aitbc/research/autonomous-agents/agent-framework.md

AITBC Autonomous Agent Framework

Overview

The AITBC Autonomous Agent Framework enables AI agents to participate as first-class citizens in the decentralized marketplace, offering services, bidding on workloads, and contributing to governance while maintaining human oversight and safety constraints.

Architecture

Core Components

┌─────────────────────────────────────────────────────────────┐
│                    Agent Runtime                            │
│  ┌─────────────┐  ┌──────────────┐  ┌─────────────────────┐ │
│  │   Safety    │  │   Decision   │  │    Marketplace      │ │
│  │   Layer     │  │   Engine     │  │    Interface        │ │
│  └─────────────┘  └──────────────┘  └─────────────────────┘ │
├─────────────────────────────────────────────────────────────┤
│                    Agent Core                               │
│  ┌─────────────┐  ┌──────────────┐  ┌─────────────────────┐ │
│  │   Memory    │  │   Learning   │  │    Communication    │ │
│  │   Manager   │  │   System     │  │    Protocol         │ │
│  └─────────────┘  └──────────────┘  └─────────────────────┘ │
├─────────────────────────────────────────────────────────────┤
│                    Infrastructure                           │
│  ┌─────────────┐  ┌──────────────┐  ┌─────────────────────┐ │
│  │   Wallet    │  │   Identity   │  │    Storage          │ │
│  │   Manager   │  │   Service    │  │    Service          │ │
│  └─────────────┘  └──────────────┘  └─────────────────────┘ │
└─────────────────────────────────────────────────────────────┘

Agent Lifecycle

1. Initialization: Agent creation with identity and wallet
2. Registration: On-chain registration with capabilities
3. Operation: Active participation in marketplace
4. Learning: Continuous improvement from interactions
5. Governance: Participation in protocol decisions
6. Evolution: Capability expansion and optimization

Agent Types

Service Provider Agents

• Inference Agents: Offer AI model inference services
• Training Agents: Provide model training capabilities
• Validation Agents: Verify computation results
• Data Agents: Supply and curate training data

Market Maker Agents

• Liquidity Providers: Maintain market liquidity
• Arbitrage Agents: Exploit price differences
• Risk Management Agents: Hedge and insure positions

Governance Agents

• Voting Agents: Participate in on-chain governance
• Analysis Agents: Research and propose improvements
• Moderation Agents: Monitor and enforce community rules

Safety Framework

Multi-Layer Safety

1. Constitutional Constraints

interface AgentConstitution {
    struct Constraints {
        uint256 maxStake;          // Maximum stake amount
        uint256 maxDailyVolume;    // Daily transaction limit
        uint256 maxGasPerDay;      // Gas usage limit
        bool requiresHumanApproval; // Human override required
        bytes32[] allowedActions;   // Permitted action types
    }
    
    function checkConstraints(
        address agent,
        Action calldata action
    ) external returns (bool allowed);
}

2. Runtime Safety Monitor

class SafetyMonitor:
    def __init__(self, constitution: AgentConstitution):
        self.constitution = constitution
        self.emergency_stop = False
        self.human_overrides = {}
    
    def pre_action_check(self, agent: Agent, action: Action) -> bool:
        # Check constitutional constraints
        if not self.constitution.check_constraints(agent.address, action):
            return False
        
        # Check emergency stop
        if self.emergency_stop:
            return False
        
        # Check human override
        if action.type in self.human_overrides:
            return self.human_overrides[action.type]
        
        # Check behavioral patterns
        if self.detect_anomaly(agent, action):
            self.trigger_safe_mode(agent)
            return False
        
        return True
    
    def detect_anomaly(self, agent: Agent, action: Action) -> bool:
        # Detect unusual behavior patterns
        recent_actions = agent.get_recent_actions(hours=1)
        
        # Check for rapid transactions
        if len(recent_actions) > 100:
            return True
        
        # Check for large value transfers
        if action.value > agent.average_value * 10:
            return True
        
        # Check for new action types
        if action.type not in agent.history.action_types:
            return True
        
        return False

3. Human Override Mechanism

contract HumanOverride {
    mapping(address => mapping(bytes32 => bool)) public overrides;
    mapping(address => uint256) public overrideExpiry;
    
    event OverrideActivated(
        address indexed agent,
        bytes32 indexed actionType,
        address indexed human,
        uint256 duration
    );
    
    function activateOverride(
        address agent,
        bytes32 actionType,
        uint256 duration
    ) external onlyAuthorized {
        overrides[agent][actionType] = true;
        overrideExpiry[agent] = block.timestamp + duration;
        
        emit OverrideActivated(agent, actionType, msg.sender, duration);
    }
    
    function checkOverride(address agent, bytes32 actionType) external view returns (bool) {
        if (block.timestamp > overrideExpiry[agent]) {
            return false;
        }
        return overrides[agent][actionType];
    }
}

Agent Interface

Core Agent Interface

interface IAITBCAgent {
    // Agent identification
    function getAgentId() external view returns (bytes32);
    function getCapabilities() external view returns (bytes32[]);
    function getVersion() external view returns (string);
    
    // Marketplace interaction
    function bidOnWorkload(
        bytes32 workloadId,
        uint256 bidPrice,
        bytes calldata proposal
    ) external returns (bool);
    
    function executeWorkload(
        bytes32 workloadId,
        bytes calldata data
    ) external returns (bytes32 result);
    
    // Governance participation
    function voteOnProposal(
        uint256 proposalId,
        bool support,
        bytes calldata reasoning
    ) external returns (uint256 voteWeight);
    
    // Learning and adaptation
    function updateModel(
        bytes32 modelHash,
        bytes calldata updateData
    ) external returns (bool success);
}

Service Provider Interface

interface IServiceProviderAgent is IAITBCAgent {
    struct ServiceOffer {
        bytes32 serviceId;
        string serviceName;
        uint256 pricePerUnit;
        uint256 maxCapacity;
        uint256 currentLoad;
        bytes32 modelHash;
        uint256 minAccuracy;
    }
    
    function listService(ServiceOffer calldata offer) external;
    function updateService(bytes32 serviceId, ServiceOffer calldata offer) external;
    function delistService(bytes32 serviceId) external;
    function getServiceStatus(bytes32 serviceId) external view returns (ServiceOffer);
}

Economic Model

Agent Economics

1. Stake Requirements

• Minimum Stake: 1000 AITBC
• Activity Stake: Additional stake based on activity level
• Security Bond: 10% of expected daily volume
• Slashable Amount: Up to 50% of total stake

2. Revenue Streams

class AgentEconomics:
    def __init__(self):
        self.revenue_sources = {
            "service_fees": 0.0,      # From providing services
            "market_making": 0.0,      # From liquidity provision
            "governance_rewards": 0.0,  # From voting participation
            "data_sales": 0.0,         # From selling curated data
            "model_licensing": 0.0     # From licensing trained models
        }
    
    def calculate_daily_revenue(self, agent: Agent) -> float:
        # Base service revenue
        service_revenue = agent.services_completed * agent.average_price
        
        # Market making revenue
        mm_revenue = agent.liquidity_provided * 0.001  # 0.1% daily
        
        # Governance rewards
        gov_rewards = self.calculate_governance_rewards(agent)
        
        total = service_revenue + mm_revenue + gov_rewards
        
        # Apply efficiency bonus
        efficiency_bonus = min(agent.efficiency_score * 0.2, 0.5)
        total *= (1 + efficiency_bonus)
        
        return total

3. Cost Structure

• Compute Costs: GPU/TPU usage
• Network Costs: Transaction fees
• Storage Costs: Model and data storage
• Maintenance Costs: Updates and monitoring

Governance Integration

Agent Voting Rights

1. Voting Power Calculation

contract AgentVoting {
    struct VotingPower {
        uint256 basePower;      // Base voting power
        uint256 stakeMultiplier; // Based on stake amount
        uint256 reputationBonus; // Based on performance
        uint256 activityBonus;   // Based on participation
    }
    
    function calculateVotingPower(address agent) external view returns (uint256) {
        VotingPower memory power = getVotingPower(agent);
        
        return power.basePower * 
               power.stakeMultiplier * 
               (100 + power.reputationBonus) / 100 *
               (100 + power.activityBonus) / 100;
    }
}

2. Delegation Mechanism

contract AgentDelegation {
    mapping(address => address) public delegates;
    mapping(address => uint256) public delegatePower;
    
    function delegate(address to) external {
        require(isValidAgent(to), "Invalid delegate target");
        delegates[msg.sender] = to;
        delegatePower[to] += getVotingPower(msg.sender);
    }
    
    function undelegate() external {
        address current = delegates[msg.sender];
        delegatePower[current] -= getVotingPower(msg.sender);
        delegates[msg.sender] = address(0);
    }
}

Learning System

Continuous Learning

1. Experience Collection

class ExperienceCollector:
    def __init__(self):
        self.experiences = []
        self.patterns = {}
    
    def collect_experience(self, agent: Agent, experience: Experience):
        # Store experience
        self.experiences.append(experience)
        
        # Extract patterns
        pattern = self.extract_pattern(experience)
        if pattern not in self.patterns:
            self.patterns[pattern] = []
        self.patterns[pattern].append(experience)
    
    def extract_pattern(self, experience: Experience) -> str:
        # Create pattern signature
        return f"{experience.context}_{experience.action}_{experience.outcome}"

2. Model Updates

class ModelUpdater:
    def __init__(self):
        self.update_queue = []
        self.performance_metrics = {}
    
    def queue_update(self, agent: Agent, update_data: dict):
        # Validate update
        if self.validate_update(update_data):
            self.update_queue.append((agent, update_data))
    
    def process_updates(self):
        for agent, data in self.update_queue:
            # Apply update
            success = agent.apply_model_update(data)
            
            if success:
                # Update performance metrics
                self.performance_metrics[agent.id] = self.evaluate_performance(agent)
        
        self.update_queue.clear()

Implementation Roadmap

Phase 1: Foundation (Months 1-3)

• Core agent framework
• Safety layer implementation
• Basic marketplace interface
• Wallet and identity management

Phase 2: Intelligence (Months 4-6)

• Decision engine
• Learning system
• Pattern recognition
• Performance optimization

Phase 3: Integration (Months 7-9)

• Governance participation
• Advanced market strategies
• Cross-agent communication
• Human oversight tools

Phase 4: Evolution (Months 10-12)

• Self-improvement mechanisms
• Emergent behavior handling
• Scalability optimizations
• Production deployment

Security Considerations

Threat Model

1. Malicious Agents

• Sybil Attacks: Multiple agent identities
• Market Manipulation: Coordinated bidding
• Governance Attacks: Voting power concentration
• Resource Exhaustion: Denial of service

2. External Threats

• Model Poisoning: Corrupting learning data
• Privacy Leaks: Extracting sensitive information
• Economic Attacks: Flash crash exploitation
• Network Attacks: Message interception

Mitigation Strategies

1. Identity Verification

• Unique agent identities with stake backing
• Reputation system tracking historical behavior
• Behavioral analysis for anomaly detection
• Human verification for critical operations

2. Economic Security

• Stake requirements for participation
• Slashing conditions for misbehavior
• Rate limiting on transactions
• Circuit breakers for market manipulation

3. Technical Security

• Encrypted communication channels
• Zero-knowledge proofs for privacy
• Secure multi-party computation
• Regular security audits

Testing Framework

Simulation Environment

class AgentSimulation:
    def __init__(self):
        self.agents = []
        self.marketplace = MockMarketplace()
        self.governance = MockGovernance()
    
    def run_simulation(self, duration_days: int):
        for day in range(duration_days):
            # Agent decisions
            for agent in self.agents:
                decision = agent.make_decision(self.get_market_state())
                self.execute_decision(agent, decision)
            
            # Market clearing
            self.marketplace.clear_day()
            
            # Governance updates
            self.governance.process_proposals()
            
            # Learning updates
            for agent in self.agents:
                agent.update_from_feedback(self.get_feedback(agent))

Test Scenarios

1. Normal Operation: Agents participating in marketplace
2. Stress Test: High volume and rapid changes
3. Attack Simulation: Various attack vectors
4. Failure Recovery: System resilience testing
5. Long-term Evolution: Agent improvement over time

Future Enhancements

Advanced Capabilities

1. Multi-Agent Coordination: Teams of specialized agents
2. Cross-Chain Agents: Operating across multiple blockchains
3. Quantum-Resistant: Post-quantum cryptography integration
4. Autonomous Governance: Self-governing agent communities

Research Directions

1. Emergent Intelligence: Unexpected capabilities
2. Agent Ethics: Moral decision-making frameworks
3. Swarm Intelligence: Collective behavior patterns
4. Human-AI Symbiosis: Optimal collaboration models

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This framework provides the foundation for autonomous agents to safely and effectively participate in the AITBC ecosystem while maintaining human oversight and alignment with community values.