各位同仁，各位对Agent技术充满热情的开发者们：
今天，我们汇聚一堂，探讨一个前瞻性且极具挑战的话题：如何构建一个具备三层架构——管理、协调、执行——的超大型Agent集群，也就是我们所称的“分层团队”（Hierarchical Teams）。随着人工智能技术日新月异，单个Agent的智能虽日益提升，但面对复杂、多阶段、高并发的任务时，其局限性也日益凸显。孤立的Agent难以处理大规模协作、任务分解、资源调度及容错等问题。
借鉴人类组织架构的智慧，分层管理和专业分工是解决复杂系统挑战的有效途径。在Agent领域，这意味着我们需要设计一个能够自我组织、自我协调、具备清晰职责划分的Agent集群。这不仅仅是Agent数量的叠加，更是协作模式的革新。我们将深入剖析如何从零开始，构建这样一个健壮、可扩展且高效的Agent生态。
1. 架构总览：分层团队的基石
想象一个大型企业，它不会只有一个总经理直接指挥所有员工。而是通过部门、小组层层分解任务，逐级汇报。我们的Agent集群亦是如此。我将这个三层架构定义为：
- 管理层 (Management Layer)：全局战略制定者，负责接收外部指令，定义高层目标，宏观监控，资源分配。
- 协调层 (Coordination Layer)：战术分解者，将管理层的战略目标拆解为可执行的子任务，管理任务流转，协调执行层资源，聚合结果。
- 执行层 (Execution Layer)：一线操作者，专注于完成特定、原子性的任务，如数据检索、代码生成、API调用等。
这种分层设计带来了显著优势：
- 职责明确：每个Agent层级都有清晰的使命，避免职责重叠和混乱。
- 任务分解：复杂任务被逐步细化，降低了单一Agent处理的复杂度。
- 并行处理：协调层可以将多个子任务并行分配给执行层，提高效率。
- 故障隔离：某个执行Agent失败不会导致整个系统崩溃，协调层可以重新分配任务。
- 可扩展性：通过增加特定层级的Agent数量来应对负载增长。
我们来看一个简化的架构示意表：
| 层级 | 核心职责 | 典型Agent类型 | 交互对象 | 关键能力 |
|---|---|---|---|---|
| 管理层 | 战略规划、宏观目标设定、全局监控、资源预算 | StrategicManager | 外部系统、协调层 | 任务接收、目标分解、状态监控、资源审批 |
| 协调层 | 任务分解、调度、依赖管理、结果聚合、冲突解决 | TaskCoordinator, SubtaskManager | 管理层、执行层 | 任务细化、进度跟踪、结果合并、局部决策 |
| 执行层 | 原子任务执行、工具调用、数据处理、信息检索 | CodeGenerator, DataAnalyzer, APICaller | 协调层、外部工具/API | 专精技能、高效执行、错误报告 |
接下来，我们将逐层深入，探讨每个层级的Agent设计、通信机制与代码实现。
2. 构建基石：Agent通用框架与通信机制
在深入各层级之前，我们需要一个基础的Agent框架和一套统一的通信协议。这保证了所有Agent能够互相理解并有效协作。
2.1 Agent基类设计
所有的Agent都将继承一个基类，包含Agent ID、名称、当前状态、发送/接收消息的方法等。
import uuid
from enum import Enum
from typing import Dict, Any, Optional, List
import asyncio
import logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class AgentStatus(Enum):
IDLE = "IDLE"
BUSY = "BUSY"
WAITING = "WAITING"
ERROR = "ERROR"
COMPLETED = "COMPLETED"
class MessageType(Enum):
REQUEST = "REQUEST"
RESPONSE = "RESPONSE"
STATUS_UPDATE = "STATUS_UPDATE"
ERROR = "ERROR"
COMMAND = "COMMAND"
RESULT = "RESULT"
class AgentMessage:
def __init__(self,
sender_id: str,
receiver_id: str,
message_type: MessageType,
content: Dict[str, Any],
task_id: Optional[str] = None):
self.message_id = str(uuid.uuid4())
self.sender_id = sender_id
self.receiver_id = receiver_id
self.message_type = message_type
self.content = content
self.timestamp = asyncio.get_event_loop().time()
self.task_id = task_id if task_id else str(uuid.uuid4()) # Every message can optionally link to a task
def to_dict(self) -> Dict[str, Any]:
return {
"message_id": self.message_id,
"sender_id": self.sender_id,
"receiver_id": self.receiver_id,
"message_type": self.message_type.value,
"content": self.content,
"timestamp": self.timestamp,
"task_id": self.task_id
}
@classmethod
def from_dict(cls, data: Dict[str, Any]):
return cls(
sender_id=data["sender_id"],
receiver_id=data["receiver_id"],
message_type=MessageType[data["message_type"]],
content=data["content"],
task_id=data.get("task_id")
)
class BaseAgent:
