agent 2 — Incident Classification Agent
Scores confidence and recommends auto-remediation or human escalation.
IncidentRoot causeConfidenceAction
Agent 3 — Remediation Governance Agent
Validates blast radius, rollback posture, and change-control risk before execution.
Human Approval Gate — Review Before Execution
Governance has separated safe actions from items that require human review. Approved actions may proceed to simulated execution; risky items are ticketed for ownership.
Agent 4 — Ticketing & Shift Workflow Agent
Creates simulated ServiceNow / Remedy trouble tickets for next-shift attention.
ITSM Work Queue — ServiceNow / Remedy Tickets
Creates operational tickets for incidents that require human ownership, field work, change approval, or next-shift follow-up.
Mean Time To Resolution
4.2 hrs —
Operational Outcome Tracking — Runbook Results & Ticket Updates
Tracks the result after governance and ticketing: safe actions executed, failures escalated, tickets updated, and blocked changes routed for approval.
Agent 5 — Post-Incident Learner
Generates post-mortem summary, runbook update proposal, detection rule, and KB article draft.
Closed-Loop Learning — Runbook & Detection Rule Improvement
Turns the incident outcome into reusable operational knowledge so the next similar event is faster and safer to handle.
Shift Turnover Control Pack — Owners, SLA Risk & Next Actions
Final operational handoff for the incoming shift, including ticket queue, unresolved risks, and assigned owners.