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Proactive Problem Resolution

Introduction​

Proactive problem resolution involves anticipating, detecting, and resolving faults before they impact customers. Leveraging the bleeding-edge architecture, this process uses AI-driven predictions from Cerebro and the Knowledge Graph to identify potential issues early. The goal is to achieve zero customer-reported incidents through automation and intelligence, aligning with 2026 standards where AI agents like Sparky handle 80% of resolutions autonomously.

This section provides verbose descriptions of workflows, roles, tools, and integration points, assuming a clean-slate implementation.

Key Principles​

  • Predictive Analytics: Use machine learning models in Cerebro (powered by AWS SageMaker) to forecast failures based on historical telemetry.
  • Automation First: Sparky agent triages and resolves issues without human intervention where possible.
  • Integration with Architecture: Telemetry from all components feeds into the Knowledge Graph for real-time augmentation and decision-making.
  • Metrics for Success: Track Mean Time to Detect (MTTD) < 5 minutes, Mean Time to Resolve (MTTR) < 15 minutes for proactive cases.

Verbose Workflow: Proactive Fault Detection and Resolution​

This workflow is triggered by continuous monitoring and runs asynchronously via the Autonomous Engine.

  1. Data Ingestion and Monitoring:

    • All architectural components (e.g., Platform Kubernetes clusters, Applications like Control Center) emit telemetry data (logs, metrics, traces) to the Observability API.
    • Data is ingested into the Knowledge Graph:
      • Bronze Layer: Raw, unstructured data storage for immediate access.
      • Silver Layer: Structured and cleaned data, with basic connections (e.g., linking API call logs to customer sessions).
      • Gold Layer: Augmented with AI insights, such as anomaly scores or predictive failure probabilities.
    • Tools: New Relic for metrics aggregation, Sentry for error capturing, integrated with Cloudflare for edge-level monitoring.

  2. Anomaly Detection:

    • Cerebro Cognitive Platform analyzes gold-layer data using AWS Bedrock for inference on pre-trained models and SageMaker for custom training on XOPS-specific patterns (e.g., detecting patterns leading to AWS zone failures).
    • Thresholds: If anomaly score > 0.7 (based on historical baselines), trigger alert.
    • Examples of anomalies: Unusual spike in API latency, degrading performance in service mesh, or impending token expiration in ecosystem integrations.

  3. Sparky Agent Activation:

    • Trigger: Webhook from Sentry (for errors) or Jira Service Management (for flagged incidents) activates Sparky.
    • L1 Triage (Automated Analysis):
      • Sparky queries the Knowledge Graph for correlated data (e.g., "Is this latency spike linked to a recent deployment?").
      • Uses Autonomous Engine to run diagnostic scripts (e.g., check pod health in Kubernetes).
      • If resolvable automatically (e.g., restart a pod), Sparky executes via Platform API.
    • L2 Remediation (Code-Level Fixes):
      • If issue requires code changes (e.g., inefficient query in Knowledge Graph), Sparky generates a fix using AI code generation.
      • Creates a GitHub Pull Request (PR) with detailed description, including root cause analysis and test cases.
      • Notifies L3 engineers via PagerDuty for review.
    • L3 Escalation (Human Oversight):
      • For complex issues (e.g., architectural flaws), Sparky escalates to engineering team with a pre-filled Jira ticket containing all diagnostics.
      • Engineers review PR, merge, and deploy via CI/CD pipeline.

  4. Resolution and Notification:

    • Post-resolution, Sparky updates the Knowledge Graph with resolution details for future learning.
    • Autonomous notification to customers if relevant (e.g., "We've proactively resolved a potential performance issue in your tenant").
    • Update operational dashboards with resolution metrics.

  5. Post-Resolution Review and Learning:

    • Automated post-mortem generated by Cerebro: Includes timeline, root cause, and recommendations.
    • Feed back into SageMaker for model retraining to improve prediction accuracy.
    • Quarterly review: Analyze all proactive resolutions to refine thresholds and workflows.

Tools and Integrations​

  • Monitoring Tools: New Relic (performance metrics), Sentry (error tracking), PagerDuty (alerting).
  • AI Components: Cerebro with AWS Bedrock/SageMaker for predictions.
  • Automation: Sparky integrated with GitHub for PRs, Jira for tickets.
  • Security: All actions adhere to zero-trust principles, with access controls rotated quarterly.

Examples​

  • Scenario: Impending API Token Expiration: Cerebro detects expiration in 7 days via Knowledge Graph scan; Sparky notifies customer autonomously and suggests renewal steps.
  • Scenario: Performance Degradation: Predictive model flags potential overload; Sparky auto-scales resources before impact.

This workflow reduces toil by automating 70-80% of resolutions, ensuring world-class reliability.