Multi-Agent Automation for Network Change Management and Approval Orchestration

High-risk network changes—like core router updates, firewall rule modifications, or BGP policy shifts—require meticulous planning, multi-tiered approvals, and precise execution to avoid costly outages. Manual coordination between engineers, change advisory boards (CAB), and network systems is slow and error-prone. This workflow automates the entire lifecycle, from interpreting a business intent (e.g., 'improve latency to Azure') to post-change validation, slashing cycle times from days to hours while embedding mandatory risk assessments and backout plans into every step.

Implementation integrates with ServiceNow or Jira for ticket lifecycle, network automation platforms (Ansible, Python scripts) for execution, and observability tools for validation. The orchestrator agent manages state across the workflow, ensuring each step—drafting with embedded compliance checks, routing to the correct technical and business approvers, scheduling, and executing with pre/post snapshots—is auditable and reversible. Critical controls include human-in-the-loop gates for high-risk changes, automated rollback triggers on validation failure, and full audit trails synced to the CMDB, creating a defensible, zero-touch change pipeline for enterprise and carrier networks.

This workflow automates the high-risk, high-labor process of network change management. It eliminates manual ticket creation, approval chasing, and error-prone implementation by using agents to draft requests based on required outcomes, assess risk, and route through technical and business approval chains in ServiceNow or Jira. The operational upside comes from slashing change lead times, enforcing policy compliance, and preventing misconfigurations that cause outages, directly protecting revenue and service levels.

Implementation integrates with ServiceNow for ITSM workflows, network platforms like Cisco DNA Center or Ansible for execution, and observability tools for validation. The architecture requires robust controls: all agent-proposed changes are logged, high-risk modifications require mandatory human review, and automated rollback plans are pre-defined and tested. Deployment is phased, starting with low-risk change types, and includes continuous monitoring of agent decision accuracy and change success rates to ensure governance.

This workflow automates the high-risk, manual bottleneck of network change management. It eliminates the cycle of drafting requests in spreadsheets, chasing technical and business approvals via email, and manually scheduling and validating changes. The operational upside comes from reducing change-related outages by enforcing pre-flight risk assessments and backout plans, while cutting the end-to-end change cycle from days to hours. Implementation integrates with ServiceNow for ticketing, Jira for technical review, and network automation platforms like Ansible or Cisco NSO for safe execution.

The architecture is built on an agentic framework like LangGraph, where each agent has a discrete role and communicates via a shared state. The Orchestrator manages the overall flow, invoking specialized agents for drafting, risk scoring, and routing. Integration with ServiceNow and Jira is via REST APIs, embedding the workflow directly into existing ITSM processes. Critical controls include mandatory risk assessment for all changes, human-in-the-loop gates for high-risk items, and automated pre/post validation checks to ensure service integrity. Rollout requires phased deployment, starting with low-risk, repeatable changes to build trust before automating complex modifications.

This workflow automates the high-risk, high-labor process of network change management. It eliminates manual ticket creation, reduces approval cycle latency, and ensures every change is backed by a validated backout plan. The operational upside comes from slashing mean-time-to-implement (MTTI), preventing unauthorized or risky changes, and freeing network engineers from administrative toil for higher-value design work. Savings are realized through reduced outage minutes and improved resource utilization.

Implementation integrates with ServiceNow or Jira for ticketing and uses a LangGraph-based orchestrator to manage specialized agents for drafting, risk assessment, and execution. The architecture requires robust exception routing to human reviewers for non-standard changes and high-risk scenarios. Controls include mandatory pre/post validation checks, audit trails for all agent decisions, and phased rollout starting with low-risk change categories. This ensures governance is embedded, not bypassed, by automation.