# Building Your First AI Agent

<Info>
This guide walks you through creating your first AI agent on the AgentArea platform. We'll cover everything from basic setup to advanced agent behaviors.
</Info>

## 🎯 What You'll Learn

By the end of this guide, you'll know how to:
- Create and configure AI agents
- Set up agent personalities and behaviors  
- Connect agents to external tools via MCP
- Enable multi-agent communication
- Deploy and monitor your agents

## 🏗️ Agent Architecture

Every AgentArea agent consists of several key components:

<Tabs>
  <Tab title="Core Identity">
    **Name & Personality**: Define who your agent is
    ```json
    {
      "name": "Customer Support Agent",
      "personality": "helpful, patient, professional",
      "role": "customer service representative"
    }
    ```
  </Tab>
  
  <Tab title="Knowledge Base">
    **Context & Memory**: What your agent knows
    ```json
    {
      "system_prompt": "You are a customer support agent...",
      "knowledge_sources": ["faq.md", "product_docs.md"],
      "memory_enabled": true
    }
    ```
  </Tab>
  
  <Tab title="Capabilities">
    **Tools & Actions**: What your agent can do
    ```json
    {
      "mcp_tools": ["web_search", "email_sender"],
      "api_integrations": ["crm_system", "payment_processor"],
      "agent_communication": true
    }
    ```
  </Tab>
</Tabs>

## 🚀 Creating Your First Agent

<Steps>
  <Step title="Choose a Template">
    Start with one of our pre-built templates:
    
    <CardGroup cols={2}>
      <Card title="Chatbot" icon="message-circle">
        Simple conversational agent
      </Card>
      <Card title="Task Assistant" icon="check-circle">
        Agent that can perform specific tasks
      </Card>
      <Card title="Customer Support" icon="headphones">
        Specialized for customer service
      </Card>
      <Card title="Data Analyst" icon="bar-chart">
        Agent that can analyze and report on data
      </Card>
    </CardGroup>
  </Step>
  
  <Step title="Configure Basic Settings">
    Set up your agent's identity:
    
    ```bash
    curl -X POST http://localhost:8000/v1/agents \
      -H "Content-Type: application/json" \
      -d '{
        "name": "My Assistant",
        "template": "task_assistant",
        "personality": "helpful and efficient",
        "system_prompt": "You are a helpful assistant that can help users with various tasks."
      }'
    ```
  </Step>
  
  <Step title="Add Knowledge Sources">
    Connect your agent to relevant information:
    
    ```bash
    curl -X POST http://localhost:8000/v1/agents/{agent_id}/knowledge \
      -H "Content-Type: application/json" \
      -d '{
        "sources": [
          {"type": "document", "path": "/docs/user_manual.pdf"},
          {"type": "url", "url": "http://localhost:8000/api/docs"},
          {"type": "database", "connection": "postgres://..."}
        ]
      }'
    ```
  </Step>
  
  <Step title="Enable Tools & Integrations">
    Give your agent superpowers with MCP tools:
    
    ```bash
    curl -X POST http://localhost:8000/v1/agents/{agent_id}/tools \
      -H "Content-Type: application/json" \
      -d '{
        "mcp_tools": [
          {"name": "web_search", "enabled": true},
          {"name": "email_sender", "enabled": true, "config": {...}},
          {"name": "calendar_manager", "enabled": true}
        ]
      }'
    ```
  </Step>
</Steps>

## 🎨 Customizing Agent Behavior

### Personality & Communication Style

Define how your agent communicates:

```yaml
personality:
  tone: "friendly and professional"
  style: "concise but thorough"
  quirks: 
    - "Uses emojis sparingly"
    - "Always asks clarifying questions"
    - "Provides step-by-step guidance"

communication_rules:
  - "Always greet users warmly"
  - "Confirm understanding before taking action"
  - "Provide progress updates for long tasks"
  - "Escalate complex issues to human agents"
```

### Knowledge Management

<Tabs>
  <Tab title="Static Knowledge">
    Upload documents, FAQs, and manuals:
    
    ```bash
    # Upload a PDF manual
    curl -X POST http://localhost:8000/v1/agents/{agent_id}/knowledge/upload \
      -F "file=@user_manual.pdf" \
      -F "type=manual"
    
    # Add FAQ entries
    curl -X POST http://localhost:8000/v1/agents/{agent_id}/knowledge/faq \
      -d '{"question": "How do I reset my password?", "answer": "..."}'
    ```
  </Tab>
  
  <Tab title="Dynamic Knowledge">
    Connect to live data sources:
    
    ```json
    {
      "live_sources": [
        {
          "type": "api",
          "url": "https://api.company.com/status",
          "refresh_interval": "5m",
          "description": "System status information"
        },
        {
          "type": "database",
          "query": "SELECT * FROM products WHERE active = true",
          "refresh_interval": "1h",
          "description": "Current product catalog"
        }
      ]
    }
    ```
  </Tab>
  
  <Tab title="Learning & Memory">
    Enable continuous learning:
    
    ```json
    {
      "memory_settings": {
        "conversation_memory": true,
        "user_preferences": true,
        "learning_enabled": true,
        "feedback_integration": true
      }
    }
    ```
  </Tab>
</Tabs>