def __init__(self, agent_id: str, name: str, layer: str, message_broker: Any):
self.agent_id = agent_id
self.name = name
self.layer = layer
self.status = AgentStatus.IDLE
self.message_broker = message_broker # This will be our central communication hub
self.inbox = asyncio.Queue() # Each agent has its own inbox
logger.info(f"Agent {self.name} ({self.agent_id}) from {self.layer} layer initialized.")
async def send_message(self, receiver_id: str, message_type: MessageType, content: Dict[str, Any], task_id: Optional[str] = None):
message = AgentMessage(self.agent_id, receiver_id, message_type, content, task_id)
await self.message_broker.publish(message)
logger.debug(f"{self.name} sent message {message.message_id} to {receiver_id} for task {task_id}")
return message.message_id
async def receive_message(self) -> AgentMessage:
message = await self.inbox.get()
logger.debug(f"{self.name} received message {message.message_id} from {message.sender_id} for task {message.task_id}")
return message
async def handle_message(self, message: AgentMessage):
"""Placeholder for message handling logic, to be overridden by subclasses."""
logger.warning(f"{self.name} received unhandled message: {message.to_dict()}")
async def run(self):
"""Main loop for the agent."""
logger.info(f"{self.name} is starting its run loop.")
while True:
message = await self.receive_message()
await self.handle_message(message)
def update_status(self, new_status: AgentStatus):
self.status = new_status
logger.debug(f"{self.name} status updated to {new_status.value}")
def __str__(self):
return f"[{self.layer}] {self.name} ({self.agent_id[:8]}...)"2.2 消息代理 (Message Broker)
为了实现Agent之间的解耦通信，一个中央消息代理至关重要。它可以是基于Redis Pub/Sub、Kafka、RabbitMQ，或者为了简化演示，我们用一个内存中的异步队列。
class MessageBroker:
def __init__(self):
self.agents: Dict[str, BaseAgent] = {} # Map agent_id to agent instance
logger.info("MessageBroker initialized.")
def register_agent(self, agent: BaseAgent):
self.agents[agent.agent_id] = agent
logger.info(f"Agent {agent.name} registered with MessageBroker.")
async def publish(self, message: AgentMessage):
if message.receiver_id in self.agents:
await self.agents[message.receiver_id].inbox.put(message)
else:
logger.error(f"MessageBroker: Receiver {message.receiver_id} not found for message {message.message_id}")
async def broadcast(self, message: AgentMessage, exclude_sender: bool = True):
for agent_id, agent_instance in self.agents.items():
if exclude_sender and agent_id == message.sender_id:
continue
# Create a new message instance for each receiver to avoid modifying shared object
# For simplicity here, we'll just put the original message, but in a real system, clone it.
await agent_instance.inbox.put(message)
logger.debug(f"Message {message.message_id} broadcasted from {message.sender_id}.")
