Agentic Workflow for Real-Time Network Health Monitoring and Anomaly Detection

Traditional threshold-based alarms flag issues only after they breach a static level, often when service is already degraded. This AI workflow uses multivariate anomaly detection on live KPIs and telemetry to identify subtle, emerging patterns indicative of a fault—such as a gradual signal quality decay or correlated performance dips across sectors—well before a major outage occurs. This shifts identification from minutes or hours to seconds, allowing NOC teams to investigate root cause while the network is still operational.

By detecting anomalies early and triggering automated diagnostics, the workflow provides field engineers with a precise fault hypothesis (e.g., 'suspected PA degradation on sector B') before dispatch. This reduces truck rolls, shortens repair times, and minimizes customer-impacting outages. For a mobile operator, preventing a single hour of outage across a major metro cell site can protect tens of thousands in lost service revenue and avoid brand damage from social media complaints.

A typical NOC console is flooded with hundreds of low-level, uncorrelated alarms. This workflow acts as a force multiplier by correlating signals, suppressing noise, and presenting a single, contextualized incident with a probable cause. This reduces the cognitive load on Tier 1/2 engineers, allowing them to focus on complex, high-value troubleshooting instead of alarm triage. The result is a leaner, more effective operations team capable of managing a larger network footprint.

Beyond fault detection, the workflow continuously analyzes performance trends to identify sub-optimal configurations—like a cell with excessive interference or poor handover success. It can either recommend or, within guardrails, autonomously execute parameter tuning (e.g., adjusting tilt, power, or handover thresholds). This proactive optimization squeezes more usable capacity from existing spectrum assets, delaying costly capacity upgrades and improving the customer experience during peak loads.

For operators in regulated markets or with stringent SLAs, the workflow automates the collection, validation, and structuring of performance data required for compliance reports (e.g., FCC coverage maps, QoS commitments). By providing a continuous, auditable log of network health and all automated actions taken, it reduces the manual effort and error risk associated with periodic reporting, while creating a defensible record of network stewardship.

When deploying a new feature (e.g., Carrier Aggregation) or a new vendor's equipment, the workflow provides a continuous validation layer. It establishes a performance baseline and monitors for deviations post-activation, quickly identifying rollout issues that could impact service. This reduces the risk and time associated with change management, allowing faster, more confident deployment of revenue-generating services and technology migrations.

This foundational agent acts as the workflow's central nervous system. It continuously pulls structured KPIs (e.g., RSRP, SINR, handover success rate) and unstructured logs from RAN elements (O-RAN RIC, EMS/NMS), core network probes, and performance management systems. It normalizes, timestamps, and fuses this data into a unified event stream, resolving data schema conflicts and handling backpressure from high-volume sources. Without this robust ingestion layer, downstream anomaly detection lacks the consistent, high-fidelity input required for accurate analysis.

This is the core analytical component. It employs an ensemble of models—including unsupervised clustering for novel fault discovery, supervised classifiers for known issue patterns, and time-series forecasting to predict metric deviations—running in parallel on the fused telemetry stream. The engine identifies subtle, correlated degradations (e.g., a gradual rise in uplink noise coupled with an increase in RACH failures) that traditional single-threshold alarms would miss. It outputs a scored, classified anomaly event with associated network elements and a preliminary confidence score.

Upon receiving an anomaly event, this agent performs a deep-dive investigation. It queries the network's digital twin and CMDB for topology context, retrieves recent configuration changes, and correlates the anomaly with other active alarms or maintenance windows using a graph-based reasoning model. Its goal is to move from a symptom ("high packet loss in Sector A") to a probable root cause ("faulty RF unit on RRH ID-4512 following a software patch"). It enriches the incident ticket with evidence and a ranked list of likely causes for the next phase.

This component decides on and executes the response. It matches the diagnosed root cause against a library of pre-approved, automated remediation playbooks (e.g., "restart radio unit," "adjust neighbor list," "trigger cell sector failover"). For actions requiring approval or those outside defined policies, it routes the incident with recommended actions to the appropriate NOC engineer queue via ServiceNow or similar ITSM integration. It manages the execution of automated scripts via RIC (RAN Intelligent Controller) APIs or EMS northbound interfaces, monitoring for completion and verifying resolution.

Critical for production governance, this system provides end-to-end visibility. It logs every agent decision, data point used, and action taken into an immutable audit trail for compliance and post-mortems. A dashboard visualizes workflow health, auto-remediation success rates, and mean-time-to-identification (MTTI) trends. Most importantly, it manages a feedback loop where NOC engineer overrides and final incident resolutions are used to retrain and improve the anomaly detection and diagnostic models, ensuring the system learns from operational experience.

This is the connective tissue that makes the workflow operational. It provides secure, standardized API adapters for the diverse systems involved: O-RAN RIC (for near-real-time control), legacy EMS/NMS (SNMP, NETCONF), ITSM platforms (ServiceNow, Jira), data lakes (Hadoop, Snowflake), and notification channels (Slack, PagerDuty). This layer handles authentication, protocol translation, rate limiting, and failover, insulating the core agents from the complexity and instability of backend network and IT systems. Its robustness directly determines the workflow's scalability and reliability.