AI Agent 架构设计：从单智能体到多智能体系统

AI Agent 架构设计：从单智能体到多智能体系统

Agent 核心概念

什么是 AI Agent？

AI Agent 是能够：

• 感知环境：接收和处理信息
• 自主决策：基于目标和状态做出选择
• 执行行动：调用工具或 API 完成任务
• 持续学习：从反馈中改进

核心组件

class AIAgent:
    def __init__(self, name, role, goal):
        self.name = name
        self.role = role
        self.goal = goal
        self.memory = []
        self.tools = []
    
    def perceive(self, observation):
        """感知环境"""
        pass
    
    def decide(self):
        """决策：选择下一步行动"""
        pass
    
    def act(self, action):
        """执行行动"""
        pass
    
    def learn(self, experience):
        """学习和改进"""
        pass
​

单智能体架构

1. ReAct 模式

推理+行动循环：

class ReActAgent:
    def __init__(self, llm, tools):
        self.llm = llm
        self.tools = tools
        self.memory = []
    
    def run(self, task):
        while not self.is_completed(task):
            # Thought: 推理
            thought = self.llm.generate(
                f"Task: {task}\nMemory: {self.memory}\nThought:"
            )
            
            # Action: 行动
            if "Search" in thought:
                action = self.tools["search"].run(thought)
            elif "Calculator" in thought:
                action = self.tools["calculator"].run(thought)
            else:
                action = "Finish"
            
            # Observation: 观察
            observation = self.execute(action)
            
            # 更新记忆
            self.memory.append((thought, action, observation))
​

2. 记忆增强

短期记忆：

class ShortTermMemory:
    def __init__(self, max_items=10):
        self.memory = deque(maxlen=max_items)
    
    def add(self, item):
        self.memory.append(item)
    
    def get_recent(self, n=5):
        return list(self.memory)[-n:]
​

长期记忆：

class LongTermMemory:
    def __init__(self, vector_store):
        self.vector_store = vector_store
    
    def add(self, document, metadata):
        embedding = self.embed(document)
        self.vector_store.add(embedding, metadata)
    
    def search(self, query, top_k=5):
        embedding = self.embed(query)
        return self.vector_store.search(embedding, top_k=top_k)
​

3. 工具使用

class ToolUse:
    def __init__(self):
        self.tools = {
            "search": WebSearchTool(),
            "calculator": CalculatorTool(),
            "code_interpreter": CodeInterpreterTool()
        }
    
    def use_tool(self, tool_name, input_data):
        if tool_name in self.tools:
            return self.tools[tool_name].run(input_data)
        else:
            raise ValueError(f"Unknown tool: {tool_name}")
​

多智能体系统

1. 协作模式

序列协作

class SequentialCollaboration:
    def __init__(self, agents):
        self.agents = agents
    
    def execute(self, task):
        result = task
        for agent in self.agents:
            result = agent.run(result)
        return result
​

并行协作

class ParallelCollaboration:
    def __init__(self, agents):
        self.agents = agents
    
    def execute(self, task):
        import concurrent.futures
        
        with concurrent.futures.ThreadPoolExecutor() as executor:
            futures = {
                executor.submit(agent.run, task): agent 
                for agent in self.agents
            }
            
            results = {}
            for future, agent in futures.items():
                results[agent.name] = future.result()
        
        return self.aggregate(results)
​

2. 分层架构

class HierarchicalAgents:
    def __init__(self):
        self.coordinator = CoordinatorAgent()
        self.workers = [
            ResearchAgent(),
            WriterAgent(),
            AnalystAgent()
        ]
    
    def execute(self, task):
        # 协调者分解任务
        subtasks = self.coordinator.decompose(task)
        
        # 分配给工作智能体
        results = {}
        for subtask, worker in zip(subtasks, self.workers):
            results[subtask] = worker.run(subtask)
        
        # 协调者聚合结果
        return self.coordinator.aggregate(results)
​

3. 通信机制

class AgentCommunication:
    def __init__(self):
        self.message_queue = []
    
    def send_message(self, from_agent, to_agent, message):
        self.message_queue.append({
            'from': from_agent,
            'to': to_agent,
            'message': message,
            'timestamp': time.time()
        })
    
    def receive_messages(self, agent):
        return [
            msg for msg in self.message_queue 
            if msg['to'] == agent
        ]
​

