IT Operations Monitor

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Overview

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What the Agent Needs to Accomplish

• Monitor system health and status
• Analyze performance metrics
• Track errors and incidents
• Generate incident reports
• Identify performance degradation
• Recommend optimizations

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How Pylar Helps

• Unified Operations View: Combining system logs, metrics, and error data
• Real-time Monitoring: Querying current system status
• Automated Analysis: Performance and error analysis
• Incident Management: Automated incident detection and reporting

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Without Pylar vs With Pylar

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Without Pylar

• ❌ Multiple monitoring tools
• ❌ Manual log analysis
• ❌ Time-consuming incident detection
• ❌ Limited correlation

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With Pylar

• ✅ Single endpoint for operations data
• ✅ Real-time monitoring
• ✅ Automated incident detection
• ✅ Comprehensive visibility

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Step-by-Step Implementation

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Step 1: Connect Data Sources

1. Connect System Logs (Application logs, server logs)
2. Connect Metrics (Performance metrics, system stats)
3. Connect Error Tracking (Error logs, exceptions)

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Step 2: Create Operations Views

CREATE VIEW system_health AS
SELECT 
  s.server_id,
  s.server_name,
  s.status,
  m.cpu_usage,
  m.memory_usage,
  m.disk_usage,
  e.error_count_last_hour,
  e.critical_errors,
  -- Health score
  CASE 
    WHEN s.status = 'Down' THEN 0
    WHEN e.critical_errors > 0 THEN 25
    WHEN m.cpu_usage > 90 OR m.memory_usage > 90 THEN 50
    WHEN e.error_count_last_hour > 100 THEN 75
    ELSE 100
  END as health_score
FROM infrastructure.servers s
LEFT JOIN metrics.server_metrics m ON s.server_id = m.server_id
LEFT JOIN errors.error_summary e ON s.server_id = e.server_id;

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Step 3: Create MCP Tools

• monitor_system_health(server_id: string, service_name: string)

• analyze_performance(server_id: string, hours_back: number)

• track_errors(server_id: string, error_type: string, hours_back: number)

• generate_incident_report(incident_id: string, include_metrics: boolean)

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Example Agent Interactions

• Overall Status: Healthy (95/100)
• Servers: 12/12 online
• Issues:
  • Server DB-02: High memory usage (92%)
  • API-01: Increased error rate (150 errors/hour)
• Recommendations:
  • Scale DB-02 memory
  • Investigate API-01 error spike”

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Outcomes

• Incident Detection: 70% faster
• Uptime: 99.9% uptime achieved
• MTTR: 40% reduction in mean time to repair
• Efficiency: 60% improvement in operations efficiency

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Next Steps

• Supply Chain Coordinator Example
• Product Usage Analyst Example