# Global message broker instance (or injected via DI)
global_message_broker = MessageBroker()3. 管理层：战略指挥与宏观掌控
管理层Agent是整个集群的“大脑”，负责与外部世界交互，接收高层次的任务请求，并将其转化为内部可处理的战略目标。它不关心具体执行细节，只关注任务的整体进度、资源分配和风险管理。
3.1 核心职责
- 任务入口：接收来自用户或外部系统的原始任务描述。
- 高层分解：将复杂任务分解为一系列大的战略目标，并分配给协调层。
- 资源审批：根据任务需求，分配计算资源、预算等。
- 全局监控：收集协调层的进度报告，提供整体视图，识别瓶颈或异常。
- 危机干预：当协调层报告重大问题时，进行决策和干预。
3.2 典型Agent：StrategicManager
StrategicManager是管理层的核心，负责任务的始发和终结。
class StrategicManager(BaseAgent):
def __init__(self, agent_id: str, name: str, message_broker: MessageBroker, coordination_agents: List[str]):
super().__init__(agent_id, name, "Management", message_broker)
self.active_tasks: Dict[str, Dict[str, Any]] = {} # Stores high-level task states
self.coordination_agents = coordination_agents # IDs of available coordination agents
self.next_coordinator_idx = 0
logger.info(f"StrategicManager {self.name} initialized with coordination agents: {coordination_agents}")
async def handle_message(self, message: AgentMessage):
if message.message_type == MessageType.REQUEST and message.content.get("type") == "NewProject":
await self.handle_new_project_request(message)
elif message.message_type == MessageType.RESULT and message.content.get("type") == "ProjectStatusUpdate":
await self.handle_project_status_update(message)
elif message.message_type == MessageType.ERROR:
logger.error(f"StrategicManager received error from {message.sender_id} for task {message.task_id}: {message.content.get('error_message')}")
# Potentially trigger a recovery or alert mechanism
else:
await super().handle_message(message)
async def handle_new_project_request(self, message: AgentMessage):
project_name = message.content["project_name"]
project_description = message.content["description"]
new_task_id = message.task_id # Use message's task_id as project ID
logger.info(f"StrategicManager received new project '{project_name}' (Task ID: {new_task_id})")
self.active_tasks[new_task_id] = {
"name": project_name,
"description": project_description,
"status": AgentStatus.BUSY,
"subtasks": {}, # To track subtasks delegated to coordination layer
"requester_id": message.sender_id # Who initiated this project
}
# Example: Simple decomposition into strategic goals
strategic_goals = self._decompose_project_to_strategic_goals(project_description)
# Delegate to a coordination agent (simple round-robin for now)
if not self.coordination_agents:
logger.error("No coordination agents available to delegate task.")
await self.send_message(message.sender_id, MessageType.ERROR,
{"error_message": "No coordination agents available"}, new_task_id)
return
coordinator_id = self.coordination_agents[self.next_coordinator_idx]
self.next_coordinator_idx = (self.next_coordinator_idx + 1) % len(self.coordination_agents)
logger.info(f"StrategicManager delegating project {project_name} to {coordinator_id}")
await self.send_message(
coordinator_id,
MessageType.COMMAND,
{
"type": "StartProjectPhase",
"project_id": new_task_id,
"project_name": project_name,
"strategic_goals": strategic_goals
},
task_id=new_task_id
)
self.active_tasks[new_task_id]["delegated_to"] = coordinator_id
self.update_status(AgentStatus.BUSY)
async def handle_project_status_update(self, message: AgentMessage):
project_id = message.task_id
status_update = message.content
if project_id in self.active_tasks:
self.active_tasks[project_id].update(status_update)
logger.info(f"StrategicManager received status update for project {project_id}: {status_update}")
if status_update.get("status") == AgentStatus.COMPLETED.value:
logger.info(f"Project {project_id} completed successfully!")
