DeepAgents+MCP+A2A+Skills超级多智能体全流程实战

感知 → 规划 → 行动 → 记忆 → 反思 → 感知（循环迭代）

┌─────────────────────────┐
│  Claude / DeepAgents    │ ← MCP Client
└───────────┬─────────────┘
            │ MCP协议（JSON-RPC 2.0）
     ┌──────┼──────┐
     ▼      ▼      ▼
┌────────┐┌────────┐┌────────┐
│ K8s    ││ 数据库 ││ 云API  │ ← MCP Servers
│ Server ││ Server ││ Server │
└────────┘└────────┘└────────┘

┌─────────────────────────────────────────────────────┐
│                    交互层 (WorkBuddy)                 │
│              自然语言理解 / 任务规划 / 审核把关          │
├─────────────────────────────────────────────────────┤
│                   协作层 (A2A Bus)                    │
│         Agent Card发布 / 任务路由 / 消息总线            │
│              基于腾讯云Redis实现高速消息队列              │
├─────────────────────────────────────────────────────┤
│                   执行层 (Skills + MCP)               │
│     Skills编排流程 ←→ MCP调用工具 ←→ 外部系统          │
├─────────────────────────────────────────────────────┤
│                 腾讯云基础设施层                       │
│   TKE(容器) │ TencentDB(数据库) │ Redis(消息) │ 云监控  │
└─────────────────────────────────────────────────────┘

super_agent_system/
├── config/
│   ├── agents.yaml          # Agent注册与角色定义
│   ├── mcp_servers.yaml     # MCP服务端配置（K8s, AWS, DB）
│   └── skills/
│       ├── report_gen.yaml
│       └── alert_analysis.yaml
├── core/
│   ├── deep_agent_wrapper.py   # DeepAgents封装（支持Checkpoint）
│   ├── mcp_client.py           # MCP统一工具调用客户端
│   ├── a2a_bus.py              # A2A消息总线（基于Redis Stream）
│   └── skill_loader.py         # 动态Skill加载器
├── agents/
│   ├── diagnostic_agent.py
│   ├── change_executor_agent.py
│   └── compliance_agent.py
├── mcp_servers/
│   ├── k8s_server.py           # 暴露kubectl/patch/describe为MCP工具
│   └── cloudwatch_server.py
├── orchestrator.py              # 主控流程（A2A路由+容错）
└── run.py                       # 启动入口

python# core/deep_agent_wrapper.py
from deepagents import DeepAgent

class DiagnosticAgent(DeepAgent):
    def __init__(self):
        super().__init__(
            name="diagnostic-agent",
            model="deepseek-v3",  # 或其他LLM
            tools=[mcp_client],    # 注入MCP工具
            memory_type="checkpoint",  # 支持断点续传
            planning_method="hierarchical"  # 分层规划
        )
    
    async def run(self, task: str):
        """自主规划→执行→反思→迭代"""
        plan = await self.plan(task)  # 任务分解
        for step in plan:
            result = await self.act(step)  # 调用MCP工具
            if not self.reflect(result):    # 反思检查
                plan = await self.replan(step)  # 重新规划
        return await self.synthesize()  # 综合输出

python# mcp_servers/k8s_server.py
from fastmcp import FastMCP
import subprocess

mcp = FastMCP("k8s-ops")

@mcp.tool()
def kubectl_get(resource: str, namespace: str = "default") -> str:
    """获取K8s资源"""
    cmd = f"kubectl get {resource} -n {namespace} -o json"
    return subprocess.run(cmd, shell=True, capture_output=True, text=True).stdout

@mcp.tool()
def kubectl_patch(resource: str, name: str, patch: str) -> str:
    """修改K8s资源"""
    cmd = f"kubectl patch {resource} {name} -p '{patch}'"
    return subprocess.run(cmd, shell=True, capture_output=True, text=True).stdout

if __name__ == "__main__":
    mcp.run(transport="sse")  # 生产环境用SSE

python# core/a2a_bus.py
import json
import uuid
import redis
from abc import ABC, abstractmethod

class BaseA2AAgent(ABC):
    def __init__(self, agent_id: str, capabilities: list, redis_client: redis.Redis):
        self.agent_id = agent_id
        self.capabilities = capabilities
        self.redis = redis_client
        # 发布Agent Card
        self.agent_card = {
            "id": agent_id,
            "capabilities": capabilities,
            "endpoint": f"http://tke-service.internal/{agent_id}/a2a"
        }
        self._publish_card()
    
    def _publish_card(self):
        self.redis.set(f"a2a:card:{self.agent_id}", json.dumps(self.agent_card))
    
    @abstractmethod
    def handle_task(self, task_input: dict) -> dict:
        pass
    
    def send_message(self, target_agent_id: str, message: dict):
        a2a_msg = {
            "jsonrpc": "2.0",
            "id": str(uuid.uuid4()),
            "method": "message/send",
            "params": {"message": message, "metadata": {"sender": self.agent_id}}
        }
        # 通过Redis Stream发送
        self.redis.xadd(f"a2a:queue:{target_agent_id}", a2a_msg)


class InventoryAgent(BaseA2AAgent):
    def handle_task(self, task_input: dict):
        product_id = task_input.get("product_id")
        # 调用MCP工具获取数据
        result = f"商品 {product_id} 库存充足，建议维持现有发货节奏。"
        return {"status": "completed", "result": result}

python# core/skill_loader.py
import yaml
from pathlib import Path

class SkillLoader:
    def __init__(self, skills_dir: str = "config/skills"):
        self.skills_dir = Path(skills_dir)
        self._skills_cache = {}
    
    def load_skill(self, skill_name: str):
        if skill_name in self._skills_cache:
            return self._skills_cache[skill_name]
        
        skill_file = self.skills_dir / f"{skill_name}.yaml"
        with open(skill_file) as f:
            skill_def = yaml.safe_load(f)
        
        self._skills_cache[skill_name] = skill_def
        return skill_def
    
    def list_skills(self):
        return [f.stem for f in self.skills_dir.glob("*.yaml")]

python# orchestrator.py
from tenacity import retry, stop_after_attempt, wait_exponential

class Orchestrator:
    def __init__(self):
        self.a2a_bus = A2ABus(redis_client)
        self.skill_loader = SkillLoader()
        self.mcp_client = MCPClient()
    
    @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
    async def dispatch(self, user_task: str):
        # Step 1: 规划任务
        plan = await self.deep_agent.plan(user_task)
        
        # Step 2: 按需分发给子Agent（A2A）
        for step in plan:
            target_agent = self._select_agent(step)
            result = await self.a2a_bus.send_task(target_agent, step)
            
            # Step 3: 需要工具时走MCP
            if result.needs_tool:
                tool_result = await self.mcp_client.call(result.tool_name, result.params)
                result.data = tool_result
            
            # Step 4: 调用Skill处理结果
            if result.skill_required:
                skill = self.skill_loader.load_skill(result.skill_required)
                result.output = await self._execute_skill(skill, result.data)
        
        # Step 5: 合规审计
        audit_result = await self.compliance_agent.audit(plan, results)
        
        return audit_result.final_output

dockerfile# Dockerfile
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "run.py"]

yaml# k8s/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: super-agent-system
spec:
  replicas: 3
  selector:
    matchLabels:
      app: super-agent
  template:
    spec:
      containers:
      - name: orchestrator
        image: tencent-cr/super-agent:latest
        ports:
        - containerPort: 8080
        env:
        - name: REDIS_URL
          value: "redis://tencent-redis:6379"
        - name: MCP_SERVER_URL
          value: "http://k8s-mcp-server:9000/sse"
        resources:
          limits:
            cpu: "2"
            memory: "4Gi"

第1步：MCP接入起步（用FastMCP快速搭建MCP Server）
   ↓
第2步：引入Skills模块化（封装通用能力，动态加载）
   ↓
第3步：按需A2A扩展（Agent间协作，任务分发）
   ↓
第4步：腾讯云工程化（TKE部署 + 监控 + 安全）