// Integrated Spawn & AI Agent Examples
// Demonstrates how language-level spawn/agent constructs integrate with the AI framework
// Example 1: Basic Spawn with AI Agent
fn basic_spawn_example() {
log::info("example", { "message": "=== Basic Spawn with AI Agent ===" });
// This spawn statement now creates an AI agent using the enhanced syntax
spawn my_assistant:ai {
role: "personal_assistant",
capabilities: ["task_management", "scheduling", "reminders"],
memory_size: 2000,
max_concurrent_tasks: 3
} {
// Agent initialization code
log::info("agent", { "message": "AI Assistant agent initialized" });
// Agent can use AI functions
let greeting = ai::generate_text("Generate a friendly greeting for a personal assistant");
log::info("agent", { "greeting": greeting });
// Agent can communicate with other agents
ai::send_message(
"my_assistant",
"system_coordinator",
"agent_ready",
{ "status": "initialized", "capabilities": ["task_management", "scheduling"] },
"normal"
);
}
log::info("example", { "message": "Basic spawn example completed" });
}
// Example 2: Agent Declaration with Full Configuration
fn agent_declaration_example() {
log::info("example", { "message": "=== Agent Declaration with Full Configuration ===" });
// This agent declaration creates a fully configured AI agent
agent data_analyzer:ai {
role: "data_analysis_specialist",
memory_size: 5000,
max_concurrent_tasks: 10,
trust_level: "hybrid",
communication_protocols: ["async", "sync", "event_driven"],
ai_models: ["gpt", "bert", "data_analysis_model"]
} with ["data_analysis", "statistics", "visualization", "reporting"] {
// Agent initialization
log::info("agent", { "message": "Data Analyzer agent starting up" });
// Agent can perform AI tasks
let analysis = ai::analyze_text("This is a sample document for analysis. It contains information about data processing and analysis techniques.");
log::info("agent", {
"analysis_summary": analysis.summary,
"sentiment": analysis.sentiment,
"keywords": analysis.keywords
});
// Agent can create and execute tasks
let task = ai::create_task(
"data_analyzer",
"analyze_dataset",
"Analyze the sales dataset for trends",
{
"dataset_path": "/data/sales.csv",
"analysis_type": "trend_analysis",
"output_format": "json"
}
);
let result = ai::execute_task("data_analyzer", task.id);
log::info("agent", { "task_result": result });
}
log::info("example", { "message": "Agent declaration example completed" });
}
// Example 3: Multi-Agent Coordination via Spawn
fn multi_agent_coordination_example() {
log::info("example", { "message": "=== Multi-Agent Coordination ===" });
// Create a coordinator agent
let coordinator = ai::create_coordinator("project_coordinator");
// Spawn multiple specialized agents
spawn frontend_developer:ai {
role: "frontend_specialist",
capabilities: ["ui_design", "react", "typescript", "responsive_design"],
memory_size: 3000
} {
log::info("agent", { "message": "Frontend Developer agent ready" });
}
spawn backend_developer:ai {
role: "backend_specialist",
capabilities: ["api_design", "database", "security", "scalability"],
memory_size: 4000
} {
log::info("agent", { "message": "Backend Developer agent ready" });
}
spawn qa_engineer:ai {
role: "quality_assurance",
capabilities: ["testing", "automation", "performance", "security_testing"],
memory_size: 2500
} {
log::info("agent", { "message": "QA Engineer agent ready" });
}
// Add agents to coordinator
ai::add_agent_to_coordinator(coordinator, frontend_developer);
ai::add_agent_to_coordinator(coordinator, backend_developer);
ai::add_agent_to_coordinator(coordinator, qa_engineer);
// Create a workflow for software development
let workflow_steps = [
{
"step_id": "requirements_analysis",
"agent_id": frontend_developer.id,
"task_type": "analyze_requirements",
"dependencies": []
},
{
"step_id": "design_ui",
"agent_id": frontend_developer.id,
"task_type": "design_interface",
"dependencies": ["requirements_analysis"]
},
{
"step_id": "implement_backend",
"agent_id": backend_developer.id,
"task_type": "implement_api",
"dependencies": ["requirements_analysis"]
},
{
"step_id": "integrate_frontend",
