Building on ARKOS
Extensible Platform Architecture
ARKOS provides a comprehensive framework for building custom solutions that extend platform capabilities while maintaining seamless integration with existing agents and workflows. The extensible architecture enables organizations to create specialized functionality that addresses unique requirements.
Custom Agent Development
Agent Development Framework: Comprehensive framework for creating custom agents that integrate seamlessly with the ARKOS ecosystem. Custom agents inherit core capabilities including learning systems, coordination mechanisms, and security features.
Specialized Domain Agents: Organizations can develop agents for specialized domains including industry-specific workflows, proprietary technologies, and custom business processes.
Agent Marketplace: Platform for sharing and distributing custom agents within organizations or with the broader ARKOS community, enabling collaboration and knowledge sharing.
Extension Development
# Custom ARKOS Agent Development Framework
from arkos.agent import BaseAgent, AgentCapability
from arkos.coordination import AgentCoordinator
from arkos.learning import LearningEngine
from arkos.security import SecurityContext
from typing import Dict, List, Any, Optional
import asyncio
class CustomDomainAgent(BaseAgent):
"""
Example custom agent for specialized domain requirements.
Demonstrates ARKOS agent development patterns and best practices.
"""
def __init__(self, config: Dict[str, Any]):
super().__init__(
name="custom-domain-agent",
version="1.0.0",
capabilities=[
AgentCapability.ANALYSIS,
AgentCapability.AUTOMATION,
AgentCapability.OPTIMIZATION,
AgentCapability.LEARNING
],
config=config
)
# Initialize custom components
self.domain_analyzer = DomainSpecificAnalyzer(config)
self.custom_optimizer = CustomOptimizer(config)
self.integration_manager = IntegrationManager(config)
async def initialize(self) -> bool:
"""
Initialize agent with ARKOS ecosystem integration.
"""
try:
# Register with agent coordinator
await self.coordinator.register_agent(
agent=self,
capabilities=self.capabilities,
dependencies=self.get_dependencies()
)
# Initialize learning engine
await self.learning_engine.initialize(
domain="custom_domain",
learning_rate=self.config.get('learning_rate', 'adaptive'),
model_type=self.config.get('model_type', 'transformer')
)
# Setup security context
await self.security_context.initialize(
permissions=self.get_required_permissions(),
encryption_requirements=self.get_encryption_requirements()
)
# Initialize domain-specific components
await self.domain_analyzer.initialize()
await self.custom_optimizer.initialize()
await self.integration_manager.initialize()
self.logger.info("Custom domain agent initialized successfully")
return True
except Exception as e:
self.logger.error(f"Failed to initialize custom agent: {str(e)}")
return False
async def process_request(self, request: Dict[str, Any]) -> Dict[str, Any]:
"""
Process incoming requests with domain-specific logic.
"""
try:
# Validate request with security context
if not await self.security_context.validate_request(request):
return self.create_error_response("Unauthorized request")
# Analyze request context
context = await self.analyze_request_context(request)
# Coordinate with other agents if needed
coordination_result = await self.coordinate_with_agents(context)
# Execute domain-specific processing
processing_result = await self.execute_domain_processing(
request, context, coordination_result
)
# Learn from processing results
await self.learning_engine.record_interaction(
request=request,
context=context,
result=processing_result,
feedback=request.get('feedback')
)
return self.create_success_response(processing_result)
except Exception as e:
self.logger.error(f"Request processing failed: {str(e)}")
return self.create_error_response(str(e))
async def coordinate_with_agents(self, context: Dict[str, Any]) -> Dict[str, Any]:
"""
Coordinate with other ARKOS agents based on request context.
"""
coordination_requests = []
# Determine which agents to coordinate with
if context.get('requires_code_analysis'):
coordination_requests.append({
'agent': 'nexus',
'action': 'analyze_code_quality',
'data': context.get('code_data')
})
if context.get('requires_security_check'):
coordination_requests.append({
'agent': 'aegis',
'action': 'security_analysis',
'data': context.get('security_data')
})
if context.get('requires_infrastructure'):
coordination_requests.append({
'agent': 'oracle',
'action': 'resource_analysis',
'data': context.get('infrastructure_data')
})
# Execute coordination requests
coordination_results = await self.coordinator.execute_coordinated_requests(
requests=coordination_requests,
timeout=self.config.get('coordination_timeout', 30)
)
return coordination_results
async def execute_domain_processing(
self,
request: Dict[str, Any],
context: Dict[str, Any],
coordination_result: Dict[str, Any]
) -> Dict[str, Any]:
"""
Execute domain-specific processing logic.
