Faults, performance counters, configuration, inventory and topology, telemetry, logs and operational streams each described the network differently. Root-causing meant human correlation across all seven, fixing meant manual action, and the same faults returned because nothing closed the loop behind them.
Anomalies surfaced one domain at a time, and recognising them as a single network problem was manual pattern-matching.
Root-cause reasoning lived in senior engineers' heads and scattered documents, unrepeatable and unavailable at three in the morning.
Every fix meant a person raising tickets, chasing workflows and executing procedures by hand.
Whether a fix actually worked was rarely measured, so issues were closed on hope and reopened on evidence.
Persistent, frequent root causes kept returning because nothing aggregated them across the network.
When automation did act, nobody could trace which logic fired, what path it took, or why.
x101 closed the loop in four configurable stages. Discovery watches every data domain for symptoms and anomalies. Diagnosis walks root-cause trees assembled from rules, models and the operator's own knowledge documents. Remediation executes under governance across tickets, change requests, workflows and custom procedures. And closure is decided by quantitative metrics, with closed, on-hold or failed recorded as terminal states.
Models and custom rules run across all seven data domains, so issues are detected from symptoms rather than waiting for an alarm.
Root-cause trees combine rules, models and knowledge documents, turning the operator's diagnostic experience into something repeatable.
The identified cause drives the correction: tickets, change requests, workflow orchestration, notifications and custom procedures, autonomous or manual as configured.
Quantitative metrics decide the outcome, recorded as closed, on hold or failed. No issue is closed on hope.
A drag-and-drop modeller puts every stage in the operator's hands: data sources, root-cause trees, actions and closure metrics.
Estate-level analytics surface top offenders and persistent causes, and every executed fix carries its full traversal and action trail.
Recurring faults stopped consuming the team. The loop discovers, diagnoses, fixes and verifies on its own for the well-understood majority of network problems, and hands engineers a complete evidence trail for everything else.
Recurring faults handled autonomously through the full loop.
Diagnosis made executable rather than remembered.
Persistent causes surfaced and removed rather than re-fixed.
Every automated correction carries its evidence and action trail.
| Dimension | Before | After · with x101 |
|---|---|---|
| Detection | Alarm-chasing, domain by domain | Symptom and anomaly-driven discovery across seven data domains |
| Diagnosis | Tribal knowledge, unrepeatable | Root-cause trees from rules, models and knowledge documentsAround 50% faster to root cause |
| Remediation | Manual tickets, workflows and procedures | Governed auto-correction, around 60% of recurring faults resolved end to end |
| Verification | Closed on hope, reopened on evidence | Quantitative feedback with recorded terminal states |
| Repeat incidents | Same causes returning, untracked | Top-offender analytics, around 35% fewer repeats |
| Traceability | Automation as a black box | Full provenance of every reasoning traversal and action taken |
Self-healing is not one clever action, it is a loop that never skips a step: discover from symptoms, diagnose with the operator's own knowledge made executable, act under governance, and refuse to close until the numbers say fixed. The provenance is what makes the autonomy trustworthy.
Solution summary · x101 closed-loop network automation deployment
Nothing here was built for one customer. Each capability below is standard platform behaviour, applied to this problem.
About this case study. The customer's identity is withheld at their request and is referred to throughout as a Tier-1 telecom operator in North America. Improvement figures reflect the target and expected outcomes of the deployment and are indicative; actual results vary with network scope, data quality and rollout phase.