# Notify the original requester
requester_id = self.active_tasks[project_id].get("requester_id", "external_system")
await self.send_message(requester_id, MessageType.RESULT,
{"type": "ProjectCompletion", "project_id": project_id, "final_report": status_update.get("final_report")},
project_id)
del self.active_tasks[project_id]
if not self.active_tasks:
self.update_status(AgentStatus.IDLE)
elif status_update.get("status") == AgentStatus.ERROR.value:
logger.error(f"Project {project_id} reported an error: {status_update.get('error_message')}")
# Trigger specific error handling, potentially re-assign or escalate
else:
logger.warning(f"StrategicManager received status update for unknown project ID: {project_id}")
def _decompose_project_to_strategic_goals(self, project_description: str) -> List[Dict[str, Any]]:
# In a real system, this would involve an LLM call or a sophisticated planning algorithm.
# For demonstration, we'll hardcode some logical steps.
if "e-commerce platform" in project_description.lower():
return [
{"goal_name": "Design Core Architecture", "priority": 1},
{"goal_name": "Implement User Management", "priority": 2},
{"goal_name": "Develop Product Catalog", "priority": 2},
{"goal_name": "Integrate Payment Gateway", "priority": 3},
{"goal_name": "Build Frontend Interface", "priority": 4},
{"goal_name": "Deploy & Test System", "priority": 5},
]
return [{"goal_name": "Generic Task Completion", "priority": 1}]4. 协调层：战术部署与流程管理
协调层Agent是管理层与执行层之间的桥梁。它们接收来自管理层的高层目标，将其分解为一系列具体的、可执行的子任务，并分配给合适的执行Agent。它们还要负责跟踪子任务的进度，处理任务间的依赖关系，并在必要时进行局部冲突解决，最终将聚合的结果汇报给管理层。
4.1 核心职责
- 任务分解：将战略目标细化为操作性强的子任务。
- Agent调度：根据子任务的类型和执行Agent的能力进行匹配和分配。
- 进度跟踪：监控执行层Agent的工作状态和子任务完成情况。
- 依赖管理：确保有前置依赖的子任务在依赖完成后才开始执行。
- 结果聚合：收集所有子任务的输出，整合成协调层级别的报告。
- 局部决策/冲突解决：处理执行层Agent报告的局部问题，如资源竞争、轻微错误等。
4.2 典型Agent：TaskCoordinator
TaskCoordinator是协调层的核心，一个项目或一个大的战略目标可以由一个或多个TaskCoordinator来负责。
class TaskCoordinator(BaseAgent):
def __init__(self, agent_id: str, name: str, message_broker: MessageBroker, execution_agents: List[str]):
super().__init__(agent_id, name, "Coordination", message_broker)
self.managed_projects: Dict[str, Dict[str, Any]] = {} # project_id -> project_state
self.execution_agents = execution_agents # IDs of available execution agents
self.next_executor_idx = 0 # Simple round-robin for demonstration
logger.info(f"TaskCoordinator {self.name} initialized with execution agents: {execution_agents}")
async def handle_message(self, message: AgentMessage):
if message.message_type == MessageType.COMMAND and message.content.get("type") == "StartProjectPhase":
await self.handle_start_project_phase(message)
elif message.message_type == MessageType.RESULT and message.content.get("type") == "SubtaskCompletion":
await self.handle_subtask_completion(message)
elif message.message_type == MessageType.ERROR and message.content.get("type") == "SubtaskFailure":
await self.handle_subtask_failure(message)
else:
await super().handle_message(message)
async def handle_start_project_phase(self, message: AgentMessage):
project_id = message.task_id
project_name = message.content["project_name"]
strategic_goals = message.content["strategic_goals"]
logger.info(f"TaskCoordinator {self.name} starting project phase for '{project_name}' (Project ID: {project_id})")
self.managed_projects[project_id] = {
"name": project_name,
"strategic_goals": strategic_goals,
"status": AgentStatus.BUSY,
"subtasks": {}, # subtask_id -> subtask_state
"completed_subtasks_count": 0,
