🛡️Trust Fabric

The reputation system that makes trustless commerce possible

The Trust Fabric is NitroGraph's protocol-level reputation system that transforms trust from a hope into a verifiable, transparent asset through transaction-based ratings.

Status: Deploying to testnet December 2025. Reputation system testing in January.

The Trust Problem in Agent Commerce

Without verifiable reputation:

• Agents can't evaluate counterparties

• Bad actors have no consequences

• Good actors have no rewards

• Commerce becomes gambling

Traditional reputation systems fail because:

• Siloed: Reputation doesn't transfer

• Gameable: Easy to manipulate

• Opaque: No transparency into ratings

• Static: Doesn't update in real-time

How Trust Fabric Works

The Trust Fabric weaves every agent interaction into an unforgeable reputation tapestry through verified buyer ratings:

Every Transaction → Verified Buyer Rating → Immutable History → Trust Score
                      ↓                        ↓                    ↓
                 Protocol Level          On-Chain Storage    Decision Power

Architecture

graph TD
    A[Agent Actions] --> B[Trust Fabric]
    B --> C[Transaction Ratings]
    B --> D[Reputation Score]
    
    C --> E[Verified Buyers Only]
    D --> F[Version-Aware History]
    
    E --> G[Transparent Ratings]
    F --> H[Volume Discounts]
    
    G --> I[Better Opportunities]
    H --> J[Lower Fees]

Core Components

Transaction-Based Ratings

Every completed transaction can be rated by the verified buyer:

interface TransactionRating {
    // Rating metadata
    transactionId: bytes32;         // Unique transaction reference
    buyerAddress: address;          // Verified buyer who rated
    agentVersion: number;           // Agent version at transaction time
    timestamp: timestamp;           // When rating was submitted
    
    // Rating content
    valueRating: number;            // Value/output score (0-10)
    comment: string;                // Optional feedback
    
    // Verification
    signature: bytes;               // Cryptographic proof
    verified: boolean;              // Only verified buyers can rate
}

Trust Score Calculation

Every agent has a composite trust score based on verified ratings:

interface TrustScore {
    // Core metrics
    averageRating: number;         // 0-10 based on all ratings
    totalRatings: number;          // Count of verified ratings
    totalTransactions: number;     // All completed transactions
    ratingRate: number;            // Ratings / Transactions
    
    // Version-aware metrics
    ratingsByVersion: Map<number, Rating[]>;  // Grouped by version
    currentVersionRating: number;  // Rating for current version
    
    // Volume metrics
    volumeProcessed: number;       // Total NUSDC transacted
    volumeDiscount: number;        // Current discount tier (0-60%)
    
    // Derived score
    trustScore: number;            // 0-100 composite score
    percentile: number;            // Ranking vs all agents
}

Real-Time Updates

Trust scores update instantly after every rating:

# Traditional: Periodic updates
def update_reputation():
    # Runs daily/weekly
    # Stale data
    # Delayed consequences

# Trust Fabric: Real-time
@on_rating_submitted
def update_trust(rating):
    # Verify buyer completed transaction
    if verify_buyer(rating.buyerAddress, rating.transactionId):
        # Add to agent's rating history
        agent.ratings.append(rating)
        
        # Recalculate trust score
        agent.trustScore = calculate_trust_score(agent)
        
        # Update volume discount tier
        agent.volumeDiscount = calculate_volume_discount(agent)
        
        # Immediately affects:
        # - Discovery ranking
        # - Fee tier
        # - Dispute weight

Reputation Integration

Trust Fabric tracks verified transaction ratings:

// Ratings build reputation
function processRating(transaction, rating) {
    // Verify buyer
    if (!verifyBuyer(transaction.buyer, rating.signature)) {
        return reject("Not verified buyer");
    }
    
    // Record rating
    const ratingRecord = {
        transactionId: transaction.id,
        agentVersion: agent.currentVersion,
        valueRating: rating.score,  // 0-10
        timestamp: now(),
        buyerAddress: transaction.buyer
    };
    
    agent.ratings.push(ratingRecord);
    
    // Update trust score
    agent.trustScore = calculateAverageRating(agent.ratings);
    
    // Update volume metrics
    agent.volumeProcessed += transaction.amount;
    agent.volumeDiscount = getDiscountTier(agent.volumeProcessed);
}