实际应用场景

1. 软件开发团队

class SoftwareDevTeam:
    def __init__(self):
        self.product_manager = ProductOwner()
        self.architect = Architect()
        self.developer = Developer()
        self.tester = QAEngineer()
    
    def develop_feature(self, feature_description):
        # 产品经理定义需求
        requirements = self.product_manager.define(feature_description)
        
        # 架构师设计系统
        design = self.architect.design(requirements)
        
        # 开发者实现
        code = self.developer.implement(design)
        
        # 测试工程师测试
        test_results = self.tester.test(code)
        
        return {
            'requirements': requirements,
            'design': design,
            'code': code,
            'test_results': test_results
        }
​

2. 研究团队

class ResearchTeam:
    def __init__(self):
        self.literature_reviewer = LiteratureReviewer()
        self.experiment_designer = ExperimentDesigner()
        self.data_analyst = DataAnalyst()
        self.report_writer = ReportWriter()
    
    def conduct_research(self, topic):
        # 文献综述
        literature = self.literature_reviewer.review(topic)
        
        # 设计实验
        experiment = self.experiment_designer.design(literature)
        
        # 分析数据
        results = self.data_analyst.analyze(experiment)
        
        # 撰写报告
        report = self.report_writer.write(results)
        
        return report
​

3. 客户支持团队

class CustomerSupportTeam:
    def __init__(self):
        self.triage_agent = TriageAgent()
        self.technical_agent = TechnicalSupportAgent()
        self.billing_agent = BillingAgent()
        self.escalation_agent = EscalationAgent()
    
    def handle_ticket(self, ticket):
        # 分诊
        category = self.triage_agent.classify(ticket)
        
        # 路由到专门的智能体
        if category == 'technical':
            response = self.technical_agent.solve(ticket)
        elif category == 'billing':
            response = self.billing_agent.handle(ticket)
        else:
            response = self.escalation_agent.escalate(ticket)
        
        return response
​

框架实现

使用 LangGraph

from langgraph.graph import StateGraph, END

class AgentState(TypedDict):
    task: str
    current_step: str
    result: str
    messages: List[str]

# 创建工作流
workflow = StateGraph(AgentState)

# 添加节点
workflow.add_node("researcher", research_node)
workflow.add_node("writer", write_node)
workflow.add_node("editor", edit_node)
workflow.add_node("publisher", publish_node)

# 添加边
workflow.add_edge("researcher", "writer")
workflow.add_edge("writer", "editor")
workflow.add_edge("editor", "publisher")
workflow.add_edge("publisher", END)

# 设置入口
workflow.set_entry_point("researcher")
​

使用 CrewAI

from crewai import Agent, Task, Crew

# 定义智能体
researcher = Agent(
    role='研究员',
    goal='收集和整理信息',
    backstory='你是一位经验丰富的研究专家',
    tools=[search_tool]
)

writer = Agent(
    role='作家',
    goal='撰写高质量文章',
    backstory='你是一位专业的技术作家',
    tools=[file_tool]
)

# 定义任务
research_task = Task(
    description='研究 AI Agent 的最新发展',
    agent=researcher
)

write_task = Task(
    description='基于研究结果撰写技术文章',
    agent=writer
)

# 组建团队
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    verbose=True
)

# 执行
result = crew.kickoff()
​

最佳实践

1. 角色定义

# ✅ 好的角色定义
agent = Agent(
    role='Python 开发者',
    goal='编写高质量的 Python 代码',
    backstory='你是一位有 10 年经验的 Python 开发者，擅长编写清晰、高效的代码',
    tools=[python_tool, documentation_tool]
)
​

2. 任务分解

# ❌ 不好的任务描述
task = Task(
    description='写个程序'
)

# ✅ 好的任务描述
task = Task(
    description='编写一个 Python 脚本，使用 pandas 读取 CSV 文件，计算平均值、中位数、标准差，并生成可视化图表'
)
​

3. 工具选择

# 为智能体选择合适的工具
developer_agent.tools = [
    CodeInterpreterTool(),
    DocumentationTool(),
    GitHubTool()
]

analyst_agent.tools = [
    PythonREPLTool(),
    PandasTool(),
    MatplotlibTool()
]
​

总结

AI Agent 系统设计要点：

1. 单智能体：掌握 ReAct 模式、记忆管理、工具使用
2. 多智能体：理解协作模式、通信机制、任务分配
3. 框架选择：LangGraph、CrewAI、AutoGen
4. 实际应用：软件开发、研究、客服等场景
5. 持续优化：监控性能、收集反馈、迭代改进

从简单开始，逐步构建复杂的智能体系统！