"total_subtasks_count": 0,
"parent_manager_id": message.sender_id
}
self.update_status(AgentStatus.BUSY)
# Decompose strategic goals into concrete subtasks
for goal in strategic_goals:
subtasks = self._decompose_strategic_goal_to_subtasks(goal["goal_name"], project_id)
for subtask_data in subtasks:
subtask_id = str(uuid.uuid4()) # Generate unique ID for each subtask
self.managed_projects[project_id]["subtasks"][subtask_id] = {
"task_name": subtask_data["name"],
"description": subtask_data["description"],
"status": AgentStatus.IDLE,
"assigned_to": None,
"result": None,
"dependencies": subtask_data.get("dependencies", []) # Simplified dependency: just names
}
self.managed_projects[project_id]["total_subtasks_count"] += 1
await self._schedule_subtasks(project_id) # Start scheduling the decomposed subtasks
async def _schedule_subtasks(self, project_id: str):
project_state = self.managed_projects[project_id]
ready_to_schedule = []
# Find subtasks that are IDLE and have all dependencies met
for subtask_id, subtask_state in project_state["subtasks"].items():
if subtask_state["status"] == AgentStatus.IDLE:
dependencies_met = True
for dep_name in subtask_state["dependencies"]:
found_dep = False
for existing_subtask_id, existing_subtask_state in project_state["subtasks"].items():
if existing_subtask_state["task_name"] == dep_name and
existing_subtask_state["status"] == AgentStatus.COMPLETED:
found_dep = True
break
if not found_dep:
dependencies_met = False
break
if dependencies_met:
ready_to_schedule.append(subtask_id)
for subtask_id in ready_to_schedule:
subtask_state = project_state["subtasks"][subtask_id]
executor_id = self._select_executor_agent(subtask_state["task_name"]) # Intelligent selection needed
if executor_id:
subtask_state["assigned_to"] = executor_id
subtask_state["status"] = AgentStatus.BUSY
logger.info(f"TaskCoordinator {self.name} assigning subtask '{subtask_state['task_name']}' ({subtask_id}) to {executor_id} for project {project_id}")
await self.send_message(
executor_id,
MessageType.COMMAND,
{
"type": "ExecuteSubtask",
"project_id": project_id,
"subtask_id": subtask_id,
"task_name": subtask_state["task_name"],
"description": subtask_state["description"]
},
task_id=project_id # Link to the main project task_id
)
else:
logger.warning(f"No suitable executor found for subtask '{subtask_state['task_name']}' ({subtask_id}). Retrying later.")
# Implement retry logic or re-evaluation
async def handle_subtask_completion(self, message: AgentMessage):
project_id = message.task_id
subtask_id = message.content["subtask_id"]
result = message.content["result"]
sender_id = message.sender_id
if project_id in self.managed_projects and subtask_id in self.managed_projects[project_id]["subtasks"]:
subtask_state = self.managed_projects[project_id]["subtasks"][subtask_id]
subtask_state["status"] = AgentStatus.COMPLETED
subtask_state["result"] = result
self.managed_projects[project_id]["completed_subtasks_count"] += 1
logger.info(f"Subtask '{subtask_state['task_name']}' ({subtask_id}) completed by {sender_id}. Result: {result[:50]}...") # Truncate for log
# Re-schedule tasks as dependencies might be met now
await self._schedule_subtasks(project_id)
# Check if all subtasks are completed for this project
if self.managed_projects[project_id]["completed_subtasks_count"] == self.managed_projects[project_id]["total_subtasks_count"]:
logger.info(f"All subtasks for project {project_id} completed.")