"""
# Custom domain analysis
analysis_result = await self.domain_analyzer.analyze(
data=request.get('data'),
context=context,
coordination_input=coordination_result
)
# Apply domain-specific optimizations
optimization_result = await self.custom_optimizer.optimize(
analysis=analysis_result,
constraints=request.get('constraints', {}),
objectives=request.get('objectives', {})
)
# Execute custom integrations
integration_result = await self.integration_manager.execute_integrations(
optimization=optimization_result,
target_systems=request.get('target_systems', [])
)
return {
'analysis': analysis_result,
'optimization': optimization_result,
'integration': integration_result,
'recommendations': await self.generate_recommendations(
analysis_result, optimization_result, integration_result
)
}
async def learn_from_feedback(self, feedback: Dict[str, Any]) -> None:
"""
Incorporate user feedback into learning system.
"""
await self.learning_engine.process_feedback(
feedback_data=feedback,
context=feedback.get('context'),
outcome=feedback.get('outcome')
)
# Update custom components based on learning
await self.domain_analyzer.update_from_learning()
await self.custom_optimizer.update_from_learning()
def get_dependencies(self) -> List[str]:
"""Define dependencies on other agents"""
return ['nexus', 'aegis', 'oracle'] # Customize based on needs
def get_required_permissions(self) -> List[str]:
"""Define required permissions for agent operation"""
return [
'read_project_data',
'execute_analysis',
'coordinate_with_agents',
'write_optimization_results'
]
class DomainSpecificAnalyzer:
"""Custom analyzer for specialized domain requirements"""
def __init__(self, config: Dict[str, Any]):
self.config = config
self.analysis_models = {}
async def initialize(self):
"""Initialize domain-specific analysis capabilities"""
# Load custom models, rules, and patterns
pass
async def analyze(
self,
data: Any,
context: Dict[str, Any],
coordination_input: Dict[str, Any]
) -> Dict[str, Any]:
"""Execute domain-specific analysis"""
# Implement custom analysis logic
return {
'analysis_type': 'domain_specific',
'findings': [],
'confidence': 0.95,
'recommendations': []
}
class CustomOptimizer:
"""Custom optimization engine for specialized requirements"""
def __init__(self, config: Dict[str, Any]):
self.config = config
async def initialize(self):
"""Initialize optimization engine"""
pass
async def optimize(
self,
analysis: Dict[str, Any],
constraints: Dict[str, Any],
objectives: Dict[str, Any]
) -> Dict[str, Any]:
"""Execute custom optimization logic"""
return {
'optimization_type': 'custom_domain',
'improvements': [],
'estimated_impact': {},
'implementation_plan': []
}
# Agent registration and deployment
async def deploy_custom_agent():
"""Deploy custom agent to ARKOS platform"""
config = {
'learning_rate': 'adaptive',
'coordination_timeout': 30,
'custom_parameters': {
'domain_specific_setting': 'value'
}
}
agent = CustomDomainAgent(config)
# Initialize and register agent
if await agent.initialize():
print("Custom agent deployed successfully")
return agent
else:
print("Failed to deploy custom agent")
return None
# Usage example
async def main():
custom_agent = await deploy_custom_agent()
if custom_agent:
# Process a custom request
request = {
'type': 'domain_analysis',
'data': {'custom_data': 'value'},
'objectives': {'optimize_for': 'performance'},
'target_systems': ['system1', 'system2']
}
result = await custom_agent.process_request(request)
print(f"Processing result: {result}")
if __name__ == "__main__":
asyncio.run(main())Workflow Customization
Custom Workflow Engine: Advanced workflow engine enables creation of sophisticated automation sequences that combine ARKOS agents with custom logic and external integrations.
Business Process Integration: Workflows can integrate with existing business processes including approval workflows, compliance procedures, and custom operational requirements.
Event-Driven Architecture: Support for complex event-driven workflows that respond to business events, system changes, and external triggers with sophisticated logic and coordination.
API Extension Framework
Custom API Endpoints: Framework for creating custom API endpoints that extend ARKOS functionality while maintaining security, authentication, and monitoring capabilities.
Business Logic Integration: Custom endpoints can implement business-specific logic while leveraging ARKOS agent capabilities for analysis, optimization, and automation.
Third-Party Integration: Simplified framework for creating integrations with proprietary systems, legacy applications, and specialized tools.
Plugin Architecture
Modular Extensions: Plugin architecture enables development of modular extensions that add functionality without modifying core platform code.
Community Plugins: Support for community-developed plugins that extend platform capabilities and can be shared across organizations.
Enterprise Plugins: Framework for developing enterprise-specific plugins that address unique organizational requirements while maintaining platform compatibility.
Data Integration Framework
Custom Data Sources: Framework for integrating custom data sources including proprietary databases, legacy systems, and specialized data formats.
Data Processing Pipelines: Custom data processing pipelines that leverage ARKOS agents for analysis while integrating with existing data infrastructure.
Analytics Extensions: Framework for creating custom analytics and reporting capabilities that extend platform insights with business-specific metrics.
Last updated