AI Agents by Industry: Sector-Specific Applications

class ClinicalDecisionSupportAgent:
    def __init__(self):
        self.diagnosis = DiagnosisAgent()
        self.medications = MedicationAgent()
        self.interactions = InteractionChecker()
        self.guidelines = GuidelineAgent()
    
    async def support_diagnosis(self, patient: Patient, symptoms: list) -> Recommendation:
        # Get diagnostic suggestions
        diagnoses = await self.diagnosis.suggest(symptoms, patient.history)
        
        # Check medication interactions
        interactions = await self.interactions.check(
            patient.current_medications,
            diagnoses.recommended_medications
        )
        
        # Check against guidelines
        guideline_recommendations = await self.guidelines.apply(
            diagnoses,
            patient
        )
        
        # Generate recommendation
        return Recommendation(
            possible_diagnoses=diagnoses,
            medications=interactions,
            guidelines=guideline_recommendations,
            confidence=diagnoses.confidence,
            requires_human_approval=True  # Always for healthcare
        )


CASE_STUDY = {
    "company": "IBM Watson Health",
    "application": "Clinical decision support",
    "results": {
        "diagnostic_accuracy": "+25%",
        "treatment_adherence": "+30%",
        "physician_time_savings": "40%"
    }
}

class PatientSchedulingAgent:
    def __init__(self):
        self.availability = AvailabilityAgent()
        self.preferences = PreferenceAgent()
        self.reminders = ReminderAgent()
        self.waitlist = WaitlistAgent()
    
    async def schedule_appointment(
        self, 
        patient: Patient, 
        provider: Provider,
        reason: str
    ) -> Appointment:
        # Check provider availability
        slots = await self.availability.get_slots(provider, reason)
        
        # Consider patient preferences
        preferred = await self.preferences.get_preferred_times(patient)
        suitable = [s for s in slots if s in preferred]
        
        if suitable:
            slot = suitable[0]
        else:
            slot = slots[0]
        
        # Book appointment
        appointment = await self.book(patient, provider, slot)
        
        # Send reminders
        await self.reminders.schedule(appointment, patient)
        
        return appointment
    
    async def handle_cancellation(self, appointment: Appointment):
        # Find waitlist candidates
        waitlist = await self.waitlist.get_matching(appointment)
        
        if waitlist:
            # Offer to waitlist patients
            for patient in waitlist[:3]:
                offered = await self.offer_slot(patient, appointment.slot)
                if offered.accepted:
                    break


CASE_STUDY = {
    "company": "Notable Health",
    "application": "Patient scheduling automation",
    "results": {
        "no_show_rate": "-35%",
        "scheduling_time": "-80%",
        "patient_satisfaction": "+20%"
    }
}

class InvestmentResearchAgent:
    def __init__(self):
        self.market = MarketDataAgent()
        self.fundamental = FundamentalAgent()
        self.sentiment = SentimentAgent()
        self.analyst = AnalystAgent()
    
    async def research_company(self, ticker: str) -> ResearchReport:
        # Gather market data
        market_data = await self.market.get_data(ticker)
        
        # Analyze fundamentals
        fundamentals = await self.fundamental.analyze(ticker)
        
        # Sentiment analysis
        sentiment = await self.sentiment.analyze(ticker)
        
        # Generate report
        report = await self.analyst.generate(
            ticker=ticker,
            market=market_data,
            fundamentals=fundamentals,
            sentiment=sentiment
        )
        
        return Report(
            ticker=ticker,
            rating=report.rating,
            target_price=report.target,
            analysis=report.analysis,
            confidence=report.confidence,
            risks=report.risks
        )


CASE_STUDY = {
    "company": "Bloomberg",
    "application": "AI investment research",
    "results": {
        "research_coverage": "+300%",
        "analyst_productivity": "+50%",
        "idea_generation": "+200%"
    }
}

class FraudDetectionAgent:
    def __init__(self):
        self.transaction_monitor = TransactionMonitor()
        self.pattern_detector = PatternDetector()
        self.investigator = InvestigationAgent()
        self.blocker = BlockerAgent()
    
    async def evaluate_transaction(self, transaction: Transaction) -> Decision:
        # Real-time monitoring
        flags = await self.transaction_monitor.check(transaction)
        
        # Pattern detection
        patterns = await self.pattern_detector.analyze(
            transaction,
            account=transaction.account
        )
        
        # Calculate risk score
        risk_score = self.calculate_risk(flags, patterns)
        
        if risk_score > 0.95:
            # High risk - block immediately
            await self.blocker.block(transaction)
            return Decision(action="block", reason="high_risk")
        
        elif risk_score > 0.7:
            # Medium risk - investigate
            investigation = await self.investigator.start(transaction)
            return Decision(action="review", investigation_id=investigation.id)
        
        # Low risk - approve
        return Decision(action="approve")


CASE_STUDY = {
    "company": "PayPal",
    "application": "AI fraud detection",
    "results": {
        "fraud_detection_rate": "99.9%",
        "false_positive_rate": "0.1%",
        "annual_fraud_prevention": "$20B+"
    }
}

class PersonalShoppingAgent:
    def __init__(self):
        self.preferences = PreferenceAgent()
        self.inventory = InventoryAgent()
        self.pricing = PricingAgent()
        self.recommendations = RecommendationAgent()
    
    async def create_outfit(self, customer: Customer, occasion: str) -> OutfitRecommendation:
        # Get customer preferences
        prefs = await self.preferences.get(customer)
        
