# AgentGPTRouter

**AgentGPTRouter** dynamically routes workflows to the optimal agent or blockchain based on real-time conditions (cost, speed, compliance). Built for enterprises managing cross-chain payrolls, regulatory arbitrage, and high-frequency DeFi strategies, it combines AI-driven decision-making with trustless execution to maximize efficiency and minimize risk.

***

#### **Core Components**

**1. Routing Decision Engine**

* **Algorithms**: Multi-armed bandit, reinforcement learning, and rule-based routing (gas fees, latency, liquidity).
* **Compliance Layer**: Ensures jurisdictional adherence (e.g., blocks Tornado Cash interactions in sanctioned regions).

**2. Real-Time Data Feeds**

* Sources: Chainlink (gas), Pyth (prices), on-chain sanctions lists.
* Auto-refreshed every 3 seconds.

**3. Execution Fallback**

* **Primary Route**: Optimal path (e.g., Ethereum).
* **Fallback Routes**: Backup agents/chains (e.g., Polygon → Arbitrum).

**4. ZK-Routed Proofs**

* Validates routing logic integrity via zero-knowledge proofs.

***

#### **Workflow**

```mermaid
sequenceDiagram  
    User->>Router: "Pay 10,000 USDC Salaries"  
    Router->>DataFeeds: Fetch Gas/Compliance Data  
    DataFeeds-->>Router: Polygon Gas: 12 Gwei, ETH: 89 Gwei  
    Router->>Compliance: Validate Recipients  
    Compliance-->>Router: Approved  
    Router->>Executor: Route via Polygon (Optimal)  
    Executor-->>User: Tx Confirmed (0.4s, $0.87 Fee)  
    alt Failure  
        Executor->>Router: Fallback to Arbitrum  
        Router-->>Executor: Retry  
    end  
```

***

#### **Logic Code Example**

```python
from agentgpt.architectures import RouterAgent  
from agentgpt.web3 import ERC7645, ZKValidator  
from typing import Dict, List  
import numpy as np  

class PayrollRouter(RouterAgent):  
    def __init__(self):  
        super().__init__(  
            routes=["ethereum", "polygon", "arbitrum"],  
            algorithms=["cost", "speed", "compliance"]  
        )  
        self.executor = ERC7645()  
        self.validator = ZKValidator()  
        self.gas_feed = ChainlinkGasFeed()  

    def compute_route_score(self, route: str, task: Dict) -> float:  
        gas_price = self.gas_feed.get_gas(route)  
        latency = self.gas_feed.get_latency(route)  
        compliance_risk = self.check_compliance(route, task)  
        
        # Weighted score (70% cost, 20% speed, 10% compliance)  
        cost_score = 1 / (gas_price * task["gas_limit"])  
        speed_score = 1 / latency  
        compliance_score = 10 if compliance_risk == 0 else 0  
        return (  
            0.7 * cost_score +  
            0.2 * speed_score +  
            0.1 * compliance_score  
        )  

    def select_route(self, task: Dict) -> str:  
        scores = {route: self.compute_route_score(route, task) for route in self.routes}  
        return max(scores, key=scores.get)  

    def execute_task(self, task: Dict, retries=3):  
        for attempt in range(retries):  
            route = self.select_route(task)  
            try:  
                tx_hash = self.executor.send_transaction(  
                    chain=route,  
                    amount=task["amount"],  
                    recipient=task["recipient"],  
                    gas_limit=task["gas_limit"]  
                )  
                proof = self.validator.generate_route_proof(route, tx_hash)  
                return {"tx_hash": tx_hash, "proof": proof}  
            except Exception as e:  
                self.log_failure(route, e)  
                if attempt == retries - 1:  
                    self.escalate_to_manual(task)  

# Usage  
router = PayrollRouter()  
task = {  
    "amount": 10000.0,  
    "recipient": "0xEmployee123",  
    "gas_limit": 50000  
}  
result = router.execute_task(task)  
```

***

#### **Key Features**

**1. Multi-Armed Bandit Routing**

Adaptively favor high-success routes:

```python
self.update_route_weights(success_rate={"ethereum": 0.94, "polygon": 0.97})  
```

**2. Deadline-Aware Routing**

Prioritize speed if time-sensitive:

```python
if task["deadline"] < 60:  # seconds  
    self.set_algorithm_weights(speed=0.8)  
```

**3. Compliance Guardrails**

Auto-block non-compliant routes:

```python
def check_compliance(self, route: str, task: Dict) -> float:  
    if route == "tornado_cash" and task["region"] == "EU":  
        return 100  # High risk  
    return 0  
```

***

#### **Enterprise Use Cases**

**1. Cross-Chain Payroll Optimization**

This month’s $35M payroll routed as:

* **62% Polygon** (avg. fee $0.92, 2.3s latency).
* **28% Arbitrum** (avg. fee $1.69, 1.1s latency).
* **10% Ethereum** (for audited executives only).

**2. DeFi Treasury Rebalancing**

* Swaps routed to Uniswap (mainnet), Curve (Polygon), or Sushiswap (Arbitrum) based on liquidity depth.

***

#### **Performance Metrics**

| **Metric**            | **Result**    |
| --------------------- | ------------- |
| Avg. Routing Speed    | 0.6s/decision |
| Cost Savings          | 59% vs manual |
| Compliance Violations | 0.003%        |

***

#### **Implementation Guide**

**1. CLI Commands**

Deploy a compliance-first payroll router:

```bash
agentgpt-cli create-router \  
  --name "EnterprisePayroll" \  
  --routes ethereum,polygon,arbitrum \  
  --weights compliance=0.7,cost=0.3  
```

**2. Best Practices**

* Use `route_health_check()` hourly to deprecate slow/failed routes.
* Set `retries=2` for mission-critical tasks to reduce latency.

***

#### **Limitations**

* **Data Feed Reliance**: Inaccurate gas estimates can lead to suboptimal routes.
* **Regulatory Lag**: Sanctions lists update every 10 minutes (\~3 TX can slip).


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