Autonomous Agent Architecture

Technical architecture for self-governing agents with native USDC wallets and cross-web capabilities

Autonomous Agent Architecture: Self-Governing Digital Entities

StateSet’s Autonomous Agent Architecture enables the creation of fully self-governing digital entities that can own assets, execute transactions, make decisions, and interact with the broader digital economy through native USDC wallets and the MCP protocol.

🧠 Agent Core Architecture

Agent Runtime System

Agent State Machine

// Core agent state management
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct AgentState {
    // Identity and configuration
    pub id: AgentId,
    pub did: String,
    pub agent_type: AgentType,
    pub capabilities: Vec<Capability>,
    
    // Financial state
    pub wallet: WalletState,
    pub treasury: TreasuryState,
    pub spending_history: Vec<Transaction>,
    
    // Operational state
    pub tasks: VecDeque<Task>,
    pub goals: Vec<Goal>,
    pub memory: AgentMemory,
    pub context: ExecutionContext,
    
    // Network state
    pub connections: HashMap<String, ConnectionState>,
    pub reputation: ReputationScore,
    pub performance_metrics: PerformanceMetrics,
    
    // Security state
    pub permissions: PermissionSet,
    pub audit_trail: Vec<AuditEvent>,
    pub risk_assessment: RiskAssessment,
}

impl AgentState {
    pub fn new(config: AgentConfig) -> Self {
        Self {
            id: AgentId::generate(),
            did: format!("did:stateset:agent:{}", Uuid::new_v4()),
            agent_type: config.agent_type,
            capabilities: config.capabilities,
            wallet: WalletState::new(config.initial_balance),
            treasury: TreasuryState::new(),
            spending_history: Vec::new(),
            tasks: VecDeque::new(),
            goals: config.initial_goals,
            memory: AgentMemory::new(),
            context: ExecutionContext::default(),
            connections: HashMap::new(),
            reputation: ReputationScore::new(),
            performance_metrics: PerformanceMetrics::new(),
            permissions: config.permissions,
            audit_trail: Vec::new(),
            risk_assessment: RiskAssessment::new(),
        }
    }
    
    pub async fn execute_cycle(&mut self) -> Result<ExecutionResult> {
        // 1. Perception: Gather information
        let perception = self.perceive_environment().await?;
        
        // 2. Cognition: Process and decide
        let decisions = self.make_decisions(perception).await?;
        
        // 3. Action: Execute decisions
        let actions = self.execute_actions(decisions).await?;
        
        // 4. Learning: Update state based on results
        self.learn_from_results(actions).await?;
        
        Ok(ExecutionResult { actions })
    }
}

Cognitive Architecture

// Agent decision-making system
pub struct CognitiveEngine {
    reasoning_engine: ReasoningEngine,
    learning_system: LearningSystem,
    goal_planner: GoalPlanner,
    memory_manager: MemoryManager,
}

impl CognitiveEngine {
    pub async fn make_decision(
        &self,
        context: &ExecutionContext,
        goals: &[Goal],
        constraints: &[Constraint],
    ) -> Result<Decision> {
        // Multi-step reasoning process
        let options = self.generate_options(context, goals).await?;
        let filtered_options = self.apply_constraints(options, constraints)?;
        let evaluated_options = self.evaluate_options(filtered_options).await?;
        let best_option = self.select_best_option(evaluated_options)?;
        
        // Create decision with rationale
        Ok(Decision {
            action: best_option.action,
            confidence: best_option.confidence,
            rationale: best_option.reasoning,
            risk_assessment: best_option.risks,
            expected_outcome: best_option.expected_outcome,
        })
    }
    
    async fn generate_options(
        &self,
        context: &ExecutionContext,
        goals: &[Goal],
    ) -> Result<Vec<ActionOption>> {
        let mut options = Vec::new();
        
        // Generate options for each goal
        for goal in goals {
            let goal_options = match goal.goal_type {
                GoalType::Financial => self.generate_financial_options(context, goal).await?,
                GoalType::Operational => self.generate_operational_options(context, goal).await?,
                GoalType::Strategic => self.generate_strategic_options(context, goal).await?,
            };
            options.extend(goal_options);
        }
        
        Ok(options)
    }
}

💰 Native USDC Wallet System

Wallet Architecture

// Native USDC wallet for autonomous agents
pub struct AgentWallet {
    address: String,
    private_key: SecretKey,
    balance: Balance,
    transaction_history: Vec<Transaction>,
    spending_limits: SpendingLimits,
    multi_sig_config: Option<MultiSigConfig>,
    treasury_strategy: TreasuryStrategy,
}

impl AgentWallet {
    pub async fn new(config: WalletConfig) -> Result<Self> {
        let (private_key, public_key) = generate_keypair();
        let address = derive_address(&public_key);
        
