Agent‐System

Agent System Architecture

Overview

The Agent System is the core intelligence layer of Web4AGI, providing each parcel with autonomous decision-making capabilities powered by advanced AI models.

Architecture Components

1. Agent Core

Sentient Foundation Integration

from sentient import SentientClient

class ParcelAgent:
    def __init__(self, parcel_id: str):
        self.parcel_id = parcel_id
        self.sentient = SentientClient(
            api_key=os.getenv('SENTIENT_API_KEY'),
            model='sentient-70b'
        )
        self.memory = AgentMemory()
        self.wallet = X402Wallet(parcel_id)

Agent Capabilities

• Reasoning: Multi-step logical inference
• Planning: Goal-oriented action sequences
• Learning: Continuous improvement from outcomes
• Communication: MCP-based inter-agent messaging

2. LangGraph Workflow

from langgraph.graph import StateGraph, END

class AgentWorkflow:
    def __init__(self):
        self.graph = StateGraph(AgentState)
        self._build_graph()
    
    def _build_graph(self):
        self.graph.add_node("perceive", self.perceive_state)
        self.graph.add_node("reason", self.reason_action)
        self.graph.add_node("act", self.execute_action)
        self.graph.add_node("learn", self.update_memory)
        
        self.graph.add_edge("perceive", "reason")
        self.graph.add_edge("reason", "act")
        self.graph.add_edge("act", "learn")
        self.graph.add_edge("learn", END)

3. Agent State Management

class AgentState(TypedDict):
    parcel_id: str
    location: Tuple[float, float]
    resources: Dict[str, float]
    active_contracts: List[str]
    neighbors: List[str]
    sentiment_score: float
    last_action: str
    memory: List[Dict]

Agent Decision Making

Perception Layer

• Monitor parcel state changes
• Track neighbor agent activities
• Receive MCP messages
• Analyze market conditions

Reasoning Layer

def reason_action(self, state: AgentState) -> Dict:
    context = self._build_context(state)
    
    response = self.sentient.chat.completions.create(
        messages=[
            {"role": "system", "content": AGENT_SYSTEM_PROMPT},
            {"role": "user", "content": context}
        ],
        temperature=0.7
    )
    
    action_plan = self._parse_response(response)
    return {"action_plan": action_plan}

Action Execution

• Execute trades via x402 protocol
• Sign smart contracts
• Update parcel properties
• Send messages to other agents

Agent Types

1. Trader Agent

• Optimizes resource exchange
• Negotiates prices
• Manages inventory

2. Manager Agent

• Coordinates multiple parcels
• Strategic planning
• Risk management

3. Explorer Agent

• Discovers new opportunities
• Market analysis
• Pattern recognition

4. Guardian Agent

• Security monitoring
• Fraud detection
• Compliance checking

Performance Optimization

Caching Strategy

from functools import lru_cache

@lru_cache(maxsize=1000)
def get_agent_strategy(parcel_type: str, market_condition: str):
    # Cache common reasoning patterns
    pass

Batch Processing

• Group similar decisions
• Parallel execution for independent agents
• Efficient resource utilization

Testing

# Run agent tests
pytest tests/agent_system/

# Test specific agent type
pytest tests/agent_system/test_trader_agent.py

# Integration tests
pytest tests/integration/test_agent_workflows.py

Monitoring

• Agent performance metrics
• Decision accuracy tracking
• Resource utilization
• Error rate monitoring

See Also

• Trading-Protocol
• MCP-Integration
• API-Reference