        # Get inventory for occasion
        items = await self.inventory.search(
            category=["top", "bottom", "shoes"],
            occasion=occasion,
            size=customer.size
        )
        
        # Filter by preferences
        filtered = self.filter_by_preferences(items, prefs)
        
        # Get personalized pricing
        priced = await self.pricing.personalize(filtered, customer)
        
        # Generate outfit
        outfit = await self.recommendations.create_outfit(priced)
        
        return OutfitRecommendation(
            items=outfit.items,
            total_price=outfit.total,
            savings=outfit.savings,
            confidence=outfit.confidence
        )


CASE_STUDY = {
    "company": "Stitch Fix",
    "application": "AI styling agent",
    "results": {
        "personalization_accuracy": "80%",
        "return_rate": "-20%",
        "customer_retention": "+30%"
    }
}

class InventoryAgent:
    def __init__(self):
        self.demand = DemandForecaster()
        self.replenishment = ReplenishmentAgent()
        self.allocations = AllocationAgent()
        self.alerts = AlertAgent()
    
    async def optimize_inventory(self, retailer: Retailer) -> OptimizationPlan:
        # Forecast demand
        forecastsemand.forecast_all = await self.d_products(
            horizon="90d",
            location="all"
        )
        
        # Generate replenishment orders
        orders = await self.replenishment.calculate(
            forecasts,
            current_inventory=await self.get_inventory(),
            lead_times=await self.get_lead_times()
        )
        
        # Optimize allocations
        allocations = await self.allocations.optimize(
            forecasts,
            store_locations=retailer.locations
        )
        
        # Generate alerts
        alerts = await self.alerts.check(
            inventory=await self.get_inventory(),
            thresholds=retailer.thresholds
        )
        
        return OptimizationPlan(
            orders=orders,
            allocations=allocations,
            alerts=alerts,
            expected_improvement=self.calculate_improvement(orders, allocations)
        )


CASE_STUDY = {
    "company": "Walmart",
    "application": "AI inventory management",
    "results": {
        "inventory_availability": "+15%",
        "stockouts": "-30%",
        "markdowns": "-20%"
    }
}

class PredictiveMaintenanceAgent:
    def __init__(self):
        self.sensors = SensorMonitor()
        self.predictor = FailurePredictor()
        self.scheduler = MaintenanceScheduler()
        self.parts = PartsAgent()
    
    async def monitor_equipment(self, equipment: Equipment) -> MonitoringResult:
        # Get sensor data
        sensors = await self.sensors.get_latest(equipment.id)
        
        # Check for anomalies
        anomalies = await self.detect_anomalies(sensors)
        
        if anomalies:
            # Predict failure
            prediction = await self.predictor.predict(equipment, anomalies)
            
            if prediction.failure_probability > 0.7:
                # Schedule maintenance
                schedule = await self.scheduler.schedule(
                    equipment=equipment,
                    predicted_failure=prediction.failure_date,
                    production_schedule=await self.get_production_schedule()
                )
                
                # Check parts availability
                parts = await self.parts.check_availability(
                    prediction.required_parts
                )
                
                return MonitoringResult(
                    status="action_required",
                    prediction=prediction,
                    maintenance_scheduled=schedule,
                    parts_status=parts
                )
        
        return MonitoringResult(status="normal", sensors=sensors)


CASE_STUDY = {
    "company": "General Electric",
    "application": "Preditive maintenance (Predix)",
    "results": {
        "downtime_reduction": "25%",
        "maintenance_cost": "-20%",
        "equipment_life": "+15%"
    }
}

class QualityControlAgent:
    def __init__(self):
        self.vision = VisionInspectionAgent()
        self.spc = StatisticalProcessControl()
        self.root_cause = RootCauseAgent()
        self.classifier = DefectClassifier()
    
    async def inspect(self, product: Product, image: Image) -> InspectionResult:
        # Vision-based inspection
        defects = await self.vision.detect_defects(image)
        
        # Classify defects
        classified = []
        for defect in defects:
            classification = await self.classifier.classify(defect)
            classified.append(classification)
        
        # Statistical process control
        spc_results = await self.spc.check(product.process_data)
        
        # Determine disposition
        if any(d.severity == "critical" for d in classified):
            disposition = "reject"
        elif spc_results.out_of_control:
            disposition = "hold"
        else:
            disposition = "release"
        
        # Root cause for defects
        if disposition != "release":
            root_cause = await self.root_cause.analyze(classified, spc_results)
        else:
            root_cause = None
        
        return InspectionResult(
            disposition=disposition,
            defects=classified,
            spc=spc_results,
            root_cause=root_cause
        )


CASE_STUDY = {
    "company": "Siemens",
    "application": "AI quality control",
    "results": {
        "defect_detection_rate": "99.9%",
        "false_positive_rate": "-50%",
        "inspection_speed": "+300%"
    }
}

# Universal vs industry-specific components

UNIVERSAL = [
    "llm_interface",
    "context_management",
    "conversation_flow",
    "basic_nlu"
]

INDUSTRY_SPECIFIC = {
    "healthcare": [
        "hipaa_compliance",
        "clinical_decision_support",
        "medical_termiology",
        "ehr_integration"
    ],
    
    "finance": [
        "fraud_detection",
        "risk_assessment",
        "regulatory_compliance",
        "audit_trails"
    ],
    
    "retail": [
        "inventory_management",
        "personalization",
        "demand_forecasting",
        "pricing_optimization"
    ],
    
    "manufacturing": [
        "predictive_maintenance",
        "quality_control",
        "process_optimization",
        "iot_integration"
    ]
}