"agent_id": frontend_developer.id,
"task_type": "integrate_components",
"dependencies": ["design_ui", "implement_backend"]
},
{
"step_id": "testing",
"agent_id": qa_engineer.id,
"task_type": "run_tests",
"dependencies": ["integrate_frontend"]
}
];
let workflow = ai::create_workflow(coordinator, "software_development", workflow_steps);
let result = ai::execute_workflow(coordinator, workflow.workflow_id);
log::info("example", {
"message": "Multi-agent coordination completed",
"workflow_id": workflow.workflow_id,
"success": result
});
}
// Example 4: Real-time Agent Communication
fn real_time_communication_example() {
log::info("example", "message": "=== Real-time Agent Communication ===" });
// Spawn a chat agent
spawn chat_agent:ai {
role: "conversation_specialist",
capabilities: ["chat", "sentiment_analysis", "language_processing"],
memory_size: 3000
} {
log::info("agent", { "message": "Chat Agent initialized and ready for communication" });
// Set up message handling
while (true ) {
// In a real implementation, this would be event-driven
// For demo purposes, we'll simulate message handling
let simulated_message = ai::send_message(
"user",
"chat_agent",
"user_input",
{ "text": "Hello, can you help me with my project?" },
"normal"
);
// Process the message
let response = ai::process_message("chat_agent", simulated_message);
// Send response back
let reply = ai::send_message(
"chat_agent",
"user",
"agent_response",
{ "text": "I'd be happy to help you with your project! What specific aspect would you like assistance with?" },
"normal"
);
log::info("agent", {
"message": "Processed user message and sent response",
"response_id": reply.id
});
break; // Exit simulation loop
}
}
// Spawn a monitoring agent
spawn monitor_agent:ai {
role: "system_monitor",
capabilities: ["monitoring", "alerts", "performance_tracking"],
memory_size: 2000
} {
log::info("agent", { "message": "Monitor Agent initialized" });
// Monitor the chat agent
let metrics = ai::get_agent_metrics(chat_agent.id);
log::info("monitor", {
"monitored_agent": chat_agent.id,
"status": metrics["status"],
"message_count": metrics["message_count"],
"task_count": metrics["task_count"]
});
}
log::info("example", "message": "Real-time communication example completed" });
}
// Example 5: System Agent for Infrastructure Management
fn system_agent_example() {
log::info("example", "message": "=== System Agent for Infrastructure ===" });
// Declare a system agent for infrastructure management
agent infrastructure_manager:system {
role: "infrastructure_specialist",
capabilities: ["server_management", "networking", "security", "monitoring"]
} {
log::info("system", { "message": "Infrastructure Manager system agent started" });
// System agents can access system-level resources
// In a real implementation, this would manage servers, networks, etc.
// Example: Monitor system resources
let system_status = {
"cpu_usage": 45.2,
"memory_usage": 67.8,
"disk_usage": 23.1,
"network_traffic": 150.5, // Mbps
"active_connections": 1250,
"timestamp": chain::get_block_timestamp(1)
};
log::info("system", {
"cpu": system_status.cpu_usage,
"memory": system_status.memory_usage,
"connections": system_status.active_connections
});
// Example: Send alert if (resources are critical
if (system_status.cpu_usage > 90.0 {
ai::send_message(
"infrastructure_manager",
"alert_system",
"high_cpu_alert",
{
"cpu_usage": system_status.cpu_usage,
"threshold": 90.0,
"recommendation": "Consider scaling up resources"
},
"high"
);
}
}
log::info("example", "message": "System agent example completed" });
}
// Example 6: Worker Agent for Background Processing
fn worker_agent_example() {
log::info("example", "message": "=== Worker Agent for Background Processing ===" });
// Declare a worker agent for background tasks
agent background_worker:worker {
role: "background_processor",
capabilities: ["data_processing", "file_operations", "scheduled_tasks"]
} {
log::info("worker", { "message": "Background Worker started" });
// Worker agents can access parent scope but run independently
// In a real implementation, this would process queues, handle file operations, etc.