## 🔗 Multi-Agent Communication

Enable your agents to work together:

### Agent-to-Agent Messaging

```python
# Python SDK example
from agentarea import Agent, MessageType

# Create agents
support_agent = Agent("customer_support")
billing_agent = Agent("billing_specialist")

# Enable communication
support_agent.enable_communication([billing_agent.id])

# Send a message
support_agent.send_message(
    to_agent=billing_agent.id,
    message="Customer needs help with billing issue #12345",
    message_type=MessageType.TASK_REQUEST,
    context={"customer_id": "cust_123", "issue_id": "12345"}
)
```

### Workflow Orchestration

```yaml
workflows:
  customer_onboarding:
    trigger: "new_customer_signup"
    agents:
      - agent: "welcome_agent"
        task: "send_welcome_message"
        next: "setup_agent"
      
      - agent: "setup_agent" 
        task: "guide_initial_setup"
        conditions:
          - if: "setup_complete"
            next: "success_agent"
          - if: "needs_help"
            next: "support_agent"
      
      - agent: "support_agent"
        task: "provide_human_assistance"
        escalation: true
```

## 🛠️ Advanced Features

### Custom Tool Development

Create your own MCP tools:

```python
# tools/custom_crm.py
from mcp import MCPTool
import requests

class CRMTool(MCPTool):
    name = "crm_lookup"
    description = "Look up customer information in CRM"
    
    def execute(self, customer_id: str):
        response = requests.get(f"/api/customers/{customer_id}")
        return response.json()

# Register the tool
agent.register_tool(CRMTool())
```

### Event-Driven Behavior

Respond to external events:

```json
{
  "event_handlers": [
    {
      "event": "user_login",
      "action": "send_greeting",
      "conditions": ["first_login_today"]
    },
    {
      "event": "order_placed",
      "action": "send_confirmation",
      "delay": "2m"
    },
    {
      "event": "payment_failed",
      "action": "escalate_to_human",
      "urgency": "high"
    }
  ]
}
```

## 📊 Monitoring & Analytics

Track your agent's performance:

<CardGroup cols={2}>
  <Card title="Conversation Metrics" icon="message-square">
    - Response time
    - User satisfaction scores
    - Conversation completion rates
    - Escalation frequency
  </Card>
  
  <Card title="System Metrics" icon="activity">
    - CPU and memory usage
    - API call latency
    - Tool execution success rates
    - Error rates and types
  </Card>
</CardGroup>

### Dashboard Access

```bash
# View agent metrics
curl http://localhost:8000/v1/agents/{agent_id}/metrics

# Export conversation logs  
curl http://localhost:8000/v1/agents/{agent_id}/conversations/export
```

## 🚀 Deployment & Scaling

### Development to Production

<Steps>
  <Step title="Test Locally">
    ```bash
    # Run agent in development mode
    agentarea dev start --agent my_agent
    ```
  </Step>
  
  <Step title="Validate Configuration">
    ```bash
    # Validate agent configuration
    agentarea validate --agent my_agent
    ```
  </Step>
  
  <Step title="Deploy to Staging">
    ```bash
    # Deploy to staging environment
    agentarea deploy staging --agent my_agent
    ```
  </Step>
  
  <Step title="Production Deployment">
    ```bash
    # Deploy to production with scaling
    agentarea deploy production --agent my_agent --replicas 3
    ```
  </Step>
</Steps>

## 💡 Best Practices

<Tip>
**Agent Design Tips**
- Keep agent personalities consistent and clear
- Provide comprehensive system prompts
- Test with real user scenarios
- Monitor and iterate based on feedback
</Tip>

<Warning>
**Common Pitfalls**
- Don't make agents too complex initially
- Avoid overlapping agent responsibilities  
- Always handle error cases gracefully
- Test multi-agent interactions thoroughly
</Warning>

## 🆘 Troubleshooting

### Common Issues

<Accordion>
  <AccordionItem title="Agent not responding">
    Check agent status and logs:
    ```bash
    agentarea status --agent my_agent
    agentarea logs --agent my_agent --tail 100
    ```
  </AccordionItem>
  
  <AccordionItem title="Tool integration failing">
    Verify MCP tool configuration:
    ```bash
    agentarea tools test --agent my_agent --tool web_search
    ```
  </AccordionItem>
  
  <AccordionItem title="Poor response quality">
    Review and improve system prompts:
    ```bash
    agentarea prompt analyze --agent my_agent
    agentarea prompt optimize --agent my_agent
    ```
  </AccordionItem>
</Accordion>

## 📚 Next Steps

<CardGroup cols={3}>
  <Card title="Advanced Agent Communication" icon="network" href="/agent-communication">
    Learn complex multi-agent patterns
  </Card>
  
  <Card title="MCP Integration Guide" icon="plug" href="/mcp-integration">
    Deep dive into Model Context Protocol
  </Card>
  
  <Card title="Production Deployment" icon="cloud" href="/deployment">
    Scale your agents to production
  </Card>
</CardGroup>

---

<Note>
Need help? Check out [GitHub Discussions](https://github.com/agentarea/agentarea/discussions) or the [API Reference](/api-reference) for detailed technical documentation.
</Note>