// Phase 4: AI Agent Framework Examples
// Demonstrates comprehensive AI agent capabilities including:
// - Agent lifecycle management and spawning
// - Message passing and communication
// - AI processing (text, image, generation)
// - Agent coordination and orchestration
// - Multi-agent collaboration workflows
// Example 1: Basic AI Agent Service
@ai
@trust("hybrid")
@chain("ethereum")
@compile_target("blockchain")
@persistent
service BasicAIAgentService {
// Agent configuration
agent_config: any;
active_agents: list<string>;
message_history: list<any>;
fn initialize() -> Result<Unit, Error> {
// Configure basic agent
let config = {
"agent_id": "basic_agent_001",
"name": "Basic Assistant",
"role": "general_assistant",
"capabilities": ["text_processing", "data_analysis", "task_execution"],
"memory_size": 1000,
"max_concurrent_tasks": 5,
"trust_level": "hybrid",
"communication_protocols": ["async_message", "direct_call"],
"ai_models": ["gpt", "bert", "custom"]
};
self.agent_config = config;
self.active_agents = [];
self.message_history = [];
log::info("ai", {
"service": "BasicAIAgentService",
"agent_config": config,
"message": "AI Agent Service initialized"
});
return Ok(Unit);
}
fn spawn_basic_agent() -> Result<string, Error> {
// Spawn a new AI agent
let agent = ai::spawn_agent(self.agent_config);
if (agent.is_none()) {
return Err(Error::new("Agent Spawn Failed", "Failed to spawn AI agent"));
}
let agent_id = agent["id"];
self.active_agents.push(agent_id);
log::info("ai", {
"action": "agent_spawned",
"agent_id": agent_id,
"agent_name": agent["config"]["name"],
"capabilities": agent["config"]["capabilities"]
});
return Ok(agent_id);
}
fn send_task_to_agent(agent_id: string, task_description: string, task_data: any) -> Result<any, Error> {
// Create task message
let message = ai::send_message(
"service_coordinator",
agent_id,
"task_assignment",
{
"task_type": "general_task",
"description": task_description,
"data": task_data,
"priority": "normal",
"deadline": null
},
"normal"
);
// Store in message history
self.message_history.push({
"timestamp": chain::get_block_timestamp(1),
"from": "service_coordinator",
"to": agent_id,
"type": "task_assignment",
"content": message
});
// Simulate agent processing
let response = ai::process_message(agent_id, message);
log::info("ai", {
"action": "task_sent",
"agent_id": agent_id,
"task_description": task_description,
"message_id": message["id"]
});
return Ok(response);
}
fn get_agent_status_report() -> any {
let report = {
"total_agents": self.active_agents.length,
"active_agents": self.active_agents,
"total_messages": self.message_history.length,
"recent_messages": self.message_history.slice(-5), // Last 5 messages
"generated_at": chain::get_block_timestamp(1)
};
return report;
}
}
// Example 2: Advanced Multi-Agent Coordination Service
// @ai
// @trust("hybrid")
// @persistent
service AdvancedMultiAgentService {
coordinator: string;
specialized_agents: any;
active_workflows: list<any>;
fn initialize() -> Result<Unit, Error> {
// Create agent coordinator
let coordinator_id = ai::create_coordinator("advanced_coordinator");
// Create specialized agents
let text_agent_config = {
"agent_id": "text_processor",
"name": "Text Processing Agent",
"role": "nlp_specialist",
"capabilities": ["text_analysis", "sentiment_analysis", "summarization"],
"memory_size": 2000,
"ai_models": ["bert", "gpt", "t5"]
};
let data_agent_config = {
"agent_id": "data_processor",
"name": "Data Processing Agent",
"role": "data_specialist",
"capabilities": ["data_analysis", "statistics", "visualization"],
"memory_size": 1500,
"ai_models": ["pandas_ai", "sklearn"]
};
let image_agent_config = {
"agent_id": "image_processor",
"name": "Image Processing Agent",
"role": "vision_specialist",
"capabilities": ["image_analysis", "object_detection", "face_recognition"],
"memory_size": 3000,
"ai_models": ["resnet", "yolo", "facenet"]
};
// Spawn specialized agents
let text_agent = ai::spawn_agent(text_agent_config);
let data_agent = ai::spawn_agent(data_agent_config);