// Example: Process a file queue
let files_to_process = [
"/data/input/file1.csv",
"/data/input/file2.json",
"/data/input/file3.xml"
];
for file_path in files_to_process {
// Process each file
let file_content = database::read_file(file_path);
// Perform processing (simplified)
let processed_content = // format!("Processed: {}", file_content);
// Save processed result
let output_path = file_path.replace("/input/", "/output/");
database::write_file(output_path, processed_content);
log::info("worker", {
"processed_file": file_path,
"output_file": output_path,
"content_length": file_content.length
});
}
// Example: Clean up old files
let cleanup_result = self.perform_cleanup("/data/temp/");
log::info("worker", {
"cleanup_result": cleanup_result,
"message": "Background cleanup completed"
});
}
log::info("example", "message": "Worker agent example completed" });
}
// Example 7: Custom Agent Type
fn custom_agent_example() {
log::info("example", "message": "=== Custom Agent Type ===" });
// Declare a custom agent with specialized behavior
agent blockchain_oracle:custom("oracle") {
role: "blockchain_data_provider",
capabilities: ["price_feeds", "market_data", "oracle_services"]
} {
log::info("oracle", { "message": "Blockchain Oracle custom agent started" });
// Custom agents can define their own behavior patterns
// In this case, an oracle agent that provides blockchain data
// Example: Provide price feed
let price_data = {
"asset": "ETH",
"price_usd": 2456.78,
"change_24h": 2.34,
"volume_24h": 1250000000,
"timestamp": chain::get_block_timestamp(1)
};
// Send price update to subscribers
ai::send_message(
"blockchain_oracle",
"price_subscribers",
"price_update",
price_data,
"normal"
);
// Example: Verify external data
let external_data = {
"source": "coinmarketcap",
"data": "market_cap_data",
"signature": "verified_signature"
};
let verification_result = self.verify_external_data(external_data);
if (verification_result {
ai::send_message(
"blockchain_oracle",
"data_consumers",
"verified_data",
external_data,
"high"
);
}
log::info("oracle", {
"message": "Oracle data provided and verified",
"price_asset": price_data.asset,
"verification_status": verification_result
});
}
log::info("example", "message": "Custom agent example completed" });
}
// Example 8: Complex Multi-Agent Workflow
fn complex_workflow_example() {
log::info("example", "message": "=== Complex Multi-Agent Workflow ===" });
// Create multiple AI agents for a complex workflow
spawn content_creator:ai {
role: "content_specialist",
capabilities: ["writing", "research", "editing"],
memory_size: 4000
} {
log::info("agent", { "message": "Content Creator agent ready" });
}
spawn content_reviewer:ai {
role: "quality_assurance",
capabilities: ["editing", "fact_checking", "grammar_check"],
memory_size: 3000
} {
log::info("agent", { "message": "Content Reviewer agent ready" });
}
spawn content_publisher:ai {
role: "publication_specialist",
capabilities: ["seo_optimization", "social_media", "analytics"],
memory_size: 2500
} {
log::info("agent", { "message": "Content Publisher agent ready" });
}
// Create a content creation workflow
let coordinator = ai::create_coordinator("content_workflow_coordinator");
// Add agents to coordinator
ai::add_agent_to_coordinator(coordinator, content_creator);
ai::add_agent_to_coordinator(coordinator, content_reviewer);
ai::add_agent_to_coordinator(coordinator, content_publisher);
// Define workflow steps
let content_workflow_steps = [
{
"step_id": "research_topic",
"agent_id": content_creator.id,
"task_type": "research",
"dependencies": []
},
{
"step_id": "write_draft",
"agent_id": content_creator.id,
"task_type": "writing",
"dependencies": ["research_topic"]
},
{
"step_id": "review_content",
"agent_id": content_reviewer.id,
"task_type": "review",
"dependencies": ["write_draft"]
},
{
"step_id": "edit_content",
"agent_id": content_creator.id,
"task_type": "editing",
"dependencies": ["review_content"]
},
{
"step_id": "final_review",
"agent_id": content_reviewer.id,
"task_type": "final_check",
"dependencies": ["edit_content"]
},
{
"step_id": "optimize_seo",
"agent_id": content_publisher.id,
"task_type": "seo_optimization",
"dependencies": ["final_review"]
},
{
"step_id": "publish_content",
"agent_id": content_publisher.id,
"task_type": "publishing",
"dependencies": ["optimize_seo"]
},
{
"step_id": "analyze_performance",
"agent_id": content_publisher.id,
"task_type": "analytics",
"dependencies": ["publish_content"]
}
];
let workflow = ai::create_workflow(coordinator, "content_creation_pipeline", content_workflow_steps);
let success = ai::execute_workflow(coordinator, workflow.workflow_id);
if (success {
log::info("example", {
"message": "Complex content creation workflow completed successfully",
"workflow_id": workflow.workflow_id,
"steps_completed": content_workflow_steps.length
});
} else {
log::info("example", {
"message": "Content creation workflow encountered issues",
"workflow_id": workflow.workflow_id
});
}
}
// Main demonstration
fn main() {
log::info("main", "message": "Starting Integrated Spawn & AI Agent Examples" });
// Run all examples
basic_spawn_example();
agent_declaration_example();
multi_agent_coordination_example();
real_time_communication_example();
system_agent_example();
worker_agent_example();
custom_agent_example();
complex_workflow_example();
log::info("main", "message": "All integrated spawn & AI agent examples completed!" });
// Summary of integration benefits
log::info("summary", {
"message": "Spawn & AI Agent Integration Summary",
"benefits": [
"Unified syntax for agent creation and AI integration",
"Seamless transition from language constructs to AI framework",
"Type-safe agent configuration and capabilities",
"Automatic AI agent lifecycle management",
"Integrated message passing between language and AI systems",
"Coordinated multi-agent workflows with language-level control",
"Extensible agent types (AI, System, Worker, Custom)",
"Real-time agent communication and task execution"
]
});
}