# Clear, deterministic logic - like following a recipe
SET total_cost TO 0
FOR EACH item IN shopping_cart DO:
    SET total_cost TO total_cost + item.price
    IF item.category EQUALS "electronics" THEN:
        APPLY warranty_option
    END_IF
END_FOR

# Intelligent operations - like asking an expert colleague
INTELLIGENTLY assess_customer_sentiment FROM feedback WITH:
    MUST_INCLUDE: [emotion_indicators, satisfaction_level]
    OUTPUT_FORMAT: detailed_analysis
END

MATHEMATICAL_CONTEXT:
    DOMAIN: real
    PRECISION: high
END_CONTEXT

# Physics constraints must be satisfied
ASSERT energy_in EQUALS energy_out + losses
ASSERT ALL(probabilities >= 0 AND probabilities <= 1)

DO subtotal_example:
    SET prices TO [12.99, 4.50, 7.25]
    SET subtotal TO SUM(prices)
    SEND "Subtotal is {subtotal}" TO console
END

DEFINE PROCEDURE apply_discount WITH PARAMETERS [amount]:
    IF amount >= 100 THEN:
        RETURN amount * 0.9
    ELSE:
        RETURN amount
    END_IF
END_PROCEDURE

DO count_electronics:
    SET count TO 0
    FOR EACH item IN shopping_cart DO:
        IF item.category EQUALS "electronics" THEN:
            SET count TO count + 1
        END_IF
    END_FOR
    SEND "Electronics items: {count}" TO console
END

DEFINE PROCEDURE shipping_cost WITH PARAMETERS [region]:
    MATCH region WITH:
        CASE "US": RETURN 5
        CASE "EU": RETURN 8
        CASE "APAC": RETURN 10
        DEFAULT: RETURN 12
    END_MATCH
END_PROCEDURE

DO pipeline:
    GET orders FROM "orders.csv"
    SET avg TO MEAN(orders.total)
    SEND {average_order: avg} TO "reports/daily.json"
END

DO finance_math:
    MATHEMATICAL_CONTEXT:
        DOMAIN: real
        PRECISION: high
    END_CONTEXT

    SET rates TO [0.01, 0.02, 0.03]
    SET expected TO MEAN(rates)
    ASSERT expected >= 0 AND expected <= 1
    SEND "Expected rate {expected}" TO console
END

DEFINE PROCEDURE normalize WITH PARAMETERS [xs]:
    SET s TO SUM(xs)
    IF s == 0 THEN RETURN xs END_IF
    RETURN [ x / s FOR x IN xs ]
END_PROCEDURE

DO summarize_feedback:
    GET feedback FROM "feedback.csv"
    INTELLIGENTLY assess_customer_sentiment FROM feedback WITH:
        MUST_INCLUDE: [emotion_indicators, satisfaction_level]
        OUTPUT_FORMAT: summary
    END
    SEND summary TO stakeholders
END

DO campaign_ideas:
    CREATIVELY generate_marketing_message BASED_ON:
        audience_demographics AND recent_sentiment
        CONSTRAINTS: [brand_guidelines, regulatory_requirements]
    END
    SEND marketing_message TO "[email protected]"
END

# Insurance pricing combines all three layers naturally
DEFINE PROCEDURE price_insurance_policy WITH PARAMETERS [applicant]:
    # Logic and domain knowledge interweave naturally
    IF applicant.age > 65 AND applicant.has_pre_existing_conditions THEN:
        # Mathematical constraints from actuarial science
        SET base_risk TO CALCULATE_ACTUARIAL_RISK(mortality_tables, applicant)
        
        # Domain expertise expressed directly
        INTELLIGENTLY adjust_for_lifestyle_factors:
            CONSIDER exercise_habits, occupation_risks, family_history
            APPLY industry_best_practices
            ENSURE regulatory_compliance
        END
        
        # Price from risk
        SET premium TO PRICE_PREMIUM(base_risk, loadings, target_margin)

        # Mathematical reality enforces boundaries
        ASSERT premium * expected_policies > expected_payouts + operational_costs
    END_IF
END_PROCEDURE

# Create a person with background and personality
INTELLIGENTLY create_person FROM "London" WITH:
    OUTPUT: alice
    MUST_INCLUDE: [name, background, personality, interaction_system]
    BOUNDS: "fictional but locally plausible"
    HINTS: {name: "Alice Chen", age_range: [25,35], profession: "data analyst"}
END