let image_agent = ai::spawn_agent(image_agent_config);
// Add agents to coordinator
ai::add_agent_to_coordinator(coordinator_id, text_agent);
ai::add_agent_to_coordinator(coordinator_id, data_agent);
ai::add_agent_to_coordinator(coordinator_id, image_agent);
self.coordinator = coordinator_id;
self.specialized_agents = {
"text": text_agent,
"data": data_agent,
"image": image_agent
};
self.active_workflows = [];
log::info("ai", {
"service": "AdvancedMultiAgentService",
"coordinator": coordinator_id,
"agents_count": 3,
"message": "Multi-agent coordination system initialized"
});
return Ok(Unit);
}
fn create_content_analysis_workflow(content_type: string, content: any) -> Result<string, Error> {
// Determine which agents to use based on content type
let workflow_steps = [];
if (content_type == "text_document") {
workflow_steps = [
{
"step_id": "text_analysis",
"agent_id": self.specialized_agents["text"]["id"],
"task_type": "analyze_text",
"dependencies": []
},
{
"step_id": "sentiment_analysis",
"agent_id": self.specialized_agents["text"]["id"],
"task_type": "sentiment_analysis",
"dependencies": ["text_analysis"]
},
{
"step_id": "generate_summary",
"agent_id": self.specialized_agents["text"]["id"],
"task_type": "summarization",
"dependencies": ["sentiment_analysis"]
}
];
} else if (content_type == "dataset" ) {
workflow_steps = [
{
"step_id": "data_validation",
"agent_id": self.specialized_agents["data"]["id"],
"task_type": "validate_data",
"dependencies": []
},
{
"step_id": "statistical_analysis",
"agent_id": self.specialized_agents["data"]["id"],
"task_type": "statistics",
"dependencies": ["data_validation"]
},
{
"step_id": "generate_insights",
"agent_id": self.specialized_agents["data"]["id"],
"task_type": "insight_generation",
"dependencies": ["statistical_analysis"]
}
];
} else if (content_type == "image" ) {
workflow_steps = [
{
"step_id": "image_analysis",
"agent_id": self.specialized_agents["image"]["id"],
"task_type": "analyze_image",
"dependencies": []
},
{
"step_id": "object_detection",
"agent_id": self.specialized_agents["image"]["id"],
"task_type": "detect_objects",
"dependencies": ["image_analysis"]
},
{
"step_id": "generate_description",
"agent_id": self.specialized_agents["image"]["id"],
"task_type": "image_description",
"dependencies": ["object_detection"]
}
];
}
// Create workflow
let workflow_name = content_type + "_analysis_workflow";
let workflow = ai::create_workflow(self.coordinator, workflow_name, workflow_steps);
self.active_workflows.push(workflow);
log::info("ai", {
"action": "workflow_created",
"workflow_id": workflow["workflow_id"],
"content_type": content_type,
"steps_count": workflow_steps.length
});
return Ok(workflow["workflow_id"]);
}
fn execute_content_analysis(workflow_id: string, content: any) -> Result<any, Error> {
// Execute the workflow
let result = ai::execute_workflow(self.coordinator, workflow_id);
if (!result) {
return Err(Error::new("Workflow Execution Failed", "Failed to execute workflow " + workflow_id));
}
// Collect results from all agents
// let workflow = self.active_workflows.find(
if (workflow.is_none()) {
return Err(Error::new("Workflow Not Found", "Workflow " + workflow_id + " not found"));
}
let results = {
"workflow_id": workflow_id,
"content_type": content["type"],
"analysis_results": {},
"execution_time": chain::get_block_timestamp(1) - workflow["created_at"],
"status": "completed"
};
// Get results from each step
for step in workflow["steps"] {
let agent_result = ai::get_agent_status(step["agent_id"]);
results["analysis_results"][step["step_id"]] = agent_result;
}
log::info("ai", {
"action": "workflow_executed",
"workflow_id": workflow_id,
"results_count": results["analysis_results"].length,
"execution_time": results["execution_time"]
});
return Ok(results);
}
fn get_coordination_metrics() -> any {
let coordinator_metrics = ai::get_coordinator_metrics(self.coordinator);
let metrics = {
"coordinator_id": self.coordinator,
"total_agents": self.specialized_agents.length,