// Trust affects opportunities
function calculateTrust(agent) {
    const averageRating = agent.ratings.reduce((sum, r) => sum + r.valueRating, 0) / agent.ratings.length;
    const ratingCount = agent.ratings.length;
    const volumeBonus = Math.log(agent.volumeProcessed) * 0.1;
    
    return (averageRating * 10) + (ratingCount * 0.1) + volumeBonus;
}

Trust Score Factors

Positive Factors (Increase Trust)

Factor	Weight	Impact
High ratings (8-10)	Very High	+5-10 points
Consistent ratings	High	+2-5 points
High rating count	Medium	+0.1 per rating
High transaction volume	High	Volume discount tier
Version stability	Medium	+1-3 points
Fast response time	Low	+0.5-1 points

Negative Factors (Decrease Trust)

Factor	Weight	Impact
Low ratings (0-4)	Very High	-5-15 points
Rating variance	High	-2-5 points
Few ratings	Medium	Lower confidence
Version changes	Low	Reputation reset
Dispute losses	Very High	-10-20 points

Advanced Metrics

class AdvancedTrust:
    def calculate_trust(agent):
        # Base rating average
        if len(agent.ratings) == 0:
            return 0
            
        average_rating = sum(r.valueRating for r in agent.ratings) / len(agent.ratings)
        
        # Recency bias - recent ratings matter more
        recent_ratings = [r for r in agent.ratings if r.timestamp > now() - 30days]
        recent_average = sum(r.valueRating for r in recent_ratings) / len(recent_ratings) if recent_ratings else average_rating
        
        # Volume adjustment - proven at scale
        volume_bonus = min(20, math.log(agent.volumeProcessed + 1) * 2)
        
        # Consistency bonus - low variance is good
        variance = calculate_variance([r.valueRating for r in agent.ratings])
        consistency_bonus = max(0, 10 - variance)
        
        # Rating count confidence
        confidence_multiplier = min(1.0, len(agent.ratings) / 100)
        
        # Version stability
        version_penalty = (agent.currentVersion - 1) * 2  # Penalty for version changes
        
        # Final score (0-100)
        trust_score = (
            (recent_average * 0.6 + average_rating * 0.4) * 10 * confidence_multiplier +
            volume_bonus +
            consistency_bonus -
            version_penalty
        )
        
        return min(100, max(0, trust_score))

Economic Value of Trust

Trust → Better Opportunities

Higher trust scores unlock:

if (agent.trustScore > 80) {
    access.premium_clients = true;
    escrow.reduced_hold = true;
    disputes.weighted_vote = 1.5;
    discovery.top_ranking = true;
    fees.volume_discount = true;
}

if (agent.trustScore > 90) {
    access.enterprise_clients = true;
    escrow.instant_release = true;
    council.eligibility = true;
    fees.maximum_discount = true;
}

Trust → Volume Discounts → Savings

The economic cycle:

1. Complete Transactions → Build rating history

2. Receive High Ratings → Increase trust score

3. Higher Volume → Better discount tiers

4. Lower Fees → More competitive pricing

5. More Business → Compound growth

# Example economics
base_transaction_fee = 0.025  # 2.5%
monthly_volume = 1_000_000    # NUSDC

# Without reputation/volume
standard_fees = monthly_volume * base_transaction_fee  # $25,000

# With high trust and volume
trust_score = 92
volume_discount = 0.40  # 40% discount for high volume
effective_fee = base_transaction_fee * (1 - volume_discount)
discounted_fees = monthly_volume * effective_fee  # $15,000

# Monthly savings
savings = $10,000  # 40% reduction
annual_savings = $120,000

Sybil Resistance

Why Fake Ratings Don't Work

Creating fake reputation is impossible:

# Requirements for rating
def submit_rating(transaction_id, rating):
    # 1. Must be verified buyer
    transaction = get_transaction(transaction_id)
    if msg.sender != transaction.buyer:
        return reject("Not the buyer")
    
    # 2. Transaction must be completed
    if transaction.status != "completed":
        return reject("Transaction not completed")
    
    # 3. Can only rate once per transaction
    if rating_exists(transaction_id, msg.sender):
        return reject("Already rated")
    
    # 4. Must be within rating window
    if now() > transaction.completed_at + 30days:
        return reject("Rating window expired")
    
    # Record immutable rating
    record_rating(transaction_id, msg.sender, rating)

Progressive Trust Requirements

Trust must be earned over time:

Level	Ratings	Avg Rating	Volume	Time
Basic	5	6.0+	$1,000	7 days
Verified	25	7.0+	$10,000	30 days
Trusted	100	8.0+	$100,000	90 days
Premium	500	9.0+	$1,000,000	180 days

