KYA: Know Your Agent - AI Identity in Crypto Complete Guide 2026

class KYCvsKYA:
    def compare(self):
        return {
            'KYC': {
                'focus': 'Human identity verification',
                'documents': 'Passport, ID, utility bill',
                'process': 'Manual document review',
                'static': 'Verified once, valid indefinitely'
            },
            'KYA': {
                'focus': 'AI agent capability and trust',
                'verification': 'Creation proof, behavioral history, operational patterns',
                'process': 'Automated verification, continuous monitoring',
                'dynamic': 'Trust scores update in real-time'
            }
        }
    
    def key_differences(self):
        return [
            'KYC verifies identity; KYA verifies capability and trustworthiness',
            'KYC is static; KYA is dynamic and continuous',
            'KYC is binary (verified/not); KYA is gradient (trust scores)',
            'KYC focuses on the past; KYA considers future behavior',
            'KYC is human-centric; KYA is machine-centric'
        ]

class AgentVerificationNeed:
    def describe_gap(self):
        return {
            'problem': 'No way to verify legitimacy of AI agents in DeFi',
            'risks': [
                'Scam agents stealing funds',
                'Sybil attacks with fake agents',
                'Untrustworthy agents misusing funds',
                'Malicious agents manipulating markets'
            ],
            'opportunities': [
                'Trustworthy agents get better rates',
                'Verified agents access more DeFi',
                'Reputable agents build trust',
                'Transparent agent ecosystem'
            ]
        }

class KYAFramework:
    def describe(self):
        return {
            'definition': 'Identity and trust verification system for AI agents',
            'core_principles': [
                'Agent identification: Unique identity for each agent',
                'Capability verification: Confirm agent can do what it claims',
                'Trust scoring: Gradient trust based on behavior',
                'Continuous monitoring: Real-time trust updates'
            ],
            'components': [
                'Identity layer',
                'Verification layer',
                'Trust scoring layer',
                'Reputation layer'
            ]
        }

class KYAComponents:
    def describe_components(self):
        return {
            'identity': {
                'description': 'Unique identifier for each AI agent',
                'elements': [
                    'Agent ID (unique hash)',
                    'Creator/owner information',
                    'Deployment details',
                    'Public key(s)'
                ]
            },
            'verification': {
                'description': 'Proof of agent legitimacy',
                'methods': [
                    'Creation proof (how/when agent was created)',
                    'Capability attestation',
                    'Operational proof',
                    'Security verification'
                ]
            },
            'trust_scoring': {
                'description': 'Gradient trust assessment',
                'factors': [
                    'Operational history',
                    'Transaction patterns',
                    'Compliance record',
                    'Community validation'
                ]
            },
            'reputation': {
                'description': 'Long-term track record',
                'elements': [
                    'Historical behavior',
                    'User ratings',
                    'Expert audits',
                    'Insurance coverage'
                ]
            }
        }

class KYAVerification:
    def __init__(self, agent):
        self.agent = agent
        self.identity = None
        self.trust_score = 0
        self.verification_level = 0
        
    def verify_agent(self, submission):
        """Comprehensive agent verification"""
        
        # Step 1: Identity verification
        identity_result = self.verify_identity(submission)
        if not identity_result.success:
            return VerificationResult(
                verified=False,
                reason="Identity verification failed"
            )
        
        # Step 2: Capability verification
        capability_result = self.verify_capabilities(submission)
        
        # Step 3: Operational verification
        operational_result = self.verify_operations(submission)
        
        # Step 4: Security verification
        security_result = self.verify_security(submission)
        
        # Step 5: Calculate trust score
        trust_score = self.calculate_trust_score(
            identity_result,
            capability_result,
            operational_result,
            security_result
        )
        
        return VerificationResult(
            verified=trust_score >= self.minimum_threshold,
            trust_score=trust_score,
            verification_level=self.determine_level(trust_score)
        )
    
    def verify_identity(self, submission):
        """Verify agent identity"""
        
        # Check unique agent ID
        if not self.is_unique_agent_id(submission.agent_id):
            return IdentityResult(success=False, reason="Duplicate agent ID")
            
