Agentic Mesh六层栈：从中心编排到P2P Agent自治网络

graph TD
    subgraph "2024: 星型编排"
        S1[Supervisor] --> W1[Worker 1]
        S1 --> W2[Worker 2]
        S1 --> W3[Worker 3]
    end

    subgraph "2025: 层级编排"
        S2[Supervisor] --> M1[Manager A]
        S2 --> M2[Manager B]
        M1 --> W4[Worker 1]
        M1 --> W5[Worker 2]
        M2 --> W6[Worker 3]
        M2 --> W7[Worker 4]
    end

    subgraph "2026: Agentic Mesh"
        A1[Agent α] --- A2[Agent β]
        A2 --- A3[Agent γ]
        A3 --- A4[Agent δ]
        A4 --- A5[Agent ε]
        A5 --- A1
        A1 --- A3
        A2 --- A5
    end

    style S1 fill:#ff6b6b,stroke:#333,color:#fff
    style S2 fill:#ffa94d,stroke:#333,color:#fff
    style A1 fill:#51cf66,stroke:#333,color:#fff
    style A2 fill:#51cf66,stroke:#333,color:#fff
    style A3 fill:#51cf66,stroke:#333,color:#fff
    style A4 fill:#51cf66,stroke:#333,color:#fff
    style A5 fill:#51cf66,stroke:#333,color:#fff

{
  "name": "billing-agent",
  "description": "Handles billing inquiries and payment processing",
  "url": "https://billing-agent.corp.example.com",
  "capabilities": {
    "streaming": true,
    "pushNotifications": true
  },
  "skills": [
    {
      "id": "process-refund",
      "name": "Process Refund",
      "description": "Initiates refund for a given transaction",
      "inputSchema": {
        "type": "object",
        "properties": {
          "transaction_id": {"type": "string"},
          "amount": {"type": "number"},
          "reason": {"type": "string"}
        }
      },
      "outputSchema": {
        "type": "object",
        "properties": {
          "refund_id": {"type": "string"},
          "status": {"type": "string"}
        }
      }
    }
  ],
  "authentication": {
    "schemes": ["bearer", "api-key"]
  }
}

# A2A JSON-RPC 任务委派示例
import httpx
import json

class A2AClient:
    def __init__(self, agent_url: str):
        self.agent_url = agent_url

    async def send_task(self, task_id: str, message: str):
        """向远程Agent发送任务"""
        payload = {
            "jsonrpc": "2.0",
            "method": "tasks/send",
            "id": task_id,
            "params": {
                "id": task_id,
                "message": {
                    "role": "user",
                    "parts": [{"type": "text", "text": message}]
                }
            }
        }
        async with httpx.AsyncClient() as client:
            response = await client.post(
                f"{self.agent_url}/a2a",
                json=payload,
                headers={"Content-Type": "application/json"}
            )
            return response.json()

    async def get_task(self, task_id: str):
        """查询任务状态"""
        payload = {
            "jsonrpc": "2.0",
            "method": "tasks/get",
            "id": f"query-{task_id}",
            "params": {"id": task_id}
        }
        async with httpx.AsyncClient() as client:
            response = await client.post(
                f"{self.agent_url}/a2a",
                json=payload
            )
            return response.json()

from agentic_mesh import MeshNode, MeshNetwork

# 创建Mesh网络
network = MeshNetwork(name="production-mesh")

# 注册Agent为Mesh节点
@network.agent(
    name="code-reviewer",
    capabilities=["code_review", "security_audit", "style_check"],
    tags=["development", "quality"]
)
class CodeReviewerAgent(MeshNode):
    async def handle_task(self, task):
        code = task.payload["code"]
        review = await self.analyze(code)
        return {"score": review.score, "issues": review.issues}

# 节点自动发现和连接
await network.start()  # Agent自动加入Mesh，广播其能力

import redis
import json
from datetime import timedelta

class AgentBlackboard:
    """基于Redis的多Agent共享黑板"""

    def __init__(self, redis_url="redis://localhost:6379"):
        self.r = redis.from_url(redis_url)

    def write(self, agent_id: str, namespace: str, key: str, data: dict,
              ttl_seconds: int = 3600):
        """Agent向黑板写入数据"""
        full_key = f"blackboard:{namespace}:{key}"
        entry = {
            "author": agent_id,
            "data": data,
            "timestamp": time.time(),
            "version": self._increment_version(full_key)
        }
        self.r.setex(full_key, ttl_seconds, json.dumps(entry))
        # 发布更新通知（订阅Agent会收到通知）
        self.r.publish(f"blackboard:updates:{namespace}", full_key)

    def read(self, namespace: str, pattern: str = "*"):
        """从黑板读取匹配的所有条目"""
        keys = self.r.keys(f"blackboard:{namespace}:{pattern}")
        return {k: json.loads(self.r.get(k)) for k in keys}

    def subscribe(self, namespace: str, callback):
        """订阅黑板更新"""
        pubsub = self.r.pubsub()
        pubsub.subscribe(f"blackboard:updates:{namespace}")
        for message in pubsub.listen():
            if message["type"] == "message":
                key = message["data"]
                data = json.loads(self.r.get(key))
                callback(data)

# 基于能力的任务路由示例
class TaskRouter:
    def __init__(self, mesh_network):
        self.mesh = mesh_network

    async def route_task(self, task_description: str, required_skills: list):
        """通过能力匹配路由任务到最佳Agent"""
        # 1. 发现所有声明了所需skill的Agent
        candidates = []
        for agent in self.mesh.agents.values():
            skill_match = set(required_skills) & set(agent.capabilities)
            if skill_match:
                candidates.append({
                    "agent": agent,
                    "match_score": len(skill_match) / len(required_skills),
                    "load": await agent.current_load(),
                    "success_rate": await agent.get_success_rate(required_skills)
                })

        if not candidates:
            raise NoAvailableAgentError(required_skills)

        # 2. 综合评分选择最佳Agent
        candidates.sort(key=lambda c: (
            c["match_score"] * 0.4 +
            c["success_rate"] * 0.4 +
            (1 - c["load"]) * 0.2
        ), reverse=True)

        return candidates[0]["agent"]

graph TB
    subgraph "Agentic Mesh 六层栈"
        L6["第六层：治理与可观测性<br/>信任链·审计·策略"]
        L5["第五层：容错与自愈<br/>心跳·回退·检查点·隔离"]
        L4["第四层：任务协商与委派<br/>能力路由·拍卖·涌现"]
        L3["第三层：共享状态/Blackboard<br/>工作记忆·短期·长期"]
        L2["第二层：P2P通信协议<br/>JSON-RPC·A2A·Gossip"]
        L1["第一层：Agent身份与发现<br/>Agent Card·Registry·Gossip"]
    end

    L1 --> L2 --> L3 --> L4 --> L5 --> L6

    style L1 fill:#4ecdc4,stroke:#333,color:#000
    style L2 fill:#45b7d1,stroke:#333,color:#000
    style L3 fill:#96ceb4,stroke:#333,color:#000
    style L4 fill:#feca57,stroke:#333,color:#000
    style L5 fill:#ff6b6b,stroke:#333,color:#fff
    style L6 fill:#a29bfe,stroke:#333,color:#fff

graph LR
    subgraph "混合拓扑：生产推荐"
        subgraph "开发域 Mesh"
            D1[Code<br/>Agent] --- D2[Review<br/>Agent]
            D2 --- D3[Test<br/>Agent]
            D3 --- D1
        end

        subgraph "运维域 Mesh"
            O1[Deploy<br/>Agent] --- O2[Monitor<br/>Agent]
            O2 --- O3[Alert<br/>Agent]
            O3 --- O1
        end

        subgraph "安全域 Mesh"
            S1[Scan<br/>Agent] --- S2[Audit<br/>Agent]
            S2 --- S3[Compliance<br/>Agent]
            S3 --- S1
        end

        H[Hub<br/>Router] --- D1
        H --- O1
        H --- S1
    end

    style H fill:#ff6b6b,stroke:#333,color:#fff
    style D1 fill:#51cf66,stroke:#333,color:#fff
    style D2 fill:#51cf66,stroke:#333,color:#fff
    style D3 fill:#51cf66,stroke:#333,color:#fff
    style O1 fill:#339af0,stroke:#333,color:#fff
    style O2 fill:#339af0,stroke:#333,color:#fff
    style O3 fill:#339af0,stroke:#333,color:#fff
    style S1 fill:#fcc419,stroke:#333,color:#000
    style S2 fill:#fcc419,stroke:#333,color:#000
    style S3 fill:#fcc419,stroke:#333,color:#000

消息传递模式（线性膨胀）：
  A(ctx=1x) → B(ctx=3x) → C(ctx=6x) → D(ctx=10x) → E(ctx=15x)

黑板模式（恒定开销）：
  A ──write──→ [Blackboard] ←──read── B(ctx=1x)
  B ──write──→ [Blackboard] ←──read── C(ctx=1x)
  C ──write──→ [Blackboard] ←──read── D(ctx=1x)
  D ──write──→ [Blackboard] ←──read── E(ctx=1x)

迁移ROI估算（100个微服务的中型企业）：

阶段一投入：2名工程师 × 3周 = 6人周
  产出：100个Agent Card，兼容A2A协议

阶段二投入：2名工程师 × 2.5周 = 5人周
  产出：Blackboard中间件，60%通信量降为黑板读写

阶段三投入：3名工程师 × 3.5周 = 10.5人周
  产出：Mesh组网，移除中心Orchestrator，延迟降低35%

阶段四投入：持续
  产出：自治Agent，人工干预减少70%

总投入：约22人周（5.5人月）
预期ROI：6个月内Agent协作效率提升3x，运维人工减少50%