AI-Driven Workflow for KPI Degradation Root Cause Analysis

Operational savings come from compressing hours of manual cross-domain data correlation into minutes. The workflow triggers on KPI breaches from the OSS, fusing telemetry from Ericsson or Nokia RAN elements, Cisco/Juniper transport, and the 5G core. An orchestrator, built on LangGraph, dispatches specialized agents to analyze temporal patterns, topology dependencies, and recent configuration changes. This first-stage triage isolates the fault domain—be it a misconfigured cell, a congested fiber link, or a core network function failure—and assigns a confidence score, routing only high-certainty diagnoses for automated remediation.

Implementation requires integrating agents with the RIC (RAN Intelligent Controller), element managers, and ITSM tools like ServiceNow. Controls are critical: all automated remediation actions must pass through an approval gate defined by severity and change-window policies. Observability is built into the orchestrator, logging every agent decision and data source for audit. The final architecture reduces MTTR by over 70%, prevents SLA breaches through proactive diagnosis, and shifts NOC engineers from firefighting to managing exceptions and refining the AI models.

This workflow automates the most time-consuming task in network operations: diagnosing the source of KPI degradation when alerts fire across siloed domains. It replaces manual log correlation and war-room analysis with a multi-agent system that ingests real-time telemetry from RAN controllers (via O-RAN RIC), transport switches, and core network probes. The operational upside comes from compressing diagnosis from hours to minutes, directly reducing service-impact duration, lowering high-severity ticket volumes, and freeing senior engineers for strategic work rather than forensic analysis.

Implementation requires deploying lightweight analysis agents within each domain's data plane, an orchestrator (built on LangGraph or similar) to manage the query/reply sequence, and a causal model that weights evidence from historical incident patterns. Critical controls include an approval gate for any automated reconfiguration actions, a human-review loop for low-confidence diagnoses, and integration with ServiceNow or similar ITSM for ticket creation and audit. The architecture must handle poor data quality from legacy elements by flagging gaps for operator review, ensuring the system enhances rather than replaces expert judgment during rollout.

This workflow automates the costly, manual process of troubleshooting KPI degradations by fusing multi-domain telemetry from RAN elements, transport switches, and core network functions. AI agents apply causal inference models to correlate subtle anomalies, ranking probable root causes like hardware faults, configuration drift, or interference. The operational upside is a 60-80% reduction in engineering triage time, directly lowering operational expenditure and preventing minor issues from escalating into service-impacting outages that affect SLAs and customer satisfaction.

Implementation requires integrating the orchestrator with OSS systems like Ericsson ENM or Nokia NSP, data lakes for telemetry, and ITSM platforms like ServiceNow. The architecture uses a LangGraph or custom state machine to manage agent coordination, with strict controls for change approval before automated remediation. Observability is built in via confidence scoring, human review gates for low-confidence diagnoses, and detailed audit trails to validate the AI's reasoning, ensuring operational trust and continuous model improvement based on field resolution outcomes.

This workflow automates the high-stakes, manual bottleneck of correlating KPI drops across RAN, transport, and core domains. It fuses multi-vendor telemetry, applies causal inference models, and ranks root-cause probabilities, delivering operational savings by slashing engineering investigation time from hours to minutes. The architecture must integrate with OSS/BSS, SON controllers, and fault management systems, requiring robust data validation and exception routing to prevent automated misdiagnosis from triggering incorrect network changes.

Implementation requires a phased rollout, starting with non-critical cells and low-risk KPIs to validate model accuracy and control logic. Governance is enforced through approval gates for high-impact changes, detailed audit trails of agent decisions, and continuous monitoring of the workflow's own performance KPIs. The final architecture embeds the agents within the RAN Intelligent Controller (RIC) or Service Management and Orchestration (SMO) framework, ensuring policy compliance and seamless integration with existing SON and assurance systems for zero-touch operations.