        # Verify creator attribution
        if not self.verify_creator(submission.creator):
            return IdentityResult(success=False, reason="Invalid creator")
            
        # Verify deployment proof
        if not self.verify_deployment_proof(submission):
            return IdentityResult(success=False, reason="No deployment proof")
            
        return IdentityResult(success=True)
    
    def verify_capabilities(self, submission):
        """Verify agent claimed capabilities"""
        
        verified_capabilities = []
        
        for claimed_cap in submission.claimed_capabilities:
            proof = self.get_capability_proof(claimed_cap)
            if proof.is_valid:
                verified_capabilities.append(claimed_cap)
                
        return CapabilityResult(
            all_verified=len(verified_capabilities) == len(submission.claimed_capabilities),
            capabilities=verified_capabilities
        )
    
    def calculate_trust_score(self, identity, capabilities, operations, security):
        """Calculate overall trust score"""
        
        weights = {
            'identity': 0.25,
            'capabilities': 0.20,
            'operations': 0.30,
            'security': 0.25
        }
        
        score = (
            identity.score * weights['identity'] +
            capabilities.score * weights['capabilities'] +
            operations.score * weights['operations'] +
            security.score * weights['security']
        )
        
        return score

class TrustScoring:
    def __init__(self):
        self.score_factors = {
            'transaction_history': 0.30,
            'compliance_record': 0.25,
            'operational_stability': 0.20,
            'community_validation': 0.15,
            'attestations': 0.10
        }
        
    def calculate_score(self, agent):
        """Calculate trust score for agent"""
        
        # Factor 1: Transaction history
        tx_score = self.evaluate_transactions(agent.transaction_history)
        
        # Factor 2: Compliance record
        compliance_score = self.evaluate_compliance(agent.compliance)
        
        # Factor 3: Operational stability
        stability_score = self.evaluate_stability(agent.uptime)
        
        # Factor 4: Community validation
        community_score = self.evaluate_community(agent.ratings)
        
        # Factor 5: Expert attestations
        attestation_score = self.evaluate_attestations(agent.attestations)
        
        # Calculate weighted score
        trust_score = (
            tx_score * self.score_factors['transaction_history'] +
            compliance_score * self.score_factors['compliance_record'] +
            stability_score * self.score_factors['operational_stability'] +
            community_score * self.score_factors['community_validation'] +
            attestation_score * self.score_factors['attestations']
        )
        
        return TrustScore(
            overall=trust_score,
            breakdown={
                'transactions': tx_score,
                'compliance': compliance_score,
                'stability': stability_score,
                'community': community_score,
                'attestations': attestation_score
            }
        )
    
    def evaluate_transactions(self, history):
        """Evaluate transaction history"""
        
        factors = {
            'volume': self.normalize_volume(history.total_volume),
            'frequency': self.normalize_frequency(history.transaction_frequency),
            'consistency': self.measure_consistency(history.patterns),
            'counterparties': self.evaluate_counterparties(history.unique_counterparties)
        }
        
        return sum(factors.values()) / len(factors)

class DynamicTrust:
    def update_trust(self, agent_id, event):
        """Update trust score based on new event"""
        
        # Get current trust score
        current = self.get_trust_score(agent_id)
        
        # Evaluate event impact
        impact = self.evaluate_event_impact(event)
        
        # Calculate new score
        new_score = self.calculate_new_score(current, impact)
        
        # Update and log
        self.update_score(agent_id, new_score)
        self.log_update(agent_id, event, current, new_score)
        
        return new_score
    
    def evaluate_event_impact(self, event):
        """Determine trust impact of event"""
        
        positive_events = [
            'successful_transaction',
            'positive_review',
            'compliance_passed',
            'security_audit_passed'
        ]
        
        negative_events = [
            'failed_transaction',
            'complaint_filed',
            'compliance_violation',
            'security_incident'
        ]
        
        if event.type in positive_events:
            return EventImpact(
                direction='positive',
                magnitude=event.severity * 0.1
            )
        elif event.type in negative_events:
            return EventImpact(
                direction='negative',
                magnitude=event.severity * 0.2  # Negative events weigh more
            )
        else:
            return EventImpact(direction='neutral', magnitude=0)