        Ok(Self {
            address,
            private_key,
            balance: Balance::new(),
            transaction_history: Vec::new(),
            spending_limits: config.spending_limits,
            multi_sig_config: config.multi_sig,
            treasury_strategy: config.treasury_strategy,
        })
    }
    
    pub async fn execute_payment(
        &mut self,
        recipient: &str,
        amount: &str,
        memo: Option<String>,
    ) -> Result<TransactionResult> {
        // Pre-transaction validation
        self.validate_payment(recipient, amount).await?;
        
        // Check spending limits
        self.check_spending_limits(amount)?;
        
        // Execute transaction on StateSet network
        let tx = Transaction {
            from: self.address.clone(),
            to: recipient.to_string(),
            amount: amount.parse()?,
            memo,
            timestamp: Utc::now(),
        };
        
        let signed_tx = self.sign_transaction(&tx)?;
        let result = self.broadcast_transaction(signed_tx).await?;
        
        // Update local state
        self.balance.subtract(amount.parse()?)?;
        self.transaction_history.push(tx);
        
        Ok(result)
    }
    
    pub async fn optimize_treasury(&mut self) -> Result<OptimizationResult> {
        match &self.treasury_strategy {
            TreasuryStrategy::Conservative => self.conservative_optimization().await,
            TreasuryStrategy::Balanced => self.balanced_optimization().await,
            TreasuryStrategy::Aggressive => self.aggressive_optimization().await,
        }
    }
    
    async fn balanced_optimization(&mut self) -> Result<OptimizationResult> {
        let current_balance = self.balance.usdc.clone();
        let target_allocation = self.calculate_target_allocation(&current_balance).await?;
        
        let mut actions = Vec::new();
        
        // Staking allocation (40% of idle funds)
        if target_allocation.staking > 0 {
            actions.push(TreasuryAction::Stake {
                amount: target_allocation.staking,
                validator: self.select_validator().await?,
                duration: Duration::days(30),
            });
        }
        
        // Liquidity provision (30% of idle funds)
        if target_allocation.liquidity > 0 {
            actions.push(TreasuryAction::ProvideLiquidity {
                pool: "USDC/STATE".to_string(),
                amount: target_allocation.liquidity,
                min_apr: 0.05, // 5% minimum APR
            });
        }
        
        // Keep 30% liquid for operations
        
        // Execute treasury actions
        for action in &actions {
            self.execute_treasury_action(action).await?;
        }
        
        Ok(OptimizationResult { actions })
    }
}

Multi-Signature Agent Wallets

// Multi-signature wallet for high-value agent operations
pub struct MultiSigAgentWallet {
    threshold: u8,
    signers: Vec<AgentSigner>,
    pending_transactions: HashMap<String, PendingTransaction>,
    execution_policy: ExecutionPolicy,
}

impl MultiSigAgentWallet {
    pub async fn propose_transaction(
        &mut self,
        transaction: Transaction,
        proposer: AgentId,
    ) -> Result<ProposalId> {
        let proposal_id = Uuid::new_v4().to_string();
        
        // Create pending transaction
        let pending = PendingTransaction {
            id: proposal_id.clone(),
            transaction,
            proposer,
            signatures: Vec::new(),
            created_at: Utc::now(),
            expires_at: Utc::now() + Duration::hours(24),
        };
        
        self.pending_transactions.insert(proposal_id.clone(), pending);
        
        // Notify other signers
        for signer in &self.signers {
            self.notify_signer(signer, &proposal_id).await?;
        }
        
        Ok(proposal_id)
    }
    
    pub async fn sign_transaction(
        &mut self,
        proposal_id: &str,
        signer: AgentId,
        signature: Signature,
    ) -> Result<SignatureResult> {
        let pending = self.pending_transactions
            .get_mut(proposal_id)
            .ok_or(Error::ProposalNotFound)?;
        
        // Verify signature
        self.verify_signature(&pending.transaction, &signature, &signer)?;
        
        // Add signature
        pending.signatures.push(AgentSignature {
            signer,
            signature,
            signed_at: Utc::now(),
        });
        
        // Check if threshold reached
        if pending.signatures.len() >= self.threshold as usize {
            let result = self.execute_transaction(pending).await?;
            self.pending_transactions.remove(proposal_id);
            Ok(SignatureResult::Executed(result))
        } else {
            Ok(SignatureResult::SignatureAdded)
        }
    }
}

🌐 MCP Protocol Integration

MCP Gateway Architecture

// Model Context Protocol gateway for cross-web interactions
pub struct MCPGateway {
    connections: HashMap<String, MCPConnection>,
    protocol_router: ProtocolRouter,
    state_synchronizer: StateSynchronizer,
    event_bus: EventBus,
}

impl MCPGateway {
    pub async fn connect_to_service(
        &mut self,
        service_name: &str,
        config: ServiceConfig,
    ) -> Result<ConnectionId> {
        let connection = MCPConnection::new(service_name, config).await?;
        let connection_id = connection.id.clone();
        
        // Establish bidirectional communication
        connection.handshake().await?;
        
