> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pylar.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# IT Operations Monitor

> Build an AI agent that monitors system health, analyzes performance metrics, and generates incident reports

## Overview

An IT Operations Monitor powered by Pylar monitors system health, analyzes performance metrics, tracks errors, and generates incident reports to ensure reliable infrastructure.

## What the Agent Needs to Accomplish

The agent must:

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

## How Pylar Helps

Pylar enables the agent by:

* **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

```mermaid theme={null}
graph LR
    subgraph "Data Sources"
        LOGS[System Logs]
        METRICS[Metrics]
        ERRORS[Error Data]
    end
    
    PYLAR[Pylar<br/>Unified Views]
    
    TOOLS[MCP Tools<br/>monitor_health<br/>analyze_performance<br/>generate_reports]
    
    AGENT[IT Monitor]
    TEAM[IT Team]
    
    LOGS --> PYLAR
    METRICS --> PYLAR
    ERRORS --> PYLAR
    PYLAR --> TOOLS
    TOOLS --> AGENT
    AGENT --> TEAM
    
    style PYLAR fill:#FF4017,stroke:#CC3300,color:#fff
```

## Without Pylar vs With Pylar

### Without Pylar

**Challenges**:

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

**Implementation Complexity**: \~5-6 weeks

### With Pylar

**Benefits**:

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

**Implementation Complexity**: \~6-7 hours

## Step-by-Step Implementation

### 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)

### Step 2: Create Operations Views

**System Health View**:

```sql theme={null}
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;
```

### Step 3: Create MCP Tools

**Tool 1: Monitor System Health**

* `monitor_system_health(server_id: string, service_name: string)`

**Tool 2: Analyze Performance**

* `analyze_performance(server_id: string, hours_back: number)`

**Tool 3: Track Errors**

* `track_errors(server_id: string, error_type: string, hours_back: number)`

**Tool 4: Generate Incident Report**

* `generate_incident_report(incident_id: string, include_metrics: boolean)`

## Example Agent Interactions

**User**: "What's the system health status?"

**Agent**: "System Health Report:

* 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"

## Outcomes

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

## Next Steps

* [Supply Chain Coordinator Example](/examples/supply-chain-coordinator)
* [Product Usage Analyst Example](/examples/product-usage-analyst)