AI Agents与混合专家系统（MoE）：架构解析与工作流协同

# 简化的AI Agent基础类结构
class AIAgent:
    def __init__(self, name, expertise, tools):
        self.name = name  # 智能体名称
        self.expertise = expertise  # 专业领域
        self.tools = tools  # 可用工具集
        self.memory = []  # 记忆存储
        
    def perceive(self, environment):
        """感知环境并提取相关信息"""
        # 实现感知逻辑
        return processed_data
        
    def plan(self, goal):
        """基于目标制定执行计划"""
        # 实现规划逻辑
        return execution_plan
        
    def act(self, plan):
        """执行计划并使用工具"""
        # 实现行动逻辑
        return result
        
    def reflect(self, result):
        """评估结果并学习"""
        # 实现反思逻辑
        return lessons_learned

# 简化的MoE实现框架
import torch
import torch.nn as nn

class Expert(nn.Module):
    """单个专家网络"""
    def __init__(self, input_size, output_size):
        super(Expert, self).__init__()
        self.net = nn.Sequential(
            nn.Linear(input_size, 128),
            nn.ReLU(),
            nn.Linear(128, output_size)
        )
        
    def forward(self, x):
        return self.net(x)

class MoE(nn.Module):
    """混合专家系统"""
    def __init__(self, num_experts, input_size, output_size):
        super(MoE, self).__init__()
        self.experts = nn.ModuleList([Expert(input_size, output_size) for _ in range(num_experts)])
        self.gating = nn.Linear(input_size, num_experts)  # 门控网络
        
    def forward(self, x):
        # 计算专家权重
        gate_scores = torch.softmax(self.gating(x), dim=-1)
        
        # 选择top-k专家
        top_k_weights, top_k_indices = torch.topk(gate_scores, k=2, dim=-1)
        
        # 计算加权输出
        output = torch.zeros_like(x)
        for i in range(2):  # 对每个选中的专家
            expert_mask = top_k_indices == i
            expert_output = self.expertsx
            output += expert_mask.float() * top_k_weights[:, i:i+1] * expert_output
            
        return output

# 企业级Agent编排示例
class DevOpsOrchestrator:
    def __init__(self, agents):
        self.agents = {agent.name: agent for agent in agents}  # Agent注册表
        self.context_history = []  # 上下文历史
        
    def classify_intent(self, user_input):
        """使用LLM确定哪个Agent应处理请求"""
        # 实现意图分类逻辑
        return routing_decision
        
    def orchestrate(self, user_input):
        """协调工作流执行"""
        self.context_history.append(f"User: {user_input}")
        
        # 分类意图并路由到合适Agent
        routing_decision = self.classify_intent(user_input)
        agent_name = routing_decision["agent"]
        task = routing_decision["task"]
        
        if agent_name in self.agents:
            result = self.agents[agent_name].execute_task(task)
            self.context_history.append(f"{agent_name}: {result}")
            return result
            
        return "No suitable agent found for this request"

# 创建专门化Agents
deployment_agent = Agent(
    name="deployment_agent",
    description="处理应用程序部署和发布",
    tools=[DeploymentTool()],
    model="gpt-4o-mini"
)

monitoring_agent = Agent(
    name="monitoring_agent",
    description="监控系统健康和处理警报",
    tools=[MetricsTool(), AlertTool()],
    model="gpt-4"
)

# 创建编排器并执行协调工作流
orchestrator = DevOpsOrchestrator([deployment_agent, monitoring_agent])
result = orchestrator.orchestrate("部署新API版本并监控错误")

# 专家负载均衡示例
class LoadBalancer:
    def __init__(self, experts):
        self.experts = experts
        self.load_history = {expert: [] for expert in experts}
        
    def route_input(self, input_data):
        """根据负载和专业化路由输入"""
        # 评估每个专家的当前负载和专业匹配度
        expert_scores = {}
        for expert in self.experts:
            load_score = self.calculate_load_score(expert)
            specialization_score = self.calculate_specialization_score(expert, input_data)
            expert_scores[expert] = (0.7 * specialization_score) + (0.3 * load_score)
            
        # 选择最佳专家
        best_expert = max(expert_scores, key=expert_scores.get)
        self.update_load_history(best_expert)
        
        return best_expert.process(input_data)