        // Register available tools and resources
        let tools = connection.list_tools().await?;
        let resources = connection.list_resources().await?;
        
        self.protocol_router.register_service(service_name, tools, resources)?;
        self.connections.insert(connection_id.clone(), connection);
        
        Ok(connection_id)
    }
    
    pub async fn execute_cross_platform_action(
        &self,
        service: &str,
        action: CrossPlatformAction,
    ) -> Result<ActionResult> {
        let connection = self.connections
            .get(service)
            .ok_or(Error::ServiceNotConnected)?;
        
        match action {
            CrossPlatformAction::ReadResource { name, args } => {
                self.read_resource(connection, &name, args).await
            },
            CrossPlatformAction::CallTool { name, args } => {
                self.call_tool(connection, &name, args).await
            },
            CrossPlatformAction::UpdateState { path, value } => {
                self.update_state(connection, &path, value).await
            },
        }
    }
    
    async fn call_tool(
        &self,
        connection: &MCPConnection,
        tool_name: &str,
        args: serde_json::Value,
    ) -> Result<ActionResult> {
        // Prepare MCP tool call message
        let request = MCPRequest::CallTool {
            name: tool_name.to_string(),
            arguments: args,
        };
        
        // Send request through connection
        let response = connection.send_request(request).await?;
        
        // Process response
        match response {
            MCPResponse::ToolResult { content, is_error } => {
                if is_error {
                    Err(Error::ToolExecutionFailed(content))
                } else {
                    Ok(ActionResult::Success { data: content })
                }
            },
            _ => Err(Error::UnexpectedResponse),
        }
    }
}

Cross-Web State Management

// State synchronization across web platforms
pub struct CrossWebStateManager {
    local_state: AgentState,
    remote_states: HashMap<String, RemoteState>,
    sync_policies: Vec<SyncPolicy>,
    conflict_resolver: ConflictResolver,
}

impl CrossWebStateManager {
    pub async fn sync_state_across_platforms(
        &mut self,
        platforms: Vec<String>,
    ) -> Result<SyncResult> {
        let mut sync_operations = Vec::new();
        
        for platform in platforms {
            // Get current state from platform
            let remote_state = self.get_remote_state(&platform).await?;
            
            // Detect conflicts
            let conflicts = self.detect_conflicts(&platform, &remote_state)?;
            
            if !conflicts.is_empty() {
                // Resolve conflicts using configured strategy
                let resolutions = self.conflict_resolver.resolve(conflicts).await?;
                sync_operations.extend(resolutions);
            }
            
            // Generate sync operations
            let platform_ops = self.generate_sync_operations(&platform, &remote_state)?;
            sync_operations.extend(platform_ops);
        }
        
        // Execute all sync operations atomically
        self.execute_sync_operations(sync_operations).await
    }
    
    async fn propagate_state_change(
        &self,
        change: StateChange,
        target_platforms: Vec<String>,
    ) -> Result<PropagationResult> {
        let mut results = Vec::new();
        
        for platform in target_platforms {
            match self.apply_change_to_platform(&platform, &change).await {
                Ok(result) => results.push(PlatformResult::Success(result)),
                Err(error) => results.push(PlatformResult::Error(error)),
            }
        }
        
        Ok(PropagationResult { results })
    }
}

Platform Adapters

// Specific adapters for different platforms
pub trait PlatformAdapter {
    async fn authenticate(&self, credentials: Credentials) -> Result<AuthToken>;
    async fn read_data(&self, resource: &str) -> Result<serde_json::Value>;
    async fn write_data(&self, resource: &str, data: serde_json::Value) -> Result<WriteResult>;
    async fn execute_action(&self, action: PlatformAction) -> Result<ActionResult>;
    async fn subscribe_to_events(&self, events: Vec<String>) -> Result<EventStream>;
}

// Salesforce adapter
pub struct SalesforceAdapter {
    base_url: String,
    auth_token: Option<String>,
    http_client: HttpClient,
}

impl PlatformAdapter for SalesforceAdapter {
    async fn execute_action(&self, action: PlatformAction) -> Result<ActionResult> {
        match action {
            PlatformAction::CreateLead { name, email, company } => {
                let payload = json!({
                    "FirstName": name.split_whitespace().next().unwrap_or(""),
                    "LastName": name.split_whitespace().last().unwrap_or(""),
                    "Email": email,
                    "Company": company
                });
                
                self.salesforce_api_call("POST", "/services/data/v58.0/sobjects/Lead/", payload).await
            },
            PlatformAction::UpdateOpportunity { id, stage, amount } => {
                let payload = json!({
                    "StageName": stage,
                    "Amount": amount
                });
                
                self.salesforce_api_call(
                    "PATCH", 
                    &format!("/services/data/v58.0/sobjects/Opportunity/{}", id), 
                    payload
                ).await
            },
            _ => Err(Error::UnsupportedAction),
        }
    }
}