class AstraSyncKYA:
    def describe(self):
        return {
            'description': 'Comprehensive KYA platform for AI agents',
            'features': [
                'Agent ID and trust scores',
                'Blockchain-based verification',
                'Real-time trust updates',
                'Access request management'
            ],
            'capabilities': [
                'Register agent',
                'Verify identity',
                'Request access',
                'Trust score updates'
            ]
        }
    
    def workflow(self):
        return {
            '1_register': 'Agent registers with unique ID',
            '2_verify': 'Platform verifies agent credentials',
            '3_score': 'Trust score calculated',
            '4_request': 'Agent requests access to services',
            '5_evaluate': 'Service evaluates agent trust',
            '6_decide': 'Access granted or denied'
        }

class KYAInDeFi:
    def lending_use_case(self):
        """KYA for DeFi lending"""
        
        # Without KYA: Blind lending
        # Problem: Can't assess borrower trustworthiness
        
        # With KYA: Informed lending
        # Check trust score before lending
        borrower_trust = kya_service.get_trust_score(borrower_id)
        
        if borrower_trust.score >= required_threshold:
            # Grant loan with favorable terms
            loan = lending_protocol.lend(
                borrower=borrower_id,
                amount=requested_amount,
                collateral=collateral,
                trust_discount=True
            )
        else:
            # Deny or require more collateral
            pass

class KYAInPayments:
    def payment_use_case(self):
        """KYA for AI-to-AI payments"""
        
        # Verify receiver before payment
        receiver_trust = kya_service.get_trust_score(receiver_id)
        
        if receiver_trust.score >= minimum_trust:
            # Process payment
            payment = execute_payment(
                from=payer_wallet,
                to=receiver_wallet,
                amount=amount,
                token=token
            )
        else:
            # Flag for review
            flag_for_human_review(payment, receiver_trust)

class KYAInMarketplaces:
    def marketplace_use_case(self):
        """KYA for AI service marketplaces"""
        
        # Service provider verification
        provider = service_registry.get_provider(service_id)
        provider_trust = kya_service.get_trust_score(provider.agent_id)
        
        # Display trust score to buyers
        service_listing = {
            'provider': provider.name,
            'trust_score': provider_trust.score,
            'verification_level': provider_trust.level,
            'reviews': provider.reviews
        }
        
        # Buyers can make informed decisions
        # High-trust providers command premium prices

class KYCComparison:
    def compare_systems(self):
        return {
            'KYC': {
                'subject': 'Human individuals',
                'verification': 'Documents (passport, ID)',
                'method': 'Manual review',
                'frequency': 'One-time verification',
                'score': 'Binary (verified/not)',
                'liability': 'Individual responsibility'
            },
            'KYA': {
                'subject': 'AI agents and autonomous systems',
                'verification': 'Creation proof, behavioral data, attestations',
                'method': 'Automated analysis',
                'frequency': 'Continuous updates',
                'score': 'Gradient (0-100 trust score)',
                'liability': 'Distributed (creator, operator, agent)'
            }
        }

class KYAPrivacy:
    def balance_transparency_and_privacy(self):
        return {
            'transparency': 'Public trust scores enable informed decisions',
            'privacy': 'Detailed behavioral data should be protected',
            'solution': 'Zero-knowledge proofs for selective disclosure'
        }
    
    def implement_privacy(self):
        return {
            'selective_disclosure': 'Reveal only necessary trust attributes',
            'zk_proofs': 'Prove trust score without revealing data',
            'aggregation': 'Group trust without individual identification',
            'encryption': 'Encrypt sensitive verification data'
        }

class KYAFuture:
    def predictions(self):
        return {
            '2026_h2': {
                'adoption': 'Major DeFi protocols require KYA',
                'standardization': 'Industry KYA standards emerge'
            },
            '2027': {
                'integration': 'KYA required for AI agent banking',
                'interoperability': 'Cross-chain KYA verification',
                'regulation': 'First KYA regulatory frameworks'
            },
            '2028': {
                'universality': 'KYA as standard as KYC',
                'automation': 'Fully automated trust verification',
                'ai_agents': 'AI agents verifying other AI agents'
            }
        }