# 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
```solidity
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
```python
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
```solidity
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
```solidity
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
```solidity
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
```python
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
```solidity
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
```solidity
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
```python
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
```python
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
```python
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

---

*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.*