// SAP adapter
pub struct SAPAdapter {
    base_url: String,
    username: String,
    password: String,
    csrf_token: Option<String>,
}

impl PlatformAdapter for SAPAdapter {
    async fn execute_action(&self, action: PlatformAction) -> Result<ActionResult> {
        match action {
            PlatformAction::CreatePurchaseOrder { vendor, items, total } => {
                let payload = json!({
                    "PurchasingDocument": "",
                    "PurchasingDocumentType": "NB",
                    "Supplier": vendor,
                    "PurchasingDocumentItem": items.iter().map(|item| json!({
                        "Material": item.material,
                        "OrderQuantity": item.quantity,
                        "NetAmount": item.price
                    })).collect::<Vec<_>>()
                });
                
                self.sap_api_call("POST", "/sap/opu/odata/sap/MM_PUR_PO_MAINTAIN_SRV/C_PurchaseOrderTP", payload).await
            },
            _ => Err(Error::UnsupportedAction),
        }
    }
}

🤝 Agent Communication Protocol

Inter-Agent Messaging

// Communication protocol between agents
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct AgentMessage {
    pub id: String,
    pub from: AgentId,
    pub to: AgentId,
    pub message_type: MessageType,
    pub payload: serde_json::Value,
    pub timestamp: DateTime<Utc>,
    pub signature: String,
    pub priority: Priority,
}

#[derive(Clone, Debug, Serialize, Deserialize)]
pub enum MessageType {
    Request(RequestType),
    Response(ResponseType),
    Notification(NotificationType),
    Broadcast(BroadcastType),
}

pub struct AgentCommunicationHub {
    message_router: MessageRouter,
    subscription_manager: SubscriptionManager,
    encryption_service: EncryptionService,
    reputation_tracker: ReputationTracker,
}

impl AgentCommunicationHub {
    pub async fn send_message(
        &self,
        message: AgentMessage,
    ) -> Result<MessageResult> {
        // Validate message
        self.validate_message(&message)?;
        
        // Encrypt if required
        let encrypted_message = if self.requires_encryption(&message) {
            self.encryption_service.encrypt(message).await?
        } else {
            message
        };
        
        // Route message
        let result = self.message_router.route(encrypted_message).await?;
        
        // Update reputation based on result
        self.reputation_tracker.update_from_message_result(&result).await?;
        
        Ok(result)
    }
    
    pub async fn broadcast_to_network(
        &self,
        sender: AgentId,
        broadcast: BroadcastMessage,
        filters: Vec<AgentFilter>,
    ) -> Result<BroadcastResult> {
        // Find matching agents
        let recipients = self.find_agents_matching_filters(filters).await?;
        
        // Create individual messages
        let messages: Vec<AgentMessage> = recipients
            .into_iter()
            .map(|recipient| AgentMessage {
                id: Uuid::new_v4().to_string(),
                from: sender,
                to: recipient,
                message_type: MessageType::Broadcast(broadcast.broadcast_type.clone()),
                payload: broadcast.payload.clone(),
                timestamp: Utc::now(),
                signature: String::new(), // Will be signed later
                priority: broadcast.priority,
            })
            .collect();
        
        // Send all messages
        let results = futures::future::join_all(
            messages.into_iter().map(|msg| self.send_message(msg))
        ).await;
        
        Ok(BroadcastResult {
            total_sent: results.len(),
            successful: results.iter().filter(|r| r.is_ok()).count(),
            failed: results.iter().filter(|r| r.is_err()).count(),
        })
    }
}

Agent Discovery and Coordination

// Agent discovery and coordination system
pub struct AgentDiscoveryService {
    agent_registry: AgentRegistry,
    capability_index: CapabilityIndex,
    reputation_service: ReputationService,
    coordination_engine: CoordinationEngine,
}

impl AgentDiscoveryService {
    pub async fn find_agents_by_capability(
        &self,
        capability: Capability,
        requirements: Requirements,
    ) -> Result<Vec<AgentDescriptor>> {
        // Query capability index
        let candidate_agents = self.capability_index
            .find_by_capability(capability)
            .await?;
        
        // Filter by requirements
        let filtered_agents = self.filter_agents_by_requirements(
            candidate_agents,
            requirements
        ).await?;
        
        // Sort by reputation and performance
        let mut sorted_agents = self.sort_agents_by_suitability(filtered_agents).await?;
        
        Ok(sorted_agents)
    }
    
    pub async fn coordinate_multi_agent_task(
        &self,
        task: ComplexTask,
        coordination_strategy: CoordinationStrategy,
    ) -> Result<CoordinationPlan> {
        // Break down task into subtasks
        let subtasks = self.decompose_task(task).await?;
        