"active_workflows": self.active_workflows.length,
"coordinator_metrics": coordinator_metrics,
"agent_metrics": {},
"generated_at": chain::get_block_timestamp(1)
};
// Get metrics for each specialized agent
for agent_type in ["text", "data", "image"] {
let agent = self.specialized_agents[agent_type];
let agent_metrics = ai::get_agent_metrics(agent["id"]);
metrics["agent_metrics"][agent_type] = agent_metrics;
}
return metrics;
}
}
// Example 3: Intelligent Customer Service Agent
// @ai
// @trust("hybrid")
// @persistent
service IntelligentCustomerService {
customer_agent: string;
knowledge_base_agent: string;
sentiment_agent: string;
escalation_agent: string;
active_conversations: any;
fn initialize() -> Result<Unit, Error> {
// Create specialized customer service agents
let customer_config = {
"agent_id": "customer_service_agent",
"name": "Customer Service Agent",
"role": "customer_support",
"capabilities": ["conversation_management", "query_resolution", "escalation_handling"],
"memory_size": 5000,
"max_concurrent_tasks": 10,
"ai_models": ["gpt", "bert", "sentiment_model"]
};
let knowledge_config = {
"agent_id": "knowledge_base_agent",
"name": "Knowledge Base Agent",
"role": "information_retrieval",
"capabilities": ["knowledge_search", "faq_lookup", "documentation_search"],
"memory_size": 10000,
"ai_models": ["semantic_search", "bert"]
};
let sentiment_config = {
"agent_id": "sentiment_agent",
"name": "Sentiment Analysis Agent",
"role": "emotion_detection",
"capabilities": ["sentiment_analysis", "emotion_detection", "urgency_detection"],
"memory_size": 2000,
"ai_models": ["sentiment_analyzer", "emotion_detector"]
};
let escalation_config = {
"agent_id": "escalation_agent",
"name": "Escalation Agent",
"role": "crisis_management",
"capabilities": ["priority_assessment", "escalation_routing", "supervisor_notification"],
"memory_size": 1500,
"ai_models": ["priority_classifier"]
};
// Spawn all agents
self.customer_agent = ai::spawn_agent(customer_config)["id"];
self.knowledge_base_agent = ai::spawn_agent(knowledge_config)["id"];
self.sentiment_agent = ai::spawn_agent(sentiment_config)["id"];
self.escalation_agent = ai::spawn_agent(escalation_config)["id"];
self.active_conversations = {};
log::info("ai", {
"service": "IntelligentCustomerService",
"agents_spawned": 4,
"message": "Intelligent customer service system initialized"
});
return Ok(Unit);
}
fn handle_customer_inquiry(customer_id: string, inquiry_text: string, context: any) -> Result<any, Error> {
// Generate conversation ID
let conversation_id = "conv_" + customer_id + "_" + chain::get_block_timestamp(1).to_string();
// Initialize conversation
self.active_conversations[conversation_id] = {
"customer_id": customer_id,
"inquiry": inquiry_text,
"context": context,
"started_at": chain::get_block_timestamp(1),
"messages": [],
"status": "active",
"sentiment_history": [],
"escalation_level": "none"
};
// Analyze sentiment of the inquiry
let sentiment_analysis = ai::analyze_text(inquiry_text);
// Store sentiment
self.active_conversations[conversation_id]["sentiment_history"].push({
"timestamp": chain::get_block_timestamp(1),
"text": inquiry_text,
"sentiment": sentiment_analysis["sentiment"],
"confidence": sentiment_analysis["confidence"]
});
// Determine if (escalation is needed
let needs_escalation = self.assess_escalation_need(sentiment_analysis, context);
if (needs_escalation) {
let escalation_result = self.escalate_inquiry(conversation_id, inquiry_text, sentiment_analysis);
return Ok(escalation_result);
}
// Search knowledge base for relevant information
let knowledge_results = ai::send_message(
self.customer_agent,
self.knowledge_base_agent,
"knowledge_search",
{
"query": inquiry_text,
"context": context,
"conversation_id": conversation_id
},
"high"
);
// Generate response using customer service agent
let prompt = "Customer inquiry: " + inquiry_text +
"\n\nKnowledge base results: " + knowledge_results +
"\n\nContext: " + context +
"\n\nGenerate a helpful, professional response:";
let response = ai::generate_text(prompt);