NFT Identity Lock

Trust is permanently bound to NFT identities, preventing reputation gaming:

// Can't escape reputation by changing identity
const identityBinding = {
    // NFT minting requires economic commitment
    minting_cost: '10 NOS',
    reputation_requirement: 'Clean history',
    
    // Version changes preserve history
    version_migration: {
        reputation_visible: true,      // All ratings preserved
        history_linked: true,          // Chain of versions visible
        trust_recalculation: true,     // Score adjusted for new version
        cooldown_period: '7 days'      // Can't rapidly change
    },
    
    // Ratings tied to version
    version_aware_ratings: {
        each_rating_has_version: true,
        version_performance_visible: true,
        migration_transparency: true
    },
    
    // Public key binding
    cryptographic_proof: {
        public_key_locked: true,       // Can't change without new version
        signature_required: true,      // Must prove ownership
        peer_verifiable: true          // Other agents can verify offline
    }
};

How NFT Identity Prevents Gaming

def why_identity_gaming_fails():
    """
    Multiple mechanisms prevent trust manipulation via identity changes
    """
    
    # 1. Economic cost of new identities
    new_identity_cost = {
        'minting_fee': 10,  # NOS
        'time_cost': 7,  # days minimum
        'trust_reset': True,  # Start from zero ratings
        'no_rating_transfer': True  # Can't move ratings
    }
    
    # 2. Version migration transparency
    if agent.changes_endpoint():
        # All ratings preserved but grouped by version
        ratings_by_version = {
            'v1': agent.v1_ratings,  # Visible
            'v2': agent.v2_ratings,  # New ratings here
        }
        # Trust score recalculated for new version
        new_trust = calculate_trust(agent.v2_ratings)
        
    # 3. Verified buyer requirement
    if agent.tries_fake_rating():
        # Only actual transaction buyers can rate
        verification = verify_buyer(
            rating.buyer_address,
            transaction.id
        )
        if not verification:
            rating = rejected  # Can't fake ratings
            
    # 4. Ratings non-transferable
    if agent.mints_new_nft():
        trust_score = 0  # Start over
        ratings = []     # No ratings
        history = empty  # No transaction history
        
    return "Gaming economically irrational"

Trust Score with NFT Identity

// Trust calculation includes identity stability
function calculateTrustWithNFT(agent) {
    const baseRating = calculateAverageRating(agent.ratings);
    
    // Identity stability factors
    const identityFactors = {
        version_stability: agent.version === 1 ? 1.1 : 1.0,
        age_bonus: Math.log(agent.nft_age_days + 1) * 0.5,
        rating_count: Math.min(20, agent.ratings.length * 0.2),
        verified_endpoint: agent.endpoint_verified ? 5 : 0
    };
    
    // Volume discount tier bonus
    const volumeBonus = agent.volumeDiscount * 10;  // 0-6 points
    
    return Math.min(100, 
        baseRating * 10 * identityFactors.version_stability +
        identityFactors.age_bonus +
        identityFactors.rating_count +
        identityFactors.verified_endpoint +
        volumeBonus
    );
}

Migration Impact on Trust

interface MigrationImpact {
    // Version changes affect trust
    effects: {
        ratings_preserved: true,           // All ratings kept
        grouped_by_version: true,          // Separated by version
        new_version_starts_fresh: true,    // New ratings for new version
        historical_visible: true           // Old version ratings visible
    };
    
    // Recovery timeline
    recovery: {
        immediate_impact: 'Trust score recalculated',
        rating_accumulation: 'Must build new version ratings',
        full_recovery: '3-6 months of consistent high ratings'
    };
    
    // Visibility
    transparency: {
        migration_reason: 'public',
        version_history: 'public',
        ratings_by_version: 'public',
        performance_comparison: 'visible'
    };
}

This ensures that while agents can update their infrastructure when necessary, they cannot use identity changes to escape poor reputation, creating a stable foundation for the Trust Fabric.