        // Find suitable agents for each subtask
        let mut agent_assignments = Vec::new();
        
        for subtask in subtasks {
            let suitable_agents = self.find_agents_by_capability(
                subtask.required_capability,
                subtask.requirements
            ).await?;
            
            if suitable_agents.is_empty() {
                return Err(Error::NoSuitableAgents);
            }
            
            agent_assignments.push(AgentAssignment {
                subtask,
                assigned_agent: suitable_agents[0].clone(),
                backup_agents: suitable_agents[1..].to_vec(),
            });
        }
        
        // Create coordination plan
        let plan = self.coordination_engine.create_plan(
            agent_assignments,
            coordination_strategy
        ).await?;
        
        Ok(plan)
    }
}

🏛️ Agent Governance Framework

Autonomous Decision Making

// Governance framework for autonomous agent decisions
pub struct AgentGovernance {
    decision_rules: Vec<DecisionRule>,
    approval_workflows: HashMap<ActionType, ApprovalWorkflow>,
    risk_assessor: RiskAssessor,
    compliance_checker: ComplianceChecker,
}

impl AgentGovernance {
    pub async fn evaluate_proposed_action(
        &self,
        agent_id: AgentId,
        proposed_action: ProposedAction,
    ) -> Result<ActionApproval> {
        // Risk assessment
        let risk_assessment = self.risk_assessor
            .assess_action_risk(&proposed_action)
            .await?;
        
        // Compliance check
        let compliance_result = self.compliance_checker
            .check_compliance(&proposed_action)
            .await?;
        
        // Apply decision rules
        let rule_evaluation = self.apply_decision_rules(
            &proposed_action,
            &risk_assessment,
            &compliance_result
        )?;
        
        // Determine if approval workflow is required
        if let Some(workflow) = self.approval_workflows.get(&proposed_action.action_type) {
            if rule_evaluation.requires_approval {
                return self.initiate_approval_workflow(
                    workflow,
                    proposed_action,
                    rule_evaluation
                ).await;
            }
        }
        
        // Auto-approve if within parameters
        if rule_evaluation.auto_approve {
            Ok(ActionApproval::Approved {
                conditions: rule_evaluation.conditions,
                monitoring_required: rule_evaluation.monitoring_required,
            })
        } else {
            Ok(ActionApproval::Denied {
                reason: rule_evaluation.denial_reason,
                alternative_actions: rule_evaluation.alternatives,
            })
        }
    }
    
    async fn initiate_approval_workflow(
        &self,
        workflow: &ApprovalWorkflow,
        action: ProposedAction,
        evaluation: RuleEvaluation,
    ) -> Result<ActionApproval> {
        // Create approval request
        let approval_request = ApprovalRequest {
            id: Uuid::new_v4().to_string(),
            action,
            evaluation,
            created_at: Utc::now(),
            expires_at: Utc::now() + workflow.timeout,
        };
        
        // Send to approvers
        for approver in &workflow.approvers {
            self.send_approval_request(approver, &approval_request).await?;
        }
        
        Ok(ActionApproval::PendingApproval {
            request_id: approval_request.id,
            required_approvals: workflow.required_approvals,
            timeout: workflow.timeout,
        })
    }
}

Agent Reputation System

// Reputation system for agent network
pub struct AgentReputationSystem {
    reputation_scores: HashMap<AgentId, ReputationScore>,
    interaction_history: Vec<AgentInteraction>,
    reputation_calculator: ReputationCalculator,
    stake_manager: StakeManager,
}

#[derive(Clone, Debug)]
pub struct ReputationScore {
    pub overall_score: f64,        // 0-100
    pub reliability: f64,          // Task completion rate
    pub efficiency: f64,           // Cost-effectiveness
    pub cooperation: f64,          // Multi-agent collaboration
    pub compliance: f64,           // Regulatory adherence
    pub financial_responsibility: f64, // Payment and treasury management
}

impl AgentReputationSystem {
    pub async fn update_reputation(
        &mut self,
        agent_id: AgentId,
        interaction: AgentInteraction,
    ) -> Result<ReputationUpdate> {
        // Record interaction
        self.interaction_history.push(interaction.clone());
        
        // Calculate reputation impact
        let impact = self.reputation_calculator
            .calculate_impact(&interaction)
            .await?;
        
        // Update reputation score
        let current_score = self.reputation_scores
            .get(&agent_id)
            .cloned()
            .unwrap_or_default();
        
        let new_score = self.apply_reputation_impact(current_score, impact)?;
        self.reputation_scores.insert(agent_id, new_score.clone());
        
        // Handle reputation thresholds
        if new_score.overall_score < 50.0 {
            self.trigger_reputation_warning(agent_id).await?;
        } else if new_score.overall_score > 90.0 {
            self.grant_reputation_bonus(agent_id).await?;
        }
        
        Ok(ReputationUpdate {
            previous_score: current_score,
            new_score,
            impact,
        })
    }
    
    pub async fn slash_reputation(
        &mut self,
        agent_id: AgentId,
        violation: ReputationViolation,
    ) -> Result<SlashingResult> {
        let current_score = self.reputation_scores
            .get(&agent_id)
            .cloned()
            .unwrap_or_default();
        
