Building Your First AI Agent 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.
🎯 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:
Core Identity
Knowledge Base
Capabilities
Name & Personality : Define who your agent is{ "name" : "Customer Support Agent" , "personality" : "helpful, patient, professional" , "role" : "customer service representative" } 🚀 Creating Your First Agent
Choose a Template
Start with one of our pre-built templates:
Chatbot Simple conversational agent
Task Assistant Agent that can perform specific tasks
Customer Support Specialized for customer service
Data Analyst Agent that can analyze and report on data
Configure Basic Settings
Set up your agent’s identity: 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." }'
Add Knowledge Sources
Connect your agent to relevant information: 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": "https://api.example.com/docs"}, {"type": "database", "connection": "postgres://..."} ] }'
Enable Tools & Integrations
Give your agent superpowers with MCP tools: 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} ] }' 🎨 Customizing Agent Behavior Personality & Communication Style Define how your agent communicates:
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 Static Knowledge
Dynamic Knowledge
Learning & Memory
Upload documents, FAQs, and manuals: # 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": "..."}' 🔗 Multi-Agent Communication Enable your agents to work together:
Agent-to-Agent Messaging # 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 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 Create your own MCP tools:
# 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:
{ "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:
Conversation Metrics
Response time
User satisfaction scores
Conversation completion rates
Escalation frequency
System Metrics
CPU and memory usage
API call latency
Tool execution success rates
Error rates and types
Dashboard Access # 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
Test Locally
# Run agent in development mode agentarea dev start --agent my_agent
Validate Configuration
# Validate agent configuration agentarea validate --agent my_agent
Deploy to Staging
# Deploy to staging environment agentarea deploy staging --agent my_agent
Production Deployment
# Deploy to production with scaling agentarea deploy production --agent my_agent --replicas 3 💡 Best Practices Agent Design Tips
Keep agent personalities consistent and clear
Provide comprehensive system prompts
Test with real user scenarios
Monitor and iterate based on feedback
Common Pitfalls
Don’t make agents too complex initially
Avoid overlapping agent responsibilities
Always handle error cases gracefully
Test multi-agent interactions thoroughly
🆘 Troubleshooting Common Issues
📚 Next Steps 
