agents/self_improvement_agent.py

import asyncio
from typing import Dict, List, Any
from core.agent import BaseAgent
from core.models import AgentConfig, Task, AgentMessage, SEOData
import logging
import random
from datetime import datetime

logger = logging.getLogger(__name__)


class SelfImprovementAgent(BaseAgent):
    """Self-Improvement Agent responsible for continuous learning and optimization"""
    
    def __init__(self, config: AgentConfig):
        super().__init__(config)
        self.performance_analytics = {}
        self.prompt_refinements = []
        self.workflow_optimizations = []
        self.feature_priorities = []
        
    async def execute(self):
        """Execute self-improvement functions"""
        logger.info(f"{self.name} executing self-improvement and learning...")
        
        # Analyze system performance
        await self.analyze_performance()
        
        # Refine prompts and processes
        await self.refine_prompts()
        
        # Optimize workflows
        await self.optimize_workflows()
        
        # Prioritize new features
        await self.prioritize_features()
    
    async def analyze_performance(self):
        """Analyze system performance and identify improvement areas"""
        logger.info(f"{self.name} analyzing system performance...")
        
        # Simulate performance analysis
        performance_data = {
            "system_efficiency": "85%",
            "task_completion_rate": "92%",
            "resource_optimization": "improved_15%",
            "bottleneck_identification": ["content_generation", "link_building_response_time"],
            "suggested_improvements": ["parallel_processing", "better_load_balancing"]
        }
        
        self.performance_analytics.update(performance_data)
        
        # Log performance analysis
        logger.info(f"Performance analysis complete: Efficiency {performance_data['system_efficiency']}")
    
    async def refine_prompts(self):
        """Refine prompts and processes based on outcomes"""
        logger.info(f"{self.name} refining prompts and processes...")
        
        # Simulate prompt refinement
        refinements = [
            {
                "component": "content_generation",
                "original_prompt": "Write an article about X",
                "refined_prompt": "Write a comprehensive, authoritative article about X with at least 2000 words, including examples, case studies, and actionable tips",
                "improvement_metric": "engagement_increased_23%"
            },
            {
                "component": "outreach_emails", 
                "original_prompt": "Write a guest post outreach email",
                "refined_prompt": "Write a personalized guest post outreach email for [site] focusing on mutual benefits and including specific article ideas relevant to their audience",
                "improvement_metric": "response_rate_increased_31%"
            },
            {
                "component": "keyword_analysis",
                "original_prompt": "Analyze keywords",
                "refined_prompt": "Analyze keywords for [niche] considering search intent, competition, and commercial value. Provide specific recommendations for content clusters",
                "improvement_metric": "accuracy_improved_18%"
            }
        ]
        
        self.prompt_refinements.extend(refinements)
        
        # Log refinements
        logger.info(f"Refined {len(refinements)} prompts/processes")
    
    async def optimize_workflows(self):
        """Optimize system workflows based on performance data"""
        logger.info(f"{self.name} optimizing workflows...")
        
        # Simulate workflow optimization
        optimizations = [
            {
                "workflow": "content_approval_process",
                "optimization": "implement_parallel_reviews_instead_of_sequential",
                "expected_impact": "reduce_time_by_40%"
            },
            {
                "workflow": "link_outreach_followup",
                "optimization": "automate_followup_sequence_after_7_days",
                "expected_impact": "increase_response_rate_by_15%"
            },
            {
                "workflow": "technical_audit_reporting",
                "optimization": "consolidate_multiple_reports_into_single_dashboard",
                "expected_impact": "reduce_manual_work_by_60%"
            }
        ]
        
        self.workflow_optimizations.extend(optimizations)
        
        # Log optimizations
        logger.info(f"Identified {len(optimizations)} workflow optimizations")
        
        # Send optimization suggestions to relevant agents
        for opt in optimizations:
            await self.send_message(
                recipient="automation_ops",
                content=f"Workflow optimization suggestion: {opt}",
                message_type="info"
            )
    
    async def prioritize_features(self):
        """Prioritize new features based on impact and feasibility"""
        logger.info(f"{self.name} prioritizing new features...")
        
        # Simulate feature prioritization
        feature_priorities = [
            {
                "feature": "multilingual_content_generation",
                "impact_score": 9.2,
                "feasibility_score": 7.5,
                "priority": "high",
                "justification": "large_market_opportunity"
            },
            {
                "feature": "advanced_competitor_tracking",
                "impact_score": 8.7,
                "feasibility_score": 8.0,
                "priority": "high", 
                "justification": "competitive_advantage"
            },
            {
                "feature": "voice_search_optimization",
                "impact_score": 7.3,
                "feasibility_score": 6.8,
                "priority": "medium",
                "justification": "emerging_trend"
            },
            {
                "feature": "video_content_generation",
                "impact_score": 8.1,
                "feasibility_score": 5.2,
                "priority": "medium",
                "justification": "high_demand_but_complex_implementation"
            }
        ]
        
        self.feature_priorities.extend(feature_priorities)
        
        # Send priorities to CEO agent
        await self.send_message(
            recipient="ceo_strategy",
            content=f"Feature priorities: {feature_priorities[:2]}",  # Send top 2
            message_type="info"
        )
    
    async def _execute_task_logic(self, task: Task) -> Dict[str, Any]:
        """Execute specific task logic for Self-Improvement agent"""
        if task.type == "analyze_performance":
            await self.analyze_performance()
            return {"status": "completed", "result": self.performance_analytics}
        elif task.type == "refine_prompts":
            await self.refine_prompts()
            return {"status": "completed", "result": self.prompt_refinements}
        elif task.type == "optimize_workflows":
            await self.optimize_workflows()
            return {"status": "completed", "result": self.workflow_optimizations}
        else:
            return {"status": "error", "message": f"Unknown task type: {task.type}"}