Agent Servers Overview
Agent servers are complete orchestration engines that provide intelligent workflow generation, multi-agent coordination, and autonomous execution capabilities. Unlike simple MCP providers, agent servers offer comprehensive workflow management with advanced AI reasoning.
What Are Agent Servers?
Agent servers combine multiple AI agents and tools to:
- π§ Generate complex workflows from natural language descriptions
- βοΈ Execute multi-step processes with error handling and retries
- π Coordinate multiple agents for complex problem-solving
- π Monitor and optimize workflow performance
- π‘οΈ Provide enterprise features like authentication and auditing
Available Agent Servers
Agent Servers vs MCP Providers
π’ Agent Servers (Complex Workflows)
Best for:
- Multi-step business processes
- Complex deployment pipelines
- Data processing workflows
- Enterprise automation
Features:
- Intelligent workflow planning
- Multi-agent coordination
- Error handling and recovery
- Streaming execution feedback
- Enterprise security and compliance
Example Use Cases:
- βDeploy my application to staging and run testsβ
- βSet up a complete CI/CD pipeline for my repositoryβ
- βCreate a data processing workflow that handles failures gracefullyβ
π MCP Providers (AI Assistant Tools)
Architecture Comparison
Getting Started with Agent Servers
Quick Decision Guide
Complex workflow requirements?
π Use Agent Servers - continue below
Choose your implementation
- Google ADK: Best for most use cases, production-ready
- Custom Server: Full control, custom requirements
Set up your environment
Follow the specific setup guide for your chosen server type
Minimum Requirements
- Python 3.10+ with virtual environment
- API Keys: Kubiya + AI provider (Together AI, OpenAI, etc.)
- Memory: 2GB+ for local development
- Network: Outbound HTTPS access for AI APIs
Google ADK (Recommended)
The Google Agent Development Kit provides the most advanced workflow generation capabilities:
Key Features
π§ Intelligent Workflow Planning
- Multi-step workflow decomposition
- Context-aware task planning
- Automatic error handling generation
- Resource optimization
π₯ Multi-Agent Coordination
- Sequential agent execution
- Parallel processing capabilities
- Dynamic agent selection
- Cross-agent context sharing
- Real-time streaming responses
- Comprehensive error handling
- Health monitoring and metrics
- Docker deployment support
π§ Developer Experience
- Hot reloading for development
- Extensive logging and debugging
- Visual workflow inspection
- Easy integration with existing systems
Quick ADK Setup
# Install dependencies
pip install kubiya-workflow-sdk google-adk
# Set environment variables
export KUBIYA_API_KEY="your-key"
export TOGETHER_API_KEY="your-key"
# Start ADK server
python3 workflow_sdk/adk_orchestration_server.py
π Complete ADK Setup Guide β
Custom Agent Servers
Build your own agent server when you need:
- Custom AI models or providers
- Specialized workflow types for your domain
- Unique security requirements
- Custom integrations with proprietary systems
Basic Custom Server
from kubiya_workflow_sdk.server import OrchestrationServer
from kubiya_workflow_sdk.providers import CustomProvider
class MyCustomProvider(CustomProvider):
async def compose(self, task: str, mode: str):
# Your custom workflow generation logic
workflow = await self.generate_workflow(task)
if mode == "act":
result = await self.execute_workflow(workflow)
return result
return workflow
# Create and start server
server = OrchestrationServer(provider=MyCustomProvider())
server.start(port=8001)
π Build Custom Agent Server β
Integration Examples
Frontend Integration
// Detect agent server capabilities
const serverResponse = await fetch('http://localhost:8001/discover');
const capabilities = await serverResponse.json();
if (capabilities.server.capabilities.orchestration) {
// This is an agent server - can handle complex workflows
const workflowResponse = await fetch('http://localhost:8001/compose', {
method: 'POST',
body: JSON.stringify({
prompt: "Deploy my application to staging",
mode: "act"
})
});
}
MCP Integration
# Expose agent server capabilities through MCP
@mcp.tool()
def execute_complex_workflow(description: str, mode: str = "plan"):
"""Execute a complex multi-step workflow"""
return agent_server.compose(task=description, mode=mode)
Production Considerations
Security
- API Authentication: Always use API keys in production
- Rate Limiting: Implement request quotas
- Input Validation: Sanitize all user inputs
- Audit Logging: Track all workflow executions
Scalability
- Load Balancing: Multiple server instances
- Caching: Workflow templates and responses
- Monitoring: Health checks and metrics
- Resource Limits: Memory and CPU constraints
Reliability
- Error Recovery: Graceful failure handling
- Retry Logic: Automatic error recovery
- Health Monitoring: Continuous server monitoring
- Backup Strategies: Data persistence and recovery
Next Steps
Ready to build? Choose your agent server type above and follow the detailed setup guide!
Responses are generated using AI and may contain mistakes.