Multi-Agent Orchestration for Intent-Based Network Operations: Automated Diagnosis and Remediation at Scale

Abstract

Modern network infrastructures have grown beyond the capacity of traditional ticket-based management systems, creating operational bottlenecks and extended resolution times. Multi-agent orchestration introduces a paradigm where specialized artificial intelligence agents collaborate to transform human intent into coordinated network actions. Each agent assumes distinct responsibilities: diagnostic agents identify fault conditions and perform root cause analysis, policy agents enforce compliance and change approval workflows, remediation agents execute configuration modifications, and verification agents validate outcomes. An orchestration layer coordinates these specialized components, decomposing complex operational scenarios such as port flap incidents into discrete, executable tasks while maintaining comprehensive audit trails. This research employs architectural analysis and implementation pattern evaluation across typical NetOps workflows spanning fault diagnosis, change window execution, and continuous compliance validation. Field observations demonstrate measurable benefits including 60-75% reduction in mean time to resolution for routine incidents, deflection of 40-50% of repetitive tickets through automated handling, achievement of 95%+ compliance rates through continuous validation loops, and consistent documentation meeting regulatory audit requirements. Governance mechanisms ensure safe execution through comprehensive logging, human approval gates for high-impact changes, and automated rollback capabilities, enabling organizations to automate network operations without sacrificing control or auditability.