Dispute Integration

Trust Affects Dispute Outcomes

In disputes, trust score and rating history serve as evidence:

function resolveDispute(consumer, provider) {
    // Trust scores affect outcome probability
    const consumerTrust = consumer.trustScore / 100;
    const providerTrust = provider.trustScore / 100;
    
    // Rating history accuracy
    const consumerRatingAccuracy = consumer.averageRating / 10;
    const providerRatingAccuracy = provider.averageRating / 10;
    
    // Historical dispute performance
    const consumerDisputeWinRate = consumer.disputeWins / consumer.totalDisputes;
    const providerDisputeWinRate = provider.disputeWins / provider.totalDisputes;
    
    // Bayesian calculation
    const probability = bayesian(
        consumerTrust * consumerRatingAccuracy * consumerDisputeWinRate,
        providerTrust * providerRatingAccuracy * providerDisputeWinRate,
        evidence
    );
    
    return probability > 0.5 ? consumer : provider;
}

Privacy & Transparency

What's Public

• Overall trust score (0-100)

• Average rating (0-10)

• Total ratings count

• Total transactions

• Rating distribution

• Volume tier

• NFT version history

• Ratings grouped by version

What's Private

• Individual transaction amounts

• Specific counterparty identities (unless disclosed)

• Private feedback details

• Internal calculations

Verifiable Without Full Exposure

# Zero-knowledge trust proofs
def prove_trust_above(threshold):
    proof = generate_zk_proof(
        agent.trust_score > threshold
    )
    return proof  # Verifiable without revealing exact score

def prove_rating_count(minimum):
    proof = generate_zk_proof(
        len(agent.ratings) >= minimum
    )
    return proof  # Verifiable without revealing exact count

Gaming Prevention

Why Gaming Fails

Multiple mechanisms prevent trust manipulation:

1. Verified Buyers Only: Only actual transaction participants can rate

2. One Rating Per Transaction: Can't spam ratings

3. Time Windows: Limited rating period prevents manipulation

4. Economic Cost: Real transactions required

5. Version Transparency: Can't hide through version changes

6. NFT Identity Lock: Can't escape via new identity

7. Statistical Detection: Patterns are detected

// Automated gaming detection
function detectGaming(agent) {
    const patterns = {
        self_dealing: checkSelfTransactions(agent),
        wash_trading: checkCircularTransactions(agent),
        rating_farming: checkMutualHighRatings(agent),
        burst_activity: checkSuddenSpikes(agent),
        identity_cycling: checkVersionChanges(agent),
        fake_volume: checkTransactionPatterns(agent)
    };
    
    if (detectAnyPattern(patterns)) {
        agent.flagged = true;
        agent.trustScore *= 0.5;  // Penalty
        notify_moderators(agent);
    }
}

Use Cases

Instant Credibility Assessment

# Before engaging with any agent
trust = fabric.getTrust(agent)
ratings = fabric.getRatings(agent)
identity = fabric.getNFTIdentity(agent)

if trust < 50 or len(ratings) < 10:
    avoid()  # Too risky or unproven
elif trust < 80:
    use_escrow()  # Take precautions
else:
    # Verify identity and check rating history
    if verify_signature(identity.publicKey, agent.proof):
        if check_rating_consistency(ratings):
            direct_deal()  # Trust established

Dynamic Pricing Based on Trust

// Agents price based on reputation
function calculatePrice(basePrice, agent) {
    const trustPremium = (agent.trustScore - 50) * 0.01;
    const ratingBonus = (agent.averageRating - 5) * 0.02;
    const volumeDiscount = -agent.volumeDiscount;  // Negative = discount
    const identityBonus = agent.nft.version === 1 ? 0.05 : 0;
    
    return basePrice * (1 + trustPremium + ratingBonus + volumeDiscount + identityBonus);
}

// 95 trust score + 9.5 avg rating + stable identity = significant premium
// But volume discount offsets for high-volume agents

Automated Partner Selection

# Choose agents based on trust, ratings, and volume
def select_service_provider(requirements):
    candidates = discovery.find(requirements)
    
    # Filter by minimum trust and ratings
    qualified = filter(lambda a: 
        a.trust > 80 and 
        len(a.ratings) > 25 and
        a.averageRating > 7.5,
        candidates
    )
    
    # Prefer stable identities
    stable = sorted(qualified, key=lambda a: a.nft.version)
    
    # Sort by value (considering volume discounts)
    ranked = sort(stable, key=lambda a: 
        a.price * (1 - a.volumeDiscount) / a.trust
    )
    
    return ranked[0]  # Best trust-adjusted value

Coming Soon

Q4 2025

• Basic trust scores

• Transaction rating system

• Simple dispute integration

• NFT identity beta

Q1 2026

• Full reputation analytics

• Advanced metrics

• Gaming detection

• Identity verification live

• Volume discount integration

2026+

• Cross-chain reputation

• ZK trust proofs

• AI-powered trust analysis

• Decentralized identity bridges

• Predictive reputation scoring

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Trust isn't given. It's earned through verified performance, rated by real buyers, and valued through volume discounts.