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The loop that never skips a step

A Tier-1 telecom operator in North America now resolves around 60% of recurring faults autonomously end to end on x101, identifies root cause around 50% faster, and sees around 35% fewer repeat incidents. The network had told its story across seven data domains, and no two told it the same way. x101 closes the loop in four stages: problems discovered from symptoms, root causes diagnosed through trees built from rules, models and the operator's own documents, remediation executed under governance, and every loop closed against quantitative metrics.

Telecom · North America

Industry
Tier-1 telecom operator
Scope
Self-healing and closed-loop network automation
Data
Fault, performance, configuration, topology, telemetry, logs, operational streams
Pattern
Four-stage autonomous loop

Last updated: 23 September 2026

4Stage closed loop: discovery, diagnosis, remediation, closure
7Network data domains correlated into one view
~60%Of recurring faults resolved autonomously, end to end
~50%Faster root-cause identification across the network
~35%Fewer repeat incidents through persistent-cause analytics
100%Of automated fixes carry full provenance

The challenge

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.

  • Symptoms without discovery

    Anomalies surfaced one domain at a time, and recognising them as a single network problem was manual pattern-matching.

  • Diagnosis as tribal knowledge

    Root-cause reasoning lived in senior engineers' heads and scattered documents, unrepeatable and unavailable at three in the morning.

  • Manual remediation

    Every fix depended on a person raising tickets, chasing workflows and executing each procedure by hand.

  • No loop closure

    Whether a fix worked was rarely measured: issues closed on hope and reopened on evidence.

  • Repeat offenders untracked

    Persistent root causes kept returning because nothing aggregated them across the network.

  • No provenance

    When automation did act, nobody could trace which logic fired, what path it took, or why.

What x101 does

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.

  1. Discovery from symptoms

    Models and custom rules run across all seven data domains, so issues are detected from symptoms rather than waiting for an alarm.

  2. Diagnosis made executable

    Root-cause trees combine rules, models and knowledge documents, turning the operator's diagnostic experience into something repeatable.

  3. Remediation under governance

    The identified cause drives the fix: tickets, change requests, workflows, notifications and procedures, autonomous or manual as configured.

  4. Closure on numbers

    Quantitative metrics decide the outcome, recorded as closed, on hold or failed. No issue is closed on hope alone.

  5. Configured by the operator

    A drag-and-drop modeller puts every stage in the operator's hands: data sources, root-cause trees, actions and closure metrics.

  6. Provenance and pattern

    Estate-level analytics surface top offenders and persistent causes, and every executed fix carries its full traversal and action trail.

Autonomy with a paper trail. Every automated correction records which reasoning path fired, what evidence supported it, what actions ran and how the loop closed, so "the system fixed it" is always a statement with provenance attached.

The impact

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.

  • ~60%

    Resolved end to end

    Recurring faults handled autonomously through the full loop.

  • ~50%

    Faster root cause

    Diagnosis made executable rather than remembered.

  • ~35%

    Fewer repeats

    Persistent causes surfaced and removed rather than re-fixed.

  • 100%

    Fixes traceable

    Every automated correction carries its evidence and action trail.

Network fault handling before and after the closed loop
DimensionBeforeAfter · with x101
DetectionAlarm-chasing, domain by domainSymptom and anomaly-driven discovery across seven data domains
DiagnosisTribal knowledge, unrepeatableRoot-cause trees from rules, models and knowledge documentsAround 50% faster to root cause
RemediationManual tickets, workflows and proceduresGoverned auto-correction, around 60% of recurring faults resolved end to end
VerificationClosed on hope, reopened on evidenceQuantitative feedback with recorded terminal states
Repeat incidentsSame causes returning, untrackedTop-offender analytics, around 35% fewer repeats
TraceabilityAutomation as a black boxFull 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

The parts of x101 this uses

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.

Close the loop on your network operations.

Symptom-driven discovery, executable root-cause analysis, governed remediation and quantitative closure, configured to your network.

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