final_report = self._aggregate_project_results(project_id)
self.managed_projects[project_id]["status"] = AgentStatus.COMPLETED
# Report back to the StrategicManager
await self.send_message(
self.managed_projects[project_id]["parent_manager_id"],
MessageType.RESULT,
{
"type": "ProjectStatusUpdate",
"status": AgentStatus.COMPLETED.value,
"final_report": final_report
},
task_id=project_id
)
del self.managed_projects[project_id] # Clean up
if not self.managed_projects:
self.update_status(AgentStatus.IDLE)
else:
logger.warning(f"TaskCoordinator received completion for unknown subtask {subtask_id} or project {project_id}.")
async def handle_subtask_failure(self, message: AgentMessage):
project_id = message.task_id
subtask_id = message.content["subtask_id"]
error_message = message.content["error_message"]
sender_id = message.sender_id
if project_id in self.managed_projects and subtask_id in self.managed_projects[project_id]["subtasks"]:
subtask_state = self.managed_projects[project_id]["subtasks"][subtask_id]
subtask_state["status"] = AgentStatus.ERROR
subtask_state["error"] = error_message
logger.error(f"Subtask '{subtask_state['task_name']}' ({subtask_id}) failed by {sender_id}: {error_message}")
# Implement retry, re-assignment, or escalation logic
# For now, we'll mark the whole project as error and report up
self.managed_projects[project_id]["status"] = AgentStatus.ERROR
await self.send_message(
self.managed_projects[project_id]["parent_manager_id"],
MessageType.ERROR,
{
"type": "ProjectStatusUpdate",
"status": AgentStatus.ERROR.value,
"error_message": f"Subtask '{subtask_state['task_name']}' failed: {error_message}"
},
task_id=project_id
)
del self.managed_projects[project_id] # Clean up failed project
if not self.managed_projects:
self.update_status(AgentStatus.IDLE)
else:
logger.warning(f"TaskCoordinator received error for unknown subtask {subtask_id} or project {project_id}.")
def _decompose_strategic_goal_to_subtasks(self, goal_name: str, project_id: str) -> List[Dict[str, Any]]:
# This is where an LLM or a rule-based system would shine.
# It takes a high-level goal (e.g., "Implement User Management") and breaks it into micro-tasks.
if goal_name == "Implement User Management":
return [
{"name": "Design User Database Schema", "description": "Define tables for users, roles, permissions.", "type": "DatabaseDesign"},
{"name": "Develop User Registration API", "description": "Write Python code for user signup.", "type": "CodeGeneration", "dependencies": ["Design User Database Schema"]},
{"name": "Develop User Login API", "description": "Write Python code for user authentication.", "type": "CodeGeneration", "dependencies": ["Design User Database Schema", "Develop User Registration API"]},
{"name": "Implement Password Reset Flow", "description": "Logic for forgotten passwords.", "type": "CodeGeneration", "dependencies": ["Develop User Login API"]},
]
elif goal_name == "Develop Product Catalog":
return [
{"name": "Design Product Database Schema", "description": "Define tables for products, categories, inventory.", "type": "DatabaseDesign"},
{"name": "Develop Product Listing API", "description": "API to fetch product lists.", "type": "CodeGeneration", "dependencies": ["Design Product Database Schema"]},
{"name": "Develop Product Detail API", "description": "API to fetch single product details.", "type": "CodeGeneration", "dependencies": ["Design Product Database Schema", "Develop Product Listing API"]},
]
# ... more decomposition logic for other goals
return [{"name": f"Execute {goal_name}", "description": f"Generic execution for {goal_name}", "type": "Generic"}]
def _select_executor_agent(self, subtask_name: str) -> Optional[str]:
# More sophisticated logic here:
# - Check agent capabilities (e.g., "CodeGenerator" for code tasks)
# - Check agent load/availability
# - Prioritize agents based on past performance
# For now, a simple round-robin that tries to match keywords.
available_executors = [self.execution_agents[i] for i in range(len(self.execution_agents)) if self.message_broker.agents[self.execution_agents[i]].status == AgentStatus.IDLE]
if not available_executors:
return None
# Simple capability matching
if "code" in subtask_name.lower() or "develop" in subtