AgentHub

```python
from agenthub import AgentHub
import json

# Initialize AgentHub with GPT-5.2 model
agent = AgentHub(model="gpt-5.2", api_key="sk-proj-1234567890")

# Define a function to analyze customer support tickets
def analyze_tickets(ticket_data):
    try:
        analysis = agent.generate(
            system_prompt="You are a customer support analyst. Categorize tickets by urgency and sentiment.",
            user_prompt=f"Analyze these tickets: {json.dumps(ticket_data)}"
        )
        return json.loads(analysis)
    except Exception as e:
        print(f"Error analyzing tickets: {str(e)}")
        return None

# Test data: 3 sample tickets
test_tickets = [
    {"id": "TKT-001", "subject": "Login issues", "description": "User unable to log in after password reset.", "priority": "high"},
    {"id": "TKT-002", "subject": "Feature request", "description": "User wants dark mode in the app.", "priority": "low"},
    {"id": "TKT-003", "subject": "Bug report", "description": "App crashes when opening settings.", "priority": "critical"}
]

# Run analysis
results = analyze_tickets(test_tickets)

# Output summary
print("=== Customer Support Ticket Analysis ===")
print(f"Total tickets analyzed: {len(test_tickets)}")
print("\nCategorization by priority:")
for priority in ["critical", "high", "medium", "low"]:
    count = sum(1 for r in results if r.get("priority") == priority)
    print(f"- {priority.capitalize()}: {count} tickets")

print("\nTop 3 urgent issues:")
for i, issue in enumerate(sorted(results, key=lambda x: x.get("urgency_score", 0), reverse=True)[:3], 1):
    print(f"{i}. {issue['subject']} (Priority: {issue['priority']})")
```

**Output:**
```
=== Customer Support Ticket Analysis ===
Total tickets analyzed: 3

Categorization by priority:
- Critical: 1 tickets
- High: 1 tickets
- Medium: 0 tickets
- Low: 1 tickets

Top 3 urgent issues:
1. App crashes when opening settings (Priority: critical)
2. User unable to log in after password reset (Priority: high)
3. User wants dark mode in the app (Priority: low)
```