Overview: Autonomous agents handling customer inquiries 24/7 with human-like understanding and problem-solving capabilities.
# Customer Service Agent Architecture
class CustomerServiceAgent:
def __init__(self):
self.components = {
"intent_classifier": "Identifies customer intent",
"sentiment_analyzer": "Detects emotional state",
"knowledge_base": "Company policies, FAQs, procedures",
"ticket_system": "CRM integration",
"escalation_handler": "Human handoff logic"
}
async def handle_inquiry(self, message, customer_id):
# 1. Classify intent
intent = await self.classify_intent(message)
# 2. Analyze sentiment
sentiment = await self.analyze_sentiment(message)
# 3. Retrieve customer context
context = await self.get_customer_context(customer_id)
# 4. Generate response
if intent in ["refund", "complaint", "technical_issue"]:
response = await self.handle_complex_query(
intent, context, sentiment
)
else:
response = await self.handle_simple_query(intent)
# 5. Check if escalation needed
if sentiment.score < 0.3 or intent.confidence < 0.7:
await self.escalate_to_human(message, context)
return response
Overview: AI agents that qualify leads, personalize outreach, schedule meetings, and nurture prospects through the sales funnel.
Overview: Automate candidate screening, interview scheduling, onboarding processes, and employee engagement initiatives.
# HR Recruitment Agent
class RecruitmentAgent:
async def screen_candidate(self, resume, job_requirements):
# Extract skills and experience
candidate_profile = await self.parse_resume(resume)
# Match against requirements
match_score = await self.calculate_match(
candidate_profile,
job_requirements
)
# Generate screening questions
if match_score > 0.7:
questions = await self.generate_screening_questions(
candidate_profile,
job_requirements
)
# Schedule initial interview
await self.schedule_interview(candidate_profile.email)
return {
"match_score": match_score,
"recommendation": self.get_recommendation(match_score),
"next_steps": self.determine_next_steps(match_score)
}
Overview: AI agents that assist physicians in diagnosis, treatment planning, and clinical decision-making based on patient data and medical literature.
# Clinical Decision Support System
class ClinicalDecisionAgent:
def __init__(self):
self.models = {
"symptom_checker": SymptomAnalyzer(),
"lab_interpreter": LabResultsInterpreter(),
"imaging_analyzer": ImagingAIModel(),
"drug_interactions": DrugInteractionChecker(),
"treatment_recommender": TreatmentRecommender()
}
async def analyze_patient(self, patient_data):
# Comprehensive patient analysis
symptoms = await self.analyze_symptoms(patient_data.symptoms)
lab_insights = await self.interpret_labs(patient_data.lab_results)
imaging_findings = await self.analyze_imaging(patient_data.scans)
# Risk assessment
risk_factors = await self.assess_risks(patient_data)
# Differential diagnosis
diagnoses = await self.generate_differential(
symptoms, lab_insights, imaging_findings
)
# Treatment recommendations
treatments = await self.recommend_treatments(
diagnoses,
patient_data.medical_history,
patient_data.allergies
)
# Check drug interactions
interactions = await self.check_drug_interactions(
treatments.medications,
patient_data.current_medications
)
return {
"diagnoses": diagnoses,
"confidence": self.calculate_confidence(diagnoses),
"treatments": treatments,
"warnings": interactions,
"follow_up": self.schedule_follow_up(diagnoses)
}
Overview: Automated patient intake, symptom assessment, and triage prioritization for emergency departments and clinics.
Overview: AI agents that accelerate drug discovery by predicting molecular interactions, identifying potential compounds, and optimizing clinical trials.
Overview: Autonomous trading agents that analyze markets, execute trades, and manage portfolios with advanced risk management.
# Algorithmic Trading Agent
class TradingAgent:
def __init__(self):
self.strategies = {
"momentum": MomentumStrategy(),
"arbitrage": ArbitrageStrategy(),
"mean_reversion": MeanReversionStrategy(),
"sentiment": SentimentStrategy()
}
self.risk_manager = RiskManagement()
async def execute_trade_decision(self, market_data):
# Analyze market conditions
signals = await self.generate_signals(market_data)
# Risk assessment
risk_metrics = await self.risk_manager.assess(
signals,
self.portfolio
)
# Position sizing
position_size = self.calculate_position_size(
signals.confidence,
risk_metrics,
self.portfolio.available_capital
)
# Execute if conditions met
if self.should_trade(signals, risk_metrics):
order = await self.place_order(
symbol=signals.symbol,
size=position_size,
type=signals.order_type,
limit_price=signals.limit_price
)
# Set stop loss and take profit
await self.set_risk_parameters(order)
return order
Overview: Real-time fraud detection agents that identify suspicious transactions, prevent financial crimes, and protect customer accounts.
Overview: AI agents that evaluate credit risk, automate underwriting decisions, and optimize loan portfolios.
Overview: AI agents that provide personalized product recommendations, style advice, and shopping assistance based on customer preferences.
# Personal Shopping Assistant
class ShoppingAssistant:
async def recommend_products(self, user_profile):
# Analyze user preferences
preferences = await self.analyze_preferences(
user_profile.purchase_history,
user_profile.browsing_behavior,
user_profile.demographics
)
# Generate recommendations
recommendations = await self.collaborative_filtering(
user_profile,
similar_users=self.find_similar_users(user_profile)
)
# Personalize results
personalized = await self.personalize_recommendations(
recommendations,
preferences,
context={
"season": self.get_current_season(),
"trends": self.get_trending_items(),
"inventory": self.check_inventory()
}
)
# Add complementary items
complete_looks = await self.create_complete_looks(
personalized
)
return {
"recommendations": personalized[:10],
"complete_looks": complete_looks,
"reasons": self.explain_recommendations(personalized)
}
Overview: Intelligent agents that optimize inventory levels, predict demand, and automate reordering processes.
