Domain Specialist AI Agents

from typing import Dict, List
from datetime import datetime
import hashlib

class HealthcareAgent:
    def __init__(self):
        self.phi_encryption_key = self._load_encryption_key()
        self.audit_logger = AuditLogger()
    
    async def generate_clinical_note(self, patient_id: str, encounter_data: Dict) -> str:
        # Verify HIPAA authorization
        if not await self._verify_hipaa_authorization(patient_id):
            await self.audit_logger.log_unauthorized_access(patient_id)
            raise PermissionError("Unauthorized access to PHI")
        
        # Generate SOAP note
        soap_note = f"""
Subjective: {encounter_data['chief_complaint']}
Objective: Vitals - BP: {encounter_data['vitals']['bp']}, HR: {encounter_data['vitals']['hr']}
Assessment: {await self._generate_assessment(encounter_data)}
Plan: {await self._generate_treatment_plan(encounter_data)}
        """
        
        # Encrypt PHI
        encrypted_note = self._encrypt_phi(soap_note)
        
        # Audit log
        await self.audit_logger.log_phi_access(
            user_id=encounter_data['provider_id'],
            patient_id=patient_id,
            action='clinical_note_generated'
        )
        
        return encrypted_note
    
    async def medical_coding_assistant(self, clinical_note: str) -> Dict:
        # Extract ICD-10 and CPT codes
        icd_codes = await self._extract_icd10_codes(clinical_note)
        cpt_codes = await self._extract_cpt_codes(clinical_note)
        
        return {
            'icd10_codes': icd_codes,
            'cpt_codes': cpt_codes,
            'billing_compliance': await self._validate_coding_compliance(icd_codes, cpt_codes)
        }

class LegalAgent:
    def __init__(self):
        self.contract_kb = ContractKnowledgeBase()
        self.regulatory_db = RegulatoryDatabase()
    
    async def analyze_contract(self, contract_text: str, contract_type: str) -> Dict:
        analysis = {
            'key_clauses': await self._extract_key_clauses(contract_text),
            'risks': await self._identify_risks(contract_text),
            'obligations': await self._extract_obligations(contract_text),
            'compliance': await self._check_regulatory_compliance(contract_text, contract_type)
        }
        
        # Flag high-risk clauses
        for clause in analysis['key_clauses']:
            if clause['risk_level'] == 'high':
                analysis['requires_attorney_review'] = True
        
        return analysis
    
    async def generate_s1_filing(self, company_data: Dict) -> str:
        # Harvey-style S-1 filing automation
        sections = {
            'prospectus_summary': await self._generate_prospectus(company_data),
            'risk_factors': await self._generate_risk_factors(company_data),
            'use_of_proceeds': await self._generate_use_of_proceeds(company_data),
            'financial_statements': await self._format_financial_statements(company_data['financials'])
        }
        
        # SEC compliance validation
        compliance_check = await self._validate_sec_compliance(sections)
        
        return self._compile_s1_document(sections, compliance_check)

class FinancialAgent:
    def __init__(self):
        self.risk_model = RiskAssessmentModel()
        self.forecasting_model = ForecastingModel()
    
    async def portfolio_risk_analysis(self, portfolio: Dict) -> Dict:
        return {
            'var_95': await self._calculate_var(portfolio, confidence=0.95),
            'expected_shortfall': await self._calculate_expected_shortfall(portfolio),
            'stress_test_results': await self._run_stress_tests(portfolio),
            'concentration_risk': await self._analyze_concentration(portfolio),
            'recommendations': await self._generate_risk_recommendations(portfolio)
        }
    
    async def financial_forecast(self, historical_data: List, horizon: int) -> Dict:
        forecast = await self.forecasting_model.predict(
            data=historical_data,
            periods=horizon,
            include_confidence_intervals=True
        )
        
        return {
            'point_forecast': forecast['predictions'],
            'confidence_intervals': forecast['ci'],
            'scenario_analysis': await self._run_scenarios(historical_data),
            'key_assumptions': forecast['assumptions']
        }

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  "systemPrompt": "You are a domain-specialist AI agent architect focused on healthcare, legal, and financial industry applications"
}