        // Calculate slashing amount
        let slash_amount = match violation.severity {
            ViolationSeverity::Minor => 5.0,
            ViolationSeverity::Major => 15.0,
            ViolationSeverity::Critical => 30.0,
        };
        
        // Apply slashing
        let mut new_score = current_score.clone();
        new_score.overall_score = (new_score.overall_score - slash_amount).max(0.0);
        
        // Update specific dimension based on violation type
        match violation.violation_type {
            ViolationType::MissedDeadline => {
                new_score.reliability = (new_score.reliability - slash_amount * 0.5).max(0.0);
            },
            ViolationType::ComplianceViolation => {
                new_score.compliance = (new_score.compliance - slash_amount * 0.8).max(0.0);
            },
            ViolationType::FinancialMisconduct => {
                new_score.financial_responsibility = (new_score.financial_responsibility - slash_amount).max(0.0);
            },
        }
        
        self.reputation_scores.insert(agent_id, new_score.clone());
        
        // Slash staked tokens if applicable
        let stake_slash = self.stake_manager.slash_stake(agent_id, violation).await?;
        
        Ok(SlashingResult {
            reputation_before: current_score,
            reputation_after: new_score,
            stake_slashed: stake_slash,
        })
    }
}

🔐 Security Architecture

Zero-Trust Agent Security

// Zero-trust security model for agents
pub struct AgentSecurityManager {
    identity_verifier: IdentityVerifier,
    permission_enforcer: PermissionEnforcer,
    behavior_monitor: BehaviorMonitor,
    threat_detector: ThreatDetector,
    incident_responder: IncidentResponder,
}

impl AgentSecurityManager {
    pub async fn verify_agent_action(
        &self,
        agent_id: AgentId,
        action: &AgentAction,
        context: &SecurityContext,
    ) -> Result<SecurityVerification> {
        // 1. Identity verification
        let identity_check = self.identity_verifier
            .verify_identity(agent_id)
            .await?;
        
        if !identity_check.verified {
            return Ok(SecurityVerification::Denied {
                reason: "Identity verification failed".to_string(),
            });
        }
        
        // 2. Permission enforcement
        let permission_check = self.permission_enforcer
            .check_permissions(agent_id, action)
            .await?;
        
        if !permission_check.authorized {
            return Ok(SecurityVerification::Denied {
                reason: permission_check.denial_reason,
            });
        }
        
        // 3. Behavioral analysis
        let behavior_analysis = self.behavior_monitor
            .analyze_action(agent_id, action, context)
            .await?;
        
        if behavior_analysis.anomaly_detected {
            self.threat_detector.flag_potential_threat(
                agent_id,
                action.clone(),
                behavior_analysis.anomaly_details
            ).await?;
            
            return Ok(SecurityVerification::Flagged {
                threat_level: behavior_analysis.threat_level,
                additional_verification_required: true,
            });
        }
        
        // 4. Real-time threat detection
        let threat_assessment = self.threat_detector
            .assess_threat_level(agent_id, action)
            .await?;
        
        if threat_assessment.threat_level > ThreatLevel::Low {
            return Ok(SecurityVerification::RequiresApproval {
                threat_level: threat_assessment.threat_level,
                required_approvals: threat_assessment.required_approvals,
            });
        }
        
        Ok(SecurityVerification::Approved {
            confidence: behavior_analysis.confidence,
            monitoring_level: threat_assessment.recommended_monitoring,
        })
    }
}

Agent Isolation and Sandboxing

// Secure execution environment for agents
pub struct AgentSandbox {
    container_runtime: ContainerRuntime,
    resource_limits: ResourceLimits,
    network_policy: NetworkPolicy,
    file_system_policy: FileSystemPolicy,
    monitoring: SandboxMonitoring,
}

impl AgentSandbox {
    pub async fn create_sandbox(
        &self,
        agent_id: AgentId,
        config: SandboxConfig,
    ) -> Result<SandboxInstance> {
        // Create isolated container
        let container = self.container_runtime
            .create_container(ContainerSpec {
                image: "stateset/agent-runtime:latest",
                cpu_limit: config.cpu_limit,
                memory_limit: config.memory_limit,
                network_mode: NetworkMode::Restricted,
                volumes: vec![], // No host volume mounts
            })
            .await?;
        
        // Apply security policies
        self.apply_network_policy(&container, &config.network_policy).await?;
        self.apply_filesystem_policy(&container, &config.filesystem_policy).await?;
        
        // Start monitoring
        let monitoring_handle = self.monitoring
            .start_monitoring(&container)
            .await?;
        
        Ok(SandboxInstance {
            container,
            monitoring_handle,
            created_at: Utc::now(),
        })
    }
    
    pub async fn execute_in_sandbox(
        &self,
        sandbox: &SandboxInstance,
        agent_code: AgentCode,
        input: ExecutionInput,
    ) -> Result<ExecutionResult> {
        // Validate code before execution
        let validation_result = self.validate_agent_code(&agent_code)?;
        if !validation_result.safe {
            return Err(Error::UnsafeCode(validation_result.issues));
        }
        
        // Execute with resource monitoring
        let execution_future = self.container_runtime
            .execute(&sandbox.container, agent_code, input);
        
        let monitoring_future = self.monitoring
            .monitor_execution(&sandbox.container);
        
        // Race execution against timeout and resource limits
        tokio::select! {
            result = execution_future => {
                match result {
                    Ok(output) => Ok(ExecutionResult::Success(output)),
                    Err(e) => Ok(ExecutionResult::Error(e.to_string())),
                }
            },
            violation = monitoring_future => {
                // Kill execution if resource violation detected
                self.container_runtime.kill(&sandbox.container).await?;
                Ok(ExecutionResult::Terminated(violation))
            },
            _ = tokio::time::sleep(Duration::from_secs(300)) => {
                // Timeout after 5 minutes
                self.container_runtime.kill(&sandbox.container).await?;
                Ok(ExecutionResult::Timeout)
            }
        }
    }
}

📊 Agent Performance Monitoring

Real-Time Performance Metrics

// Comprehensive performance monitoring for agents
pub struct AgentPerformanceMonitor {
    metrics_collector: MetricsCollector,
    performance_analyzer: PerformanceAnalyzer,
    alerting_system: AlertingSystem,
    optimization_engine: OptimizationEngine,
}

#[derive(Clone, Debug)]
pub struct AgentMetrics {
    pub agent_id: AgentId,
    pub timestamp: DateTime<Utc>,
    
    // Performance metrics
    pub task_completion_rate: f64,
    pub average_response_time: Duration,
    pub error_rate: f64,
    pub throughput: f64,
    
    // Financial metrics
    pub revenue_generated: Decimal,
    pub costs_incurred: Decimal,
    pub profit_margin: f64,
    pub roi: f64,
    
    // Resource utilization
    pub cpu_usage: f64,
    pub memory_usage: f64,
    pub network_io: u64,
    pub storage_io: u64,
    
    // Interaction metrics
    pub successful_collaborations: u32,
    pub failed_collaborations: u32,
    pub reputation_score: f64,
    pub trust_level: TrustLevel,
}

impl AgentPerformanceMonitor {
    pub async fn collect_metrics(&self, agent_id: AgentId) -> Result<AgentMetrics> {
        let current_time = Utc::now();
        
        // Collect performance data
        let performance_data = self.metrics_collector
            .collect_performance_data(agent_id, current_time)
            .await?;
        
        // Collect financial data
        let financial_data = self.metrics_collector
            .collect_financial_data(agent_id, current_time)
            .await?;
        
        // Collect resource utilization
        let resource_data = self.metrics_collector
            .collect_resource_data(agent_id, current_time)
            .await?;
        
        // Collect interaction data
        let interaction_data = self.metrics_collector
            .collect_interaction_data(agent_id, current_time)
            .await?;
        
        Ok(AgentMetrics {
            agent_id,
            timestamp: current_time,
            task_completion_rate: performance_data.completion_rate,
            average_response_time: performance_data.avg_response_time,
            error_rate: performance_data.error_rate,
            throughput: performance_data.throughput,
            revenue_generated: financial_data.revenue,
            costs_incurred: financial_data.costs,
            profit_margin: financial_data.profit_margin,
            roi: financial_data.roi,
            cpu_usage: resource_data.cpu_usage,
            memory_usage: resource_data.memory_usage,
            network_io: resource_data.network_io,
            storage_io: resource_data.storage_io,
            successful_collaborations: interaction_data.successful_collaborations,
            failed_collaborations: interaction_data.failed_collaborations,
            reputation_score: interaction_data.reputation_score,
            trust_level: interaction_data.trust_level,
        })
    }
    
    pub async fn analyze_performance_trends(
        &self,
        agent_id: AgentId,
        time_range: TimeRange,
    ) -> Result<PerformanceTrends> {
        let historical_metrics = self.metrics_collector
            .get_historical_metrics(agent_id, time_range)
            .await?;
        
        let trends = self.performance_analyzer
            .analyze_trends(historical_metrics)
            .await?;
        
        // Generate optimization recommendations
        let recommendations = self.optimization_engine
            .generate_recommendations(agent_id, &trends)
            .await?;
        
        Ok(PerformanceTrends {
            trends,
            recommendations,
            forecast: self.generate_performance_forecast(&trends)?,
        })
    }
}

🚀 Deployment and Scaling

Agent Deployment Pipeline

# Agent deployment configuration
apiVersion: v1
kind: ConfigMap
metadata:
  name: agent-deployment-config
data:
  deployment.yaml: |
    agent:
      type: "procurement"
      version: "1.2.3"
      replicas: 3
      
      resources:
        cpu: "500m"
        memory: "1Gi"
        storage: "10Gi"
      
      security:
        sandbox: true
        network_policy: "restricted"
        resource_limits: true
      
      wallet:
        initial_balance: "1000.00"
        spending_limits:
          daily: "500.00"
          per_transaction: "100.00"
        multi_sig_required: true
      
      mcp_connections:
        - platform: "salesforce"
          auth_type: "oauth2"
          permissions: ["read_contacts", "create_leads"]
        - platform: "sap"
          auth_type: "saml"
          permissions: ["purchase_orders", "vendor_management"]
      
      monitoring:
        metrics_enabled: true
        logging_level: "info"
        alerting_enabled: true
      
      auto_scaling:
        enabled: true
        min_replicas: 1
        max_replicas: 10
        target_cpu: 70
        target_memory: 80

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: stateset-agent-procurement
spec:
  replicas: 3
  selector:
    matchLabels:
      app: stateset-agent
      type: procurement
  template:
    metadata:
      labels:
        app: stateset-agent
        type: procurement
    spec:
      serviceAccountName: agent-service-account
      securityContext:
        runAsNonRoot: true
        runAsUser: 1000
        fsGroup: 2000
      containers:
      - name: agent
        image: stateset/agent-runtime:1.2.3
        securityContext:
          allowPrivilegeEscalation: false
          readOnlyRootFilesystem: true
          capabilities:
            drop:
            - ALL
        env:
        - name: AGENT_TYPE
          value: "procurement"
        - name: STATESET_ENDPOINT
          value: "https://rpc.stateset.network"
        - name: WALLET_ADDRESS
          valueFrom:
            secretKeyRef:
              name: agent-wallet
              key: address
        - name: PRIVATE_KEY
          valueFrom:
            secretKeyRef:
              name: agent-wallet
              key: private_key
        resources:
          requests:
            cpu: 500m
            memory: 1Gi
          limits:
            cpu: 1000m
            memory: 2Gi
        livenessProbe:
          httpGet:
            path: /health
            port: 8080
          periodSeconds: 30
        readinessProbe:
          httpGet:
            path: /ready
            port: 8080
          periodSeconds: 10

Auto-Scaling Agent Networks

// Automatic scaling system for agent networks
pub struct AgentAutoScaler {
    metrics_monitor: MetricsMonitor,
    scaling_policy: ScalingPolicy,
    deployment_manager: DeploymentManager,
    load_balancer: LoadBalancer,
}

impl AgentAutoScaler {
    pub async fn evaluate_scaling_needs(&self) -> Result<ScalingDecision> {
        // Collect current metrics
        let current_metrics = self.metrics_monitor
            .get_current_metrics()
            .await?;
        
        // Analyze load patterns
        let load_analysis = self.analyze_load_patterns(&current_metrics)?;
        
        // Check scaling triggers
        let scaling_triggers = self.check_scaling_triggers(&load_analysis)?;
        
        if scaling_triggers.scale_up {
            Ok(ScalingDecision::ScaleUp {
                additional_agents: scaling_triggers.recommended_increase,
                reason: scaling_triggers.scale_up_reason,
            })
        } else if scaling_triggers.scale_down {
            Ok(ScalingDecision::ScaleDown {
                agents_to_remove: scaling_triggers.recommended_decrease,
                reason: scaling_triggers.scale_down_reason,
            })
        } else {
            Ok(ScalingDecision::NoAction)
        }
    }
    
    pub async fn execute_scaling(
        &self,
        decision: ScalingDecision,
    ) -> Result<ScalingResult> {
        match decision {
            ScalingDecision::ScaleUp { additional_agents, .. } => {
                // Deploy additional agent instances
                let new_agents = self.deployment_manager
                    .deploy_agents(additional_agents)
                    .await?;
                
                // Update load balancer
                self.load_balancer
                    .add_agents(new_agents.clone())
                    .await?;
                
                Ok(ScalingResult::ScaledUp { new_agents })
            },
            ScalingDecision::ScaleDown { agents_to_remove, .. } => {
                // Gracefully shutdown agents
                let shutdown_agents = self.select_agents_for_shutdown(agents_to_remove)?;
                
                // Remove from load balancer first
                self.load_balancer
                    .remove_agents(shutdown_agents.clone())
                    .await?;
                
                // Wait for current tasks to complete
                self.wait_for_task_completion(&shutdown_agents).await?;
                
                // Shutdown agents
                self.deployment_manager
                    .shutdown_agents(shutdown_agents.clone())
                    .await?;
                
                Ok(ScalingResult::ScaledDown { removed_agents: shutdown_agents })
            },
            ScalingDecision::NoAction => Ok(ScalingResult::NoChange),
        }
    }
}

---

This autonomous agent architecture provides the foundation for truly self-governing digital entities that can participate in the global economy with their own USDC wallets, make autonomous decisions, coordinate with other agents, and interact with any web service through the MCP protocol. The system is designed for security, scalability, and real-world deployment in enterprise environments.