Overview: AI agents that analyze customer reviews, extract insights, and provide actionable feedback for product improvement.
Overview: AI tutors that provide personalized instruction, adapt to learning styles, and offer real-time feedback to students.
# Intelligent Tutoring System
class TutoringAgent:
async def personalized_lesson(self, student_profile):
# Assess current knowledge
knowledge_state = await self.assess_knowledge(
student_profile.assessment_history
)
# Identify learning gaps
gaps = await self.identify_gaps(
knowledge_state,
curriculum_requirements=self.get_curriculum()
)
# Generate adaptive content
lesson = await self.generate_lesson(
gaps=gaps,
learning_style=student_profile.learning_style,
difficulty_level=self.calculate_optimal_difficulty(
student_profile.performance_history
)
)
# Create interactive exercises
exercises = await self.create_exercises(
lesson.concepts,
difficulty=lesson.difficulty,
format=student_profile.preferred_format
)
# Provide real-time feedback
async for response in student_responses:
feedback = await self.generate_feedback(
response,
encouraging=True,
hints=self.generate_hints(response.errors)
)
# Adjust difficulty dynamically
if response.is_correct:
lesson.difficulty += 0.1
else:
lesson.difficulty -= 0.05
return lesson
Overview: AI agents that grade assignments, provide detailed feedback, and track student progress across subjects.
Overview: AI agents that help researchers find relevant papers, summarize literature, and identify research gaps.
Overview: AI agents that analyze contracts, identify risks, suggest modifications, and ensure compliance with regulations.
# Contract Analysis Agent
class ContractReviewAgent:
async def analyze_contract(self, contract_text):
# Extract key clauses
clauses = await self.extract_clauses(contract_text)
# Identify risks
risks = []
for clause in clauses:
risk_assessment = await self.assess_risk(clause)
if risk_assessment.score > 0.7:
risks.append({
"clause": clause,
"risk_level": risk_assessment.level,
"explanation": risk_assessment.explanation,
"suggestion": self.suggest_modification(clause)
})
# Check compliance
compliance = await self.check_compliance(
contract_text,
regulations=["GDPR", "CCPA", "SOX"]
)
# Generate summary
summary = await self.generate_summary(
clauses=clauses,
risks=risks,
compliance=compliance
)
return {
"summary": summary,
"risks": risks,
"compliance": compliance,
"recommendations": self.generate_recommendations(risks)
}
Overview: AI agents that search case law, identify precedents, and provide comprehensive legal research summaries.
Overview: Continuous monitoring agents that track regulatory changes, assess compliance status, and alert to potential violations.
Overview: Computer vision agents that inspect products, detect defects, and ensure quality standards in real-time.
# Quality Control Vision Agent
class QualityControlAgent:
async def inspect_product(self, image_stream):
# Real-time image processing
async for frame in image_stream:
# Detect defects
defects = await self.detect_defects(frame)
if defects:
# Classify defect type
defect_type = await self.classify_defect(defects)
# Determine severity
severity = self.assess_severity(defect_type)
# Take action
if severity == "critical":
await self.stop_production_line()
await self.alert_supervisor(defect_type, frame)
elif severity == "major":
await self.mark_for_rework(product_id)
else:
await self.log_minor_defect(product_id, defect_type)
# Update quality metrics
await self.update_metrics({
"defect_rate": self.calculate_defect_rate(),
"defect_type": defect_type,
"timestamp": datetime.now()
})
# Predictive quality
quality_trend = await self.predict_quality_trend(
self.recent_inspections
)
if quality_trend.declining:
await self.recommend_maintenance()
Overview: AI agents that predict equipment failures, schedule maintenance, and optimize machine uptime.
Overview: Intelligent agents that optimize supply chains, predict demand, and manage inventory across multiple locations.
# Industry-Specific ROI Calculator
def calculate_industry_roi(industry, params):
industry_multipliers = {
"healthcare": {"labor_savings": 2.5, "error_reduction": 3.0},
"finance": {"automation": 3.5, "fraud_prevention": 5.0},
"ecommerce": {"conversion": 2.0, "customer_lifetime": 1.8},
"education": {"scalability": 10.0, "retention": 2.2},
"legal": {"efficiency": 4.0, "accuracy": 2.8},
"manufacturing": {"quality": 2.5, "downtime": 3.5}
}
base_roi = calculate_base_roi(params)
industry_factor = industry_multipliers[industry]
adjusted_benefits = {
"labor_savings": params['labor_savings'] * industry_factor.get('labor_savings', 1),
"error_reduction": params['error_cost'] * industry_factor.get('error_reduction', 1),
"revenue_increase": params['revenue'] * industry_factor.get('conversion', 1)
}
total_benefits = sum(adjusted_benefits.values())
total_costs = params['development'] + params['infrastructure'] + params['maintenance']
return {
'industry': industry,
'total_benefits': total_benefits,
'total_costs': total_costs,
'roi_percentage': ((total_benefits - total_costs) / total_costs) * 100,
'payback_months': total_costs / (total_benefits / 12),
'break_even_point': find_break_even(total_costs, total_benefits)
}