// Store response in conversation
self.active_conversations[conversation_id]["messages"].push({
"timestamp": chain::get_block_timestamp(1),
"sender": "agent",
"message": response,
"message_type": "response"
});
let result = {
"conversation_id": conversation_id,
"response": response,
"sentiment": sentiment_analysis["sentiment"],
"escalated": false,
"knowledge_used": true,
"confidence": sentiment_analysis["confidence"]
};
log::info("ai", {
"action": "inquiry_handled",
"conversation_id": conversation_id,
"customer_id": customer_id,
"escalated": false,
"response_length": response.length
});
return Ok(result);
}
fn assess_escalation_need(sentiment_analysis: any, context: any) -> bool {
// Assess if (inquiry needs escalation based on sentiment and context
let sentiment_score = sentiment_analysis["sentiment"];
let urgency_keywords = ["urgent", "emergency", "crisis", "immediately", "asap"];
// Check for urgency keywords in description
let has_urgency_keywords = false;
for keyword in urgency_keywords {
if (context["description"].to_lower().contains(keyword)) {
has_urgency_keywords = true;
break;
}
}
let is_very_negative = sentiment_score < -0.7;
// let is_high_priority = context["priority"] == "high"
// return has_urgency_keywords || is_very_negative
}
fn escalate_inquiry(conversation_id: string, inquiry_text: string, sentiment_analysis: any) -> any {
// Send to escalation agent
let escalation_message = ai::send_message(
self.customer_agent,
self.escalation_agent,
"escalation_request",
{
"conversation_id": conversation_id,
"inquiry": inquiry_text,
"sentiment": sentiment_analysis,
"escalation_reason": "high_priority_or_negative_sentiment"
},
"critical"
);
// Update conversation status
self.active_conversations[conversation_id]["status"] = "escalated";
self.active_conversations[conversation_id]["escalation_level"] = "supervisor";
// Generate immediate acknowledgment response
let response = "Thank you for contacting us. I understand this is an urgent matter. I'm escalating this to our senior support team who will respond within the next 15 minutes.";
// Store escalation response
self.active_conversations[conversation_id]["messages"].push({
"timestamp": chain::get_block_timestamp(1),
"sender": "escalation_agent",
"message": response,
"message_type": "escalation_acknowledgment"
});
let result = {
"conversation_id": conversation_id,
"response": response,
"sentiment": sentiment_analysis["sentiment"],
"escalated": true,
"escalation_level": "supervisor",
"estimated_response_time": 900, // 15 minutes
"escalation_message_id": escalation_message["id"]
};
log::info("ai", {
"action": "inquiry_escalated",
"conversation_id": conversation_id,
"escalation_reason": "high_priority_or_negative_sentiment",
"escalation_level": "supervisor"
});
return result;
}
fn continue_conversation(conversation_id: string, customer_message: string) -> Result<any, Error> {
// Check if (conversation exists
if (!self.active_conversations.contains_key(conversation_id)) {
return Err(Error::new("Conversation Not Found", "Conversation " + conversation_id + " not found"));
}
let conversation = self.active_conversations[conversation_id];
// Analyze sentiment of new message
let sentiment_analysis = ai::analyze_text(customer_message);
conversation["sentiment_history"].push({
"timestamp": chain::get_block_timestamp(1),
"text": customer_message,
"sentiment": sentiment_analysis["sentiment"],
"confidence": sentiment_analysis["confidence"]
});
// Store customer message
conversation["messages"].push({
"timestamp": chain::get_block_timestamp(1),
"sender": "customer",
"message": customer_message,
"message_type": "customer_message"
});
// Generate contextual response
let conversation_history = conversation["messages"].slice(-5); // Last 5 messages
let context_prompt = "Conversation history: " + conversation_history.to_string() +
"\n\nLatest customer message: " + customer_message +
"\n\nCurrent sentiment: " + sentiment_analysis["sentiment"] +
"\n\nGenerate an appropriate response:";
let response = ai::generate_text(context_prompt);
// Store agent response
conversation["messages"].push({
"timestamp": chain::get_block_timestamp(1),
"sender": "agent",
"message": response,
"message_type": "response"
});
let result = {
"conversation_id": conversation_id,
"response": response,
"sentiment": sentiment_analysis["sentiment"],
"conversation_status": conversation["status"],
"message_count": conversation["messages"].length
};
return Ok(result);
}
fn get_customer_service_metrics() -> any {
let total_conversations = self.active_conversations.length;
// let escalated_conversations = self.active_conversations.values().filter(
let avg_sentiment = self.calculate_average_sentiment();
let escalation_rate = 0;
if (total_conversations > 0) {
escalation_rate = escalated_conversations / total_conversations;
}
let metrics = {
"total_conversations": total_conversations,
"active_conversations": total_conversations,
"escalated_conversations": escalated_conversations,
"escalation_rate": escalation_rate,
"average_sentiment": avg_sentiment,
"agent_metrics": {
"customer_agent": ai::get_agent_metrics(self.customer_agent),
"knowledge_agent": ai::get_agent_metrics(self.knowledge_base_agent),
"sentiment_agent": ai::get_agent_metrics(self.sentiment_agent),
"escalation_agent": ai::get_agent_metrics(self.escalation_agent)
},
"generated_at": chain::get_block_timestamp(1)
};
return metrics;
}
fn calculate_average_sentiment() -> float {
let all_sentiments = [];
for conversation in self.active_conversations.values() {
for sentiment_entry in conversation["sentiment_history"] {
all_sentiments.push(sentiment_entry["sentiment"]);
}
}
if (all_sentiments.is_empty()) {
return 0.0;
}
// let sum = all_sentiments.reduce(
return sum / all_sentiments.length;
}
}
// Example 4: AI-Powered Content Creation and Analysis
// @ai
// @trust("hybrid")
// @persistent
service ContentCreationService {
content_agent: string;
analysis_agent: string;
optimization_agent: string;
publishing_agent: string;
content_library: any;
fn initialize() -> Result<Unit, Error> {
// Create content creation agents
let content_config = {
"agent_id": "content_creator",
"name": "Content Creation Agent",
"role": "content_generator",
"capabilities": ["article_writing", "social_media_posts", "email_campaigns", "blog_posts"],
"memory_size": 8000,
"ai_models": ["gpt", "content_generator", "style_analyzer"]
};
let analysis_config = {
"agent_id": "content_analyzer",
"name": "Content Analysis Agent",
"role": "content_evaluator",
"capabilities": ["readability_analysis", "seo_analysis", "engagement_prediction", "tone_analysis"],
"memory_size": 4000,
"ai_models": ["readability_analyzer", "seo_analyzer", "engagement_predictor"]
};
let optimization_config = {
"agent_id": "content_optimizer",
"name": "Content Optimization Agent",
"role": "content_improver",
"capabilities": ["keyword_optimization", "readability_improvement", "engagement_boost", "a_b_testing"],
"memory_size": 6000,
"ai_models": ["optimizer", "a_b_tester", "performance_predictor"]
};
let publishing_config = {
"agent_id": "content_publisher",
"name": "Content Publishing Agent",
"role": "content_distributor",
"capabilities": ["platform_optimization", "timing_optimization", "audience_targeting", "performance_tracking"],
"memory_size": 3000,
"ai_models": ["platform_analyzer", "timing_optimizer"]
};
// Spawn agents
self.content_agent = ai::spawn_agent(content_config)["id"];
self.analysis_agent = ai::spawn_agent(analysis_config)["id"];
self.optimization_agent = ai::spawn_agent(optimization_config)["id"];
self.publishing_agent = ai::spawn_agent(publishing_config)["id"];
self.content_library = {};
log::info("ai", {
"service": "ContentCreationService",
"agents_spawned": 4,
"message": "AI-powered content creation system initialized"
});
return Ok(Unit);
}
fn create_content_campaign(topic: string, content_type: string, target_audience: any, campaign_goals: list<string>) -> Result<any, Error> {
// Generate content brief
let content_brief = {
"topic": topic,
"content_type": content_type,
"target_audience": target_audience,
"campaign_goals": campaign_goals,
"created_at": chain::get_block_timestamp(1),
"status": "draft"
};
// Generate initial content
let goals_str = campaign_goals.join(", ");
let prompt = "Create a " + content_type + " about '" + topic + "' for " +
target_audience["description"] + " audience. Campaign goals: " +
goals_str + "\n\nGenerate engaging content:";
let initial_content = ai::generate_text(prompt);
// Analyze content quality
let analysis = ai::analyze_text(initial_content);
let seo_analysis = self.perform_seo_analysis(initial_content, topic);
// Optimize content
let optimized_content = ai::send_message(
self.analysis_agent,
self.optimization_agent,
"content_optimization",
{
"original_content": initial_content,
"analysis": analysis,
"seo_analysis": seo_analysis,
"target_audience": target_audience,
"campaign_goals": campaign_goals
},
"high"
);
// Generate content variations for A/B testing
let variations = [];
for i in 0..3 {
let variation_prompt = "Create variation " + (i + 1).to_string() + " of this content: " +
optimized_content + "\n\nMake it unique but maintain the core message:";
let variation = ai::generate_text(variation_prompt);
variations.push(variation);
}
// Store in content library
let content_id = "content_" + generate_id();
self.content_library[content_id] = {
"brief": content_brief,
"original_content": initial_content,
"optimized_content": optimized_content,
"variations": variations,
"analysis": analysis,
"seo_analysis": seo_analysis,
"created_at": chain::get_block_timestamp(1),
"status": "ready_for_publishing"
};
let result = {
"content_id": content_id,
"content_type": content_type,
"topic": topic,
"optimized_content": optimized_content,
"variations_count": variations.length,
"seo_score": seo_analysis["score"],
"readability_score": analysis["readability"],
"estimated_engagement": self.predict_engagement(optimized_content, target_audience)
};
log::info("ai", {
"action": "content_created",
"content_id": content_id,
"content_type": content_type,
"topic": topic,
"variations": variations.length
});
return Ok(result);
}
fn perform_seo_analysis(content: string, topic: string) -> any {
// Simulate SEO analysis
let keywords = [topic, "ai", "technology", "innovation"];
// Note: keyword density calculation would go here if map() was supported
// For now, returning a simplified analysis
return {
"score": 85,
"keywords": keyword_density,
"title_optimized": content.length < 60,
"meta_description": "Good meta description generated",
"headings": ["H1", "H2", "H3"],
"internal_links": 3,
"external_links": 2,
"images": 1
};
}
fn predict_engagement(content: string, audience: any) -> any {
// Simulate engagement prediction
let content_length = content.length;
let has_questions = content.contains("?") || content.contains("how") || content.contains("what");
let has_emojis = content.contains("😊") || content.contains("🚀");
let has_numbers = content.matches("\\d+").length > 0;
let engagement_score = 0.3; // baseline
if (has_questions) {
engagement_score = engagement_score + 0.2;
}
if (has_emojis) {
engagement_score = engagement_score + 0.1;
}
if (has_numbers) {
engagement_score = engagement_score + 0.1;
}
if (content_length > 500 && content_length < 2000) {
engagement_score = engagement_score + 0.2;
}
return {
"predicted_score": engagement_score,
"estimated_likes": (audience["size"] * engagement_score * 0.05).round(),
"estimated_shares": (audience["size"] * engagement_score * 0.02).round(),
"estimated_comments": (audience["size"] * engagement_score * 0.03).round(),
"best_posting_time": "14:00",
"predicted_reach": audience["size"] * engagement_score * 1.5
};
}
fn publish_content(content_id: string, platforms: list<string>, schedule: any) -> Result<any, Error> {
// Check if (content exists
if (!self.content_library.contains_key(content_id)) {
return Err(Error::new("Content Not Found", "Content " + content_id + " not found"));
}
let content = self.content_library[content_id];
// Optimize content for each platform
let platform_optimizations = {};
for platform in platforms {
let optimization = ai::send_message(
self.content_agent,
self.publishing_agent,
"platform_optimization",
{
"content": content["optimized_content"],
"platform": platform,
"audience": content["brief"]["target_audience"],
"schedule": schedule
},
"normal"
);
platform_optimizations[platform] = optimization;
}
// Schedule publishing
let publishing_schedule = {
"content_id": content_id,
"platforms": platforms,
"optimizations": platform_optimizations,
"schedule": schedule,
"scheduled_at": chain::get_block_timestamp(1) + schedule["delay_seconds"],
"status": "scheduled"
};
// Store publishing info
content["publishing"] = publishing_schedule;
let result = {
"content_id": content_id,
"platforms": platforms,
"scheduled_at": publishing_schedule["scheduled_at"],
"optimizations_count": platform_optimizations.length,
"estimated_reach": platforms.length * 10000 // Rough estimate
};
log::info("ai", {
"action": "content_scheduled",
"content_id": content_id,
"platforms": platforms.length,
"scheduled_at": publishing_schedule["scheduled_at"]
});
return Ok(result);
}
fn analyze_content_performance(content_id: string, performance_data: any) -> Result<any, Error> {
// Send performance data to analysis agent
let analysis = ai::send_message(
self.publishing_agent,
self.analysis_agent,
"performance_analysis",
{
"content_id": content_id,
"performance_data": performance_data,
"content": self.content_library[content_id],
"time_since_publish": chain::get_block_timestamp(1) - self.content_library[content_id]["publishing"]["scheduled_at"]
},
"normal"
);
// Generate insights and recommendations
let insights_prompt = "Analyze this content performance and provide recommendations: " +
analysis.to_string() + "\n\nPerformance data: " + performance_data.to_string();
let insights = ai::generate_text(insights_prompt);
// Store analysis results
if (!self.content_library[content_id].contains_key("performance")) {
self.content_library[content_id]["performance"] = [];
}
self.content_library[content_id]["performance"].push({
"timestamp": chain::get_block_timestamp(1),
"data": performance_data,
"analysis": analysis,
"insights": insights
});
let result = {
"content_id": content_id,
"analysis": analysis,
"insights": insights,
"recommendations": self.generate_recommendations(analysis),
"performance_score": self.calculate_performance_score(performance_data)
};
return Ok(result);
}
fn generate_recommendations(analysis: string) -> list<any> {
// Extract recommendations from analysis
return [
"Increase posting frequency for similar content",
"Use more engaging headlines",
"Add more visual elements",
"Optimize posting times based on audience engagement",
"Create follow-up content based on this topic"
];
}
fn calculate_performance_score(performance_data: any) -> float {
// Calculate overall performance score (0-100)
let engagement_rate = performance_data["likes"] / performance_data["impressions"];
let click_rate = performance_data["clicks"] / performance_data["impressions"];
let share_rate = performance_data["shares"] / performance_data["impressions"];
let score = (engagement_rate * 40) + (click_rate * 35) + (share_rate * 25);
return Math.min(100, Math.max(0, score * 100));
}
fn get_content_creation_metrics() -> any {
let total_content = self.content_library.length;
// let published_content = self.content_library.values().filter(
// let avg_seo_score = self.content_library.values().map(|c| c["seo_analysis"]["score"]).reduce(
// let avg_engagement = self.content_library.values().filter(|c| c.contains_key("performance")).map(|c| c["performance"].last()["score"]).reduce(
let metrics = {
"total_content": total_content,
"published_content": published_content,
"draft_content": total_content - published_content,
"average_seo_score": avg_seo_score,
"average_engagement_score": avg_engagement,
"content_types": self.get_content_type_breakdown(),
"top_performing_topics": self.get_top_topics(),
"agent_metrics": {
"content_agent": ai::get_agent_metrics(self.content_agent),
"analysis_agent": ai::get_agent_metrics(self.analysis_agent),
"optimization_agent": ai::get_agent_metrics(self.optimization_agent),
"publishing_agent": ai::get_agent_metrics(self.publishing_agent)
},
"generated_at": chain::get_block_timestamp(1)
};
return metrics;
}
fn get_content_type_breakdown() -> any {
let breakdown = {};
for content in self.content_library.values() {
let content_type = content["brief"]["content_type"];
if (!breakdown.contains_key(content_type)) {
breakdown[content_type] = 0;
}
breakdown[content_type] = breakdown[content_type] + 1;
}
return breakdown;
}
fn get_top_topics() -> list<any> {
// This would analyze performance data to find top topics
return [
{ "topic": "AI Technology", "performance_score": 92, "content_count": 5 },
{ "topic": "Digital Marketing", "performance_score": 88, "content_count": 3 },
{ "topic": "Blockchain", "performance_score": 85, "content_count": 4 }
];
}
}
// Main demonstration
fn main() {
log::info("main", { "message": "Starting Phase 4: AI Agent Framework Examples" });
// Initialize all services
let basic_agent_service = BasicAIAgentService::new();
basic_agent_service.initialize();
let multi_agent_service = AdvancedMultiAgentService::new();
multi_agent_service.initialize();
let customer_service = IntelligentCustomerService::new();
customer_service.initialize();
let content_service = ContentCreationService::new();
content_service.initialize();
// Demonstrate basic agent functionality
let agent_id = basic_agent_service.spawn_basic_agent();
let task_result = basic_agent_service.send_task_to_agent(agent_id, "Analyze this text", { "text": "Hello, world!" });
let status_report = basic_agent_service.get_agent_status_report();
log::info("demo", { "action": "basic_agent_demo", "agent_id": agent_id, "status_report": status_report });
// Demonstrate multi-agent coordination
let workflow_id = multi_agent_service.create_content_analysis_workflow("text_document", {
"type": "text_document",
"content": "This is a sample document for analysis...",
"metadata": { "author": "Demo", "length": 100 }
});
let analysis_result = multi_agent_service.execute_content_analysis(workflow_id, {
"type": "text_document",
"content": "This is a sample document for analysis..."
});
let coordination_metrics = multi_agent_service.get_coordination_metrics();
log::info("demo", {
"action": "multi_agent_demo",
"workflow_id": workflow_id,
"analysis_result": analysis_result,
"coordination_metrics": coordination_metrics
});
// Demonstrate intelligent customer service
let inquiry_result = customer_service.handle_customer_inquiry(
"customer_123",
"I'm having trouble with my account login. Can you help me reset my password?",
{
"subject": "Password Reset Issue",
"description": "Unable to login to account",
"priority": "normal",
"channel": "web_chat"
}
);
let conversation_result = customer_service.continue_conversation(
inquiry_result["conversation_id"],
"I tried the reset link but it's not working."
);
let service_metrics = customer_service.get_customer_service_metrics();
log::info("demo", {
"action": "customer_service_demo",
"inquiry_result": inquiry_result,
"conversation_result": conversation_result,
"service_metrics": service_metrics
});
// Demonstrate content creation
let content_result = content_service.create_content_campaign(
"AI Agent Technology",
"blog_post",
{
"description": "Tech-savvy professionals",
"size": 50000,
"interests": ["AI", "technology", "innovation"]
},
["increase_brand_awareness", "drive_traffic", "generate_leads"]
);
let publishing_result = content_service.publish_content(
content_result["content_id"],
["linkedin", "twitter", "company_blog"],
{ "delay_seconds": 3600, "best_time": "14:00" }
);
let content_metrics = content_service.get_content_creation_metrics();
log::info("demo", {
"action": "content_creation_demo",
"content_result": content_result,
"publishing_result": publishing_result,
"content_metrics": content_metrics
});
log::info("main", { "message": "Phase 4 AI Agent Framework examples completed successfully!" });
}