# Mixture of Agents

**Mixture of Agents (MoA)** integrates multiple specialized agents into a unified system to handle intricate Web3 workflows—such as cross-border payrolls with compliance, tax, and multi-chain execution. By combining expertise from domain-specific agents, MoA ensures adaptive and fault-tolerant automation for enterprises operating in decentralized ecosystems.

***

#### **Core Components**

**1. Virtual Agent Pool**

| Agent Type           | Role                                   |
| -------------------- | -------------------------------------- |
| **Compliance Agent** | Validates jurisdictional regulations.  |
| **FX Optimizer**     | Executes optimal currency conversions. |
| **Risk Analyst**     | Monitors DeFi collateral/flow risks.   |
| **Chain Router**     | Routes tasks across 40+ blockchains.   |

**2. Task Orchestrator**

* Dynamically splits workflows (e.g., payroll → compliance → FX → routing).
* Aggregate results via GPT-4-driven synthesis.

**3. ZK-Consensus Layer**

* Validates multi-agent outputs via zero-knowledge proofs.
* Anchors audit trails on Ethereum and Arweave.

***

#### **Workflow**

```mermaid
sequenceDiagram  
    User->>MoA: "Send $10M Salary (EUR to USDC)"  
    MoA->>Compliance: Screen 50K Employees  
    Compliance-->>MoA: 3 Sanctions Flagged  
    MoA->>FX: Convert EUR 8.5M to USDC (500+ Routes)  
    FX-->>MoA: Optimal Rate on Polygon (0.02% Slippage)  
    MoA->>Router: Split Payments (Ethereum 60%, Polygon 40%)  
    Router-->>User: 49,997 Payments Confirmed (9.2s Avg)  
```

***

#### **Logic Code Example**

```python
from agentgpt.architectures import MoA_Orchestrator  
from agentgpt.web3 import ERC7645, ZKValidator  
from typing import Dict, List  

class EnterprisePayrollMoA(MoA_Orchestrator):  
    def __init__(self):  
        super().__init__(agents=["Compliance", "FX", "Router"])  
        self.validator = ZKValidator()  
        self.executor = ERC7645()  

    def dispatch_tasks(self, task: Dict) -> Dict:  
        # Divide task for specialized agents  
        subtasks = {  
            "Compliance": {"addresses": task["recipients"], "regions": task["jurisdictions"]},  
            "FX": {"amount": task["amount"], "source": "EUR", "target": "USDC"},  
            "Router": {"chains": ["ethereum", "polygon"], "gas_limit": "auto"}  
        }  
        return subtasks  

    def aggregate_results(self, results: Dict) -> List[str]:  
        # Filter flagged addresses from Compliance  
        valid_recipients = [  
            addr for addr in results["Compliance"]  
            if addr["status"] == "approved"  
        ]  

        # Apply optimal FX rate  
        fx_data = results["FX"]  
        usdc_amount = fx_data["rate"] * len(valid_recipients)  

        # Split payments across chains  
        payments = self.executor.split_payments(  
            usdc_amount,  
            len(valid_recipients),  
            results["Router"]["chains"]  
        )  

        return payments  

    def execute_payroll(self, task: Dict):  
        try:  
            subtasks = self.dispatch_tasks(task)  
            agent_results = {}  
            for agent in self.agents:  
                agent_results[agent] = self.agents[agent].process(subtasks[agent])  
            
            payments = self.aggregate_results(agent_results)  
            tx_hashes = [  
                self.executor.transfer(p["recipient"], p["amount"], p["chain"])  
                for p in payments  
            ]  
            zk_proof = self.validator.generate_proof(agent_results)  
            return {"tx_hashes": tx_hashes, "zk_proof": zk_proof}  
        except Exception as e:  
            self.fallback_to_manual(e)  

# Usage  
moa = EnterprisePayrollMoA()  
task = {  
    "recipients": ["0xEmp1", "0xEmp2", ...],  # 50K addresses  
    "amount": 10000000.0,  
    "jurisdictions": ["EU", "US", "SG"]  
}  
result = moa.execute_payroll(task)  
```

***

#### **Key Features**

**1. Dynamic Task Routing**

Agents self-assign subtasks based on expertise:

```python
def assign_task(self, task_type: str) -> str:  
    if "sanction" in task_type:  
        return "Compliance"  
    elif "FX" in task_type:  
        return "Optimizer"  
```

**2. Cross-Model Synthesis**

Combine outputs from GPT-4, Claude-3, and chain-specific models:

```python
output = (  
    gpt4_analysis * 0.7 +  
    claude_analysis * 0.2 +  
    chainlink_data * 0.1  
)  
```

**3. Smart Contract Integration**

Auto-deploys ERC-7645-compliant contracts for batch payments:

```solidity
// Generated by AgentGPT  
contract PayrollBatch {  
    function execute(address[] recipients, uint[] amounts) external {  
        require(msg.sender == moaOrchestrator);  
        for (uint i; i < recipients.length; i++) {  
            USDC.transfer(recipients[i], amounts[i]);  
        }  
    }  
}  
```

***

#### **Enterprise Use Cases**

**1. Cross-Jurisdictional Payment Routing**

A global tech firm uses MoA to:

* Split $250M/month payroll across Ethereum (60%), Polygon (30%), Base (10%).
* Auto-convert 12 currencies via 1,300+ DEX/CEX routes.

**2. DeFi Treasury Diversification**

* **Compliance Agent**: Screens protocols against OFAC.
* **Risk Agent**: Allocates funds based on collateral health.
* **Router**: Deposits to AAVE, Compound, and Morpho.

***

#### **Performance Metrics**

| **Metric**              | **Result**                 |
| ----------------------- | -------------------------- |
| Success Rate            | 99.1%                      |
| Execution Time (10K tx) | 8.3s                       |
| Error Recovery          | 4.2s avg                   |
| Cost Efficiency         | 74% better vs single-agent |

***

#### **Implementation Guide**

**1. CLI Commands**

Deploy a MoA payroll system:

```bash
agentgpt-cli create-moa \  
  --name "GlobalPayroll" \  
  --agents Compliance,FX,Router \  
  --chains ethereum,polygon,base  
```

**2. Best Practices**

* Pre-validate compliance rules during off-peak hours.
* Set 25% higher gas limits for multi-chain batch transactions.

***

#### **Limitations**

* **Complexity**: Requires 3-6 month onboarding for legacy systems.
* **Cost**: Initial setup 30% higher vs RoundRobin.


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