# Set personality traits (Logos/Energiae/Ethos model)
SET alice.personality.logos.reasoning_style TO "analytical"
SET alice.personality.energiae.drives TO {achievement: 0.8, connection: 0.7}
SET alice.personality.ethos.core_values TO ["integrity", "growth"]

# Example: Team collaboration with emergent dynamics
DO simulate_project_meeting:
    # Create team with different personalities
    INTELLIGENTLY create_person WITH:
        OUTPUT: sophie
        HINTS: {traits: ["detail-oriented", "morning-person"]}
    END
    
    INTELLIGENTLY create_person WITH:
        OUTPUT: james
        HINTS: {traits: ["creative", "night-owl"]}
    END
    
    # Their interactions emerge from personality differences
    SET discussion TO sophie.interact_with_person(james, work_context)
    
    # Natural conflicts arise and resolve
    IF sophie.wants_morning_meeting AND james.prefers_afternoon THEN:
        # Resolution emerges from their interaction systems
        ADAPTIVELY find_compromise BASED_ON:
            sophie.personality.ethos  # Values efficiency
            james.personality.energiae # Peak creativity afternoon
        END
        # Result: Agree on late morning with coffee
    END_IF
END

DEFINE PROCEDURE diagnose_bread_problem WITH PARAMETERS [user_observation]:
    GET description FROM "My bread's been coming out flat and dense lately."
    GET environmental_data FROM kitchen_sensors
    
    # Build testable hypotheses
    SET potential_factors TO []
    
    # Temperature hypothesis
    IF kitchen_sensors.temperature != previous_location.temperature THEN:
        # Arrhenius equation for yeast activity
        SET k1 TO A * e^(-Ea/(R*T1))  # Rate at old temperature
        SET k2 TO A * e^(-Ea/(R*T2))  # Rate at new temperature
        SET activity_ratio TO k2/k1
        
        IF activity_ratio < 0.5 THEN:
            RECOMMEND: "Your yeast is working at {activity_ratio*100}% speed. 
                       Try proofing {1/activity_ratio} times longer."
        END_IF
    END_IF
END_PROCEDURE

DEFINE PROCEDURE analyze_unknown_pattern WITH PARAMETERS [data]:
    MATHEMATICAL_CONTEXT:
        DOMAIN: real
        PRECISION: high
    END_CONTEXT
    
    # Test data characteristics
    SET autocorrelation TO [ CORRELATE(data, SHIFT(data, lag)) FOR lag IN [1..n/4] ]
    SET has_periodicity TO MAX(autocorrelation[2:]) > 0.7
    
    # Branch based on discovered characteristics
    IF has_periodicity THEN:
        # Apply frequency analysis
        SET spectrum TO FOURIER_TRANSFORM(data)
        SET frequencies TO FIND_PEAKS(|spectrum|)
        SET period TO 1 / frequencies[0]
    ELSE IF is_exponential THEN:
        # Exponential growth/decay detected
        SET growth_rate TO (LOG(data[-1]) - LOG(data[0])) / n
        SET doubling_time TO ln(2) / growth_rate IF growth_rate > 0
    END_IF
    
    RETURN findings_in_natural_language
END_PROCEDURE

# Five friends from Newcastle plan Madrid trip
CREATE group WITH [lee, ayesha, callum, sophie, gavin]

# Each person's traits influence the trip
# Sophie (runner) → dawn runs in Retiro
# Lee (social) → late-night bars
# Ayesha (vegetarian) → restaurant choices

# Natural friction emerges and resolves
IF venue_too_loud FOR sophie THEN:
    GROUP splits_temporarily
    lee.continues_night
    sophie.returns_for_sleep
    AGREEMENT: "Meet for breakfast"
END_IF

# Outputs both structured data and narrative
SEND holiday_report TO "trip_data.json"
SEND holiday_memoir TO "trip_story.md"

# A complete program combining all three layers
DO analyze_customer_behavior:
    # Deterministic: Load data
    GET transactions FROM "customer_transactions.csv"
    
    # Mathematical: Calculate metrics
    SET average_order TO MEAN(transactions.amount)
    SET trend TO LINEAR_REGRESSION(transactions.amount OVER time)
    
    # Intelligent: Understand behavior
    INTELLIGENTLY segment_customers BASED_ON:
        transaction_patterns, timing, product_preferences
        OUTPUT: customer_segments WITH characteristics
    END
    
    # Creative: Generate strategies
    FOR EACH segment IN customer_segments DO:
        CREATIVELY design_retention_strategy CONSIDERING:
            segment.characteristics, market_conditions, company_resources
        END
    END_FOR
    
    # Deterministic: Output results
    SEND analysis_report TO stakeholders
END