from scrapegraphai.graphs import SmartScraperGraph
import asyncio
from datetime import datetime, timedelta
 
class FinancialIntelligenceCollector:
    def __init__(self):
        self.config = {
            "llm": {
                "model": "openai/gpt-4",
                "api_key": "your-openai-key"
            },
            "verbose": True,
            "headless": False
        }
    
    async def collect_earnings_data(self, ticker):
        """Collect comprehensive earnings and financial data"""
        
        earnings_prompt = f"""
        Extract comprehensive financial information for {ticker}:
        - Latest quarterly earnings results and guidance
        - Revenue growth trends and segment performance
        - Margin analysis and profitability metrics
        - Cash flow generation and balance sheet strength
        - Management commentary and forward outlook
        - Analyst reactions and revised estimates
        """
        
        sources = [
            f"https://finance.yahoo.com/quote/{ticker}",
            f"https://seekingalpha.com/symbol/{ticker}",
            f"https://www.sec.gov/edgar/search/#/q={ticker}",
        ]
        
        financial_data = {}
        for source in sources:
            scraper = SmartScraperGraph(
                prompt=earnings_prompt,
                source=source,
                config=self.config
            )
            data = scraper.run()
            financial_data[source] = data
            
        return financial_data
    
    async def collect_market_sentiment(self, ticker):
        """Monitor market sentiment and analyst opinions"""
        
        sentiment_prompt = f"""
        Extract market sentiment and analyst insights for {ticker}:
        - Recent analyst upgrades, downgrades, and price target changes
        - Institutional investor activity and holdings changes
        - Options flow and unusual trading activity
        - Social sentiment and retail investor discussion
        - News sentiment and media coverage analysis
        - Competitive mentions and relative performance
        """
        
        sentiment_sources = [
            f"https://finviz.com/quote.ashx?t={ticker}",
            f"https://www.marketwatch.com/investing/stock/{ticker}",
            f"https://stocktwits.com/symbol/{ticker}",
        ]
        
        sentiment_data = {}
        for source in sentiment_sources:
            scraper = SmartScraperGraph(
                prompt=sentiment_prompt,
                source=source,
                config=self.config
            )
            data = scraper.run()
            sentiment_data[source] = data
            
        return sentiment_data

import openai
from anthropic import Anthropic
 
class FinancialAnalysisAgent:
    def __init__(self, model_provider="openai"):
        self.model_provider = model_provider
        self.intelligence_collector = FinancialIntelligenceCollector()
        
        if model_provider == "openai":
            self.client = openai.OpenAI(api_key="your-openai-key")
        elif model_provider == "anthropic":
            self.client = Anthropic(api_key="your-anthropic-key")
    
    def analyze_investment_opportunity(self, ticker, analysis_type="comprehensive"):
        """Perform comprehensive investment analysis"""
        
        # Collect real-time data
        financial_data = asyncio.run(
            self.intelligence_collector.collect_earnings_data(ticker)
        )
        sentiment_data = asyncio.run(
            self.intelligence_collector.collect_market_sentiment(ticker)
        )
        
        # Create analysis prompt with real-time data
        analysis_prompt = f"""
        You are a senior equity research analyst with 15+ years of experience. 
        Analyze the investment opportunity for {ticker} based on the following 
        real-time data and provide a comprehensive investment thesis.
        
        FINANCIAL DATA:
        {financial_data}
        
        MARKET SENTIMENT DATA:
        {sentiment_data}
        
        Please provide:
        
        1. EXECUTIVE SUMMARY
        - Investment recommendation (Buy/Hold/Sell) with confidence level
        - Price target with 12-month horizon
        - Key investment thesis in 2-3 sentences
        
        2. FINANCIAL ANALYSIS
        - Revenue growth analysis and sustainability
        - Profitability trends and margin expansion potential
        - Balance sheet strength and financial flexibility
        - Cash flow generation and capital allocation
        
        3. COMPETITIVE POSITIONING
        - Market share trends and competitive advantages
        - Industry dynamics and secular growth drivers
        - Competitive threats and market positioning
        
        4. VALUATION ANALYSIS
        - Multiple-based valuation vs. peers
        - DCF-based intrinsic value estimate
        - Scenario analysis with bull/base/bear cases
        
        5. RISK ASSESSMENT
        - Key business and financial risks
        - Regulatory and industry-specific risks
        - Market and macroeconomic sensitivities
        
        6. CATALYST TIMELINE
        - Near-term catalysts (next 3-6 months)
        - Medium-term drivers (6-18 months)
        - Long-term value creation potential
        
        Format your response as a professional equity research report with 
        clear sections, bullet points for key insights, and specific numbers 
        and metrics throughout.
        """
        
        if self.model_provider == "openai":
            response = self.client.chat.completions.create(
                model="gpt-4",
                messages=[
                    {"role": "system", "content": "You are a senior equity research analyst."},
                    {"role": "user", "content": analysis_prompt}
                ],
                temperature=0.1,
                max_tokens=3000
            )
            return response.choices[0].message.content
            
        elif self.model_provider == "anthropic":
            response = self.client.messages.create(
                model="claude-3-opus-20240229",
                max_tokens=3000,
                temperature=0.1,
                messages=[
                    {"role": "user", "content": analysis_prompt}
                ]
            )
            return response.content[0].text

class FinancialMarketMonitor:
    """Monitor broader market conditions and sector dynamics"""
    
    def __init__(self):
        self.config = {
            "llm": {
                "model": "openai/gpt-4",
                "api_key": "your-openai-key"
            }
        }
    
    def monitor_sector_trends(self, sector):
        """Monitor sector-specific trends and developments"""
        
        sector_prompt = f"""
        Monitor and analyze current trends in the {sector} sector:
        - Regulatory developments and policy changes
        - Technology disruptions and innovation trends
        - M&A activity and consolidation patterns
        - Supply chain dynamics and cost pressures
        - Consumer behavior and demand trends
        - ESG considerations and sustainability initiatives
        """
        
        sector_sources = [
            f"https://www.sec.gov/news/industry/{sector.lower()}",
            f"https://www.reuters.com/business/{sector.lower()}",
            f"https://www.bloomberg.com/news/articles/*{sector}*",
        ]
        
        sector_intelligence = {}
        for source in sector_sources:
            try:
                scraper = SmartScraperGraph(
                    prompt=sector_prompt,
                    source=source,
                    config=self.config
                )
                data = scraper.run()
                sector_intelligence[source] = data
            except Exception as e:
                print(f"Error scraping {source}: {e}")
                
        return sector_intelligence
    
    def analyze_macro_environment(self):
        """Analyze macroeconomic conditions affecting markets"""
        
        macro_prompt = """
        Extract current macroeconomic indicators and analysis:
        - Federal Reserve policy and interest rate outlook
        - Inflation trends and economic growth indicators
        - Employment data and consumer spending patterns
        - Global economic conditions and geopolitical risks
        - Currency movements and commodity price trends
        - Market volatility and investor sentiment indicators
        """
        
        macro_sources = [
            "https://www.federalreserve.gov/newsevents.htm",
            "https://www.bls.gov/news.release/",
            "https://www.conference-board.org/data/",
        ]
        
        macro_data = {}
        for source in macro_sources:
            try:
                scraper = SmartScraperGraph(
                    prompt=macro_prompt,
                    source=source,
                    config=self.config
                )
                data = scraper.run()
                macro_data[source] = data
            except Exception as e:
                print(f"Error scraping {source}: {e}")
                
        return macro_data

class CompetitiveIntelligenceAgent:
    """Monitor competitors and analyze competitive dynamics"""
    
    def __init__(self, company_name, competitors_list):
        self.company_name = company_name
        self.competitors = competitors_list
        self.config = {
            "llm": {
                "model": "anthropic/claude-3-opus-20240229",
                "api_key": "your-anthropic-key"
            }
        }
    
    def monitor_competitive_landscape(self):
        """Comprehensive competitive landscape analysis"""
        
        competitive_intelligence = {}
        
        for competitor in self.competitors:
            competitor_prompt = f"""
            Analyze competitive intelligence for {competitor}:
            - Product launches and feature updates
            - Pricing strategy changes and promotional offers
            - Marketing campaigns and messaging shifts
            - Partnership announcements and strategic moves
            - Hiring patterns and team expansion
            - Financial performance and growth metrics
            - Customer wins and market share indicators
            - Technology investments and innovation focus
            """
            
            competitor_sources = [
                f"https://{competitor.lower().replace(' ', '')}.com",
                f"https://news.google.com/search?q={competitor}",
                f"https://www.crunchbase.com/organization/{competitor.lower().replace(' ', '-')}",
            ]
            
            for source in competitor_sources:
                try:
                    scraper = SmartScraperGraph(
                        prompt=competitor_prompt,
                        source=source,
                        config=self.config
                    )
                    data = scraper.run()
                    
                    if competitor not in competitive_intelligence:
                        competitive_intelligence[competitor] = {}
                    competitive_intelligence[competitor][source] = data
                    
                except Exception as e:
                    print(f"Error analyzing {competitor} from {source}: {e}")
        
        return self._analyze_competitive_implications(competitive_intelligence)
    
    def _analyze_competitive_implications(self, intelligence_data):
        """Analyze competitive implications using AI reasoning"""
        
        analysis_prompt = f"""
        Based on the competitive intelligence gathered, provide strategic analysis:
        
        COMPETITIVE DATA:
        {intelligence_data}
        
        Analyze:
        1. COMPETITIVE THREATS
        - Immediate threats to {self.company_name}
        - Emerging competitive challenges
        - Market share vulnerabilities
        
        2. STRATEGIC OPPORTUNITIES
        - Gaps in competitor offerings
        - Market positioning opportunities
        - Partnership and collaboration possibilities
        
        3. RECOMMENDED ACTIONS
        - Defensive strategies to protect market position
        - Offensive strategies to gain competitive advantage
        - Timeline and prioritization for initiatives
        
        4. MONITORING PRIORITIES
        - Key competitors to watch closely
        - Specific metrics and indicators to track
        - Early warning signals for competitive threats
        """
        
        # Use Claude for strategic analysis
        client = Anthropic(api_key="your-anthropic-key")
        response = client.messages.create(
            model="claude-3-opus-20240229",
            max_tokens=2500,
            temperature=0.2,
            messages=[
                {"role": "user", "content": analysis_prompt}
            ]
        )
        
        return response.content[0].text

class MarketResearchAgent:
    """Conduct comprehensive market research and trend analysis"""
    
    def __init__(self, industry, geographic_focus="global"):
        self.industry = industry
        self.geographic_focus = geographic_focus
        self.config = {
            "llm": {
                "model": "openai/gpt-4",
                "api_key": "your-openai-key"
            }
        }
    
    def conduct_market_analysis(self):
        """Comprehensive market analysis and trend identification"""
        
        market_prompt = f"""
        Conduct comprehensive market research for the {self.industry} industry:
        - Market size, growth rates, and projections
        - Key market segments and customer demographics
        - Technology trends and innovation drivers
        - Regulatory environment and policy impacts
        - Consumer behavior and preference shifts
        - Distribution channels and business model evolution
        - Investment trends and funding patterns
        - Emerging opportunities and market gaps
        """
        
        research_sources = [
            f"https://www.statista.com/markets/{self.industry.lower()}",
            f"https://www.grandviewresearch.com/industry/{self.industry.lower()}",
            f"https://www.mckinsey.com/industries/{self.industry.lower()}",
            f"https://www2.deloitte.com/insights/{self.industry.lower()}",
        ]
        
        market_data = {}
        for source in research_sources:
            try:
                scraper = SmartScraperGraph(
                    prompt=market_prompt,
                    source=source,
                    config=self.config
                )
                data = scraper.run()
                market_data[source] = data
            except Exception as e:
                print(f"Error researching {source}: {e}")
        
        return self._synthesize_market_insights(market_data)
    
    def _synthesize_market_insights(self, market_data):
        """Synthesize market insights using AI analysis"""
        
        synthesis_prompt = f"""
        Synthesize market research data into actionable insights:
        
        MARKET DATA:
        {market_data}
        
        Provide:
        1. MARKET OVERVIEW
        - Current market size and growth trajectory
        - Key drivers and constraints
        - Market maturity and lifecycle stage
        
        2. OPPORTUNITY ANALYSIS
        - High-growth segments and niches
        - Underserved customer needs
        - Technology-enabled opportunities
        
        3. COMPETITIVE LANDSCAPE
        - Market concentration and key players
        - Competitive dynamics and differentiation
        - Barriers to entry and competitive moats
        
        4. TREND ANALYSIS
        - Emerging trends and disruptions
        - Consumer behavior evolution
        - Technology and innovation patterns
        
        5. STRATEGIC RECOMMENDATIONS
        - Market entry strategies
        - Product development priorities
        - Partnership and investment opportunities
        
        6. RISK ASSESSMENT
        - Market risks and uncertainties
        - Regulatory and policy risks
        - Competitive and technology risks
        """
        
        client = openai.OpenAI(api_key="your-openai-key")
        response = client.chat.completions.create(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are a senior market research analyst."},
                {"role": "user", "content": synthesis_prompt}
            ],
            temperature=0.1,
            max_tokens=2500
        )
        
        return response.choices[0].message.content

class RegulatoryComplianceAgent:
    """Monitor regulatory changes and assess compliance impact"""
    
    def __init__(self, industry, jurisdictions):
        self.industry = industry
        self.jurisdictions = jurisdictions
        self.config = {
            "llm": {
                "model": "anthropic/claude-3-opus-20240229",
                "api_key": "your-anthropic-key"
            }
        }
    
    def monitor_regulatory_changes(self):
        """Monitor regulatory developments across jurisdictions"""
        
        regulatory_prompt = f"""
        Monitor regulatory developments for {self.industry} industry:
        - New regulations and policy proposals
        - Regulatory guidance and interpretation updates
        - Enforcement actions and compliance requirements
        - Industry-specific compliance standards
        - International regulatory harmonization efforts
        - Regulatory technology and reporting requirements
        - Stakeholder consultation and comment periods
        - Implementation timelines and deadlines
        """
        
        regulatory_sources = {}
        for jurisdiction in self.jurisdictions:
            if jurisdiction.lower() == "us":
                sources = [
                    "https://www.sec.gov/news/speeches-statements",
                    "https://www.federalregister.gov/",
                    f"https://www.regulations.gov/search?filter={self.industry}",
                ]
            elif jurisdiction.lower() == "eu":
                sources = [
                    "https://eur-lex.europa.eu/homepage.html",
                    "https://www.esma.europa.eu/news-events",
                    "https://www.eba.europa.eu/news-and-press",
                ]
            # Add more jurisdictions as needed
            
            jurisdiction_data = {}
            for source in sources:
                try:
                    scraper = SmartScraperGraph(
                        prompt=regulatory_prompt,
                        source=source,
                        config=self.config
                    )
                    data = scraper.run()
                    jurisdiction_data[source] = data
                except Exception as e:
                    print(f"Error monitoring {source}: {e}")
            
            regulatory_sources[jurisdiction] = jurisdiction_data
        
        return self._assess_compliance_impact(regulatory_sources)
    
    def _assess_compliance_impact(self, regulatory_data):
        """Assess compliance impact using AI analysis"""
        
        impact_prompt = f"""
        Assess regulatory compliance impact based on gathered intelligence:
        
        REGULATORY DATA:
        {regulatory_data}
        
        Provide:
        1. IMMEDIATE COMPLIANCE REQUIREMENTS
        - New regulations requiring immediate attention
        - Updated compliance standards and deadlines
        - Required policy and procedure updates
        
        2. IMPLEMENTATION TIMELINE
        - Phase-in periods and compliance deadlines
        - Resource requirements and budget implications
        - Training and system update needs
        
        3. BUSINESS IMPACT ASSESSMENT
        - Operational changes required
        - Cost implications and budget planning
        - Competitive implications and market impact
        
        4. COMPLIANCE STRATEGY
        - Priority actions and implementation sequence
        - Risk mitigation and monitoring plans
        - Stakeholder communication and change management
        
        5. ONGOING MONITORING
        - Key regulatory developments to track
        - Compliance metrics and reporting requirements
        - Early warning indicators for regulatory changes
        """
        
        client = Anthropic(api_key="your-anthropic-key")
        response = client.messages.create(
            model="claude-3-opus-20240229",
            max_tokens=2500,
            temperature=0.1,
            messages=[
                {"role": "user", "content": impact_prompt}
            ]
        )
        
        return response.content[0].text

class IndustryIntelligenceOrchestrator:
    """Orchestrate multiple specialized agents for comprehensive analysis"""
    
    def __init__(self, industry, company_name):
        self.industry = industry
        self.company_name = company_name
        
        # Initialize specialized agents
        self.financial_agent = FinancialAnalysisAgent()
        self.competitive_agent = CompetitiveIntelligenceAgent(
            company_name, 
            self._get_key_competitors()
        )
        self.market_agent = MarketResearchAgent(industry)
        self.regulatory_agent = RegulatoryComplianceAgent(
            industry, 
            ["US", "EU"]
        )
    
    def comprehensive_industry_analysis(self):
        """Coordinate multiple agents for comprehensive analysis"""
        
        # Collect intelligence from all agents
        financial_intelligence = self.financial_agent.analyze_investment_opportunity(
            self._get_company_ticker()
        )
        competitive_intelligence = self.competitive_agent.monitor_competitive_landscape()
        market_intelligence = self.market_agent.conduct_market_analysis()
        regulatory_intelligence = self.regulatory_agent.monitor_regulatory_changes()
        
        # Synthesize cross-functional insights
        return self._synthesize_strategic_insights(
            financial_intelligence,
            competitive_intelligence,
            market_intelligence,
            regulatory_intelligence
        )
    
    def _synthesize_strategic_insights(self, financial, competitive, market, regulatory):
        """Synthesize insights across all intelligence domains"""
        
        synthesis_prompt = f"""
        You are a senior strategy consultant providing comprehensive industry analysis.
        Synthesize intelligence from multiple domains to provide strategic insights:
        
        FINANCIAL INTELLIGENCE:
        {financial}
        
        COMPETITIVE INTELLIGENCE:
        {competitive}
        
        MARKET INTELLIGENCE:
        {market}
        
        REGULATORY INTELLIGENCE:
        {regulatory}
        
        Provide an executive summary with:
        
        1. STRATEGIC SITUATION ASSESSMENT
        - Current market position and competitive standing
        - Key opportunities and threats
        - Financial health and performance trends
        
        2. STRATEGIC PRIORITIES
        - Top 3 strategic priorities for the next 12 months
        - Resource allocation recommendations
        - Timeline and success metrics
        
        3. RISK MANAGEMENT
        - Key risks and mitigation strategies
        - Scenario planning and contingency options
        - Monitoring and early warning systems
        
        4. COMPETITIVE STRATEGY
        - Defensive strategies to protect market position
        - Offensive strategies for market expansion
        - Partnership and alliance opportunities
        
        5. IMPLEMENTATION ROADMAP
        - 90-day quick wins and immediate actions
        - 6-month strategic initiatives
        - 12-month transformation goals
        """
        
        client = openai.OpenAI(api_key="your-openai-key")
        response = client.chat.completions.create(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are a senior strategy consultant."},
                {"role": "user", "content": synthesis_prompt}
            ],
            temperature=0.1,
            max_tokens=3500
        )
        
        return response.choices[0].message.content

import schedule
import time
from datetime import datetime
import json
 
class ContinuousIntelligencePipeline:
    """Maintain continuous intelligence streams with automated analysis"""
    
    def __init__(self, config_file):
        with open(config_file, 'r') as f:
            self.config = json.load(f)
        
        self.orchestrator = IndustryIntelligenceOrchestrator(
            self.config['industry'],
            self.config['company_name']
        )
    
    def setup_intelligence_schedule(self):
        """Set up automated intelligence collection and analysis"""
        
        # Daily intelligence updates
        schedule.every().day.at("08:00").do(self.daily_intelligence_brief)
        
        # Weekly deep analysis
        schedule.every().monday.at("09:00").do(self.weekly_strategic_analysis)
        
        # Real-time alerts for critical changes
        schedule.every(30).minutes.do(self.monitor_critical_alerts)
        
        # Monthly comprehensive review
        schedule.every().month.do(self.monthly_intelligence_review)
    
    def daily_intelligence_brief(self):
        """Generate daily intelligence briefing"""
        
        brief_prompt = """
        Generate a concise daily intelligence brief focusing on:
        - Critical developments in the last 24 hours
        - Immediate action items and alerts
        - Key metrics and performance indicators
        - Priority items for management attention
        
        Keep the brief to 500 words maximum with clear action items.
        """
        
        intelligence = self.orchestrator.comprehensive_industry_analysis()
        
        # Process and format for daily consumption
        daily_brief = self._format_daily_brief(intelligence)
        
        # Send to stakeholders
        self._distribute_intelligence(daily_brief, "daily")
        
        return daily_brief
    
    def weekly_strategic_analysis(self):
        """Generate weekly strategic analysis"""
        
        weekly_analysis = self.orchestrator.comprehensive_industry_analysis()
        
        # Enhanced analysis with week-over-week trends
        enhanced_analysis = self._add_trend_analysis(weekly_analysis)
        
        # Distribute to strategic planning team
        self._distribute_intelligence(enhanced_analysis, "weekly")
        
        return enhanced_analysis
    
    def monitor_critical_alerts(self):
        """Monitor for critical changes requiring immediate attention"""
        
        alert_conditions = [
            "competitor_product_launch",
            "regulatory_announcement",
            "major_market_movement",
            "supply_chain_disruption",
            "cyber_security_incident"
        ]
        
        alerts = []
        for condition in alert_conditions:
            alert_check = self._check_alert_condition(condition)
            if alert_check['triggered']:
                alerts.append(alert_check)
        
        if alerts:
            self._send_immediate_alerts(alerts)
        
        return alerts
    
    def run_continuous_monitoring(self):
        """Run the continuous intelligence monitoring system"""
        
        print(f"Starting continuous intelligence monitoring at {datetime.now()}")
        
        while True:
            schedule.run_pending()
            time.sleep(60)  # Check every minute

from functools import lru_cache
import hashlib
import pickle
from datetime import datetime, timedelta
 
class IntelligenceCache:
    def __init__(self, cache_duration_hours=24):
        self.cache_duration = timedelta(hours=cache_duration_hours)
        self.cache = {}
    
    def get_cached_intelligence(self, query_hash):
        if query_hash in self.cache:
            cached_data, timestamp = self.cache[query_hash]
            if datetime.now() - timestamp < self.cache_duration:
                return cached_data
        return None
    
    def cache_intelligence(self, query_hash, data):
        self.cache[query_hash] = (data, datetime.now())
    
    def generate_query_hash(self, query_params):
        query_string = json.dumps(query_params, sort_keys=True)
        return hashlib.md5(query_string.encode()).hexdigest()

import asyncio
import aiohttp
from concurrent.futures import ThreadPoolExecutor
 
class ParallelIntelligenceCollector:
    def __init__(self, max_workers=10):
        self.max_workers = max_workers
        self.executor = ThreadPoolExecutor(max_workers=max_workers)
    
    async def collect_parallel_intelligence(self, sources, prompts):
        """Collect intelligence from multiple sources in parallel"""
        
        tasks = []
        for source, prompt in zip(sources, prompts):
            task = asyncio.create_task(
                self._extract_from_source(source, prompt)
            )
            tasks.append(task)
        
        results = await asyncio.gather(*tasks, return_exceptions=True)
        
        # Filter out exceptions and return successful results
        successful_results = [
            result for result in results 
            if not isinstance(result, Exception)
        ]
        
        return successful_results
    
    async def _extract_from_source(self, source, prompt):
        """Extract intelligence from a single source"""
        
        loop = asyncio.get_event_loop()
        
        # Run the scraping in a thread pool to avoid blocking
        result = await loop.run_in_executor(
            self.executor,
            self._scrape_source,
            source,
            prompt
        )
        
        return result

class ProductionIntelligenceSystem:
    def __init__(self, environment="production"):
        self.environment = environment
        self.config = self._load_production_config()
        self.monitoring = IntelligenceMonitoring()
        self.alerting = AlertingSystem()
    
    def _load_production_config(self):
        """Load production configuration with security best practices"""
        
        return {
            "rate_limiting": {
                "requests_per_minute": 100,
                "concurrent_requests": 20,
                "backoff_strategy": "exponential"
            },
            "error_handling": {
                "max_retries": 3,
                "timeout_seconds": 30,
                "circuit_breaker_threshold": 10
            },
            "security": {
                "api_key_rotation": True,
                "request_encryption": True,
                "audit_logging": True
            },
            "performance": {
                "caching_enabled": True,
                "parallel_processing": True,
                "result_compression": True
            }
        }
    
    def deploy_agent(self, agent_config):
        """Deploy agent with production monitoring"""
        
        # Validate configuration
        self._validate_agent_config(agent_config)
        
        # Set up monitoring
        self.monitoring.setup_agent_monitoring(agent_config['agent_id'])
        
        # Deploy with health checks
        deployment_result = self._deploy_with_health_checks(agent_config)
        
        # Configure alerting
        self.alerting.setup_agent_alerts(agent_config['agent_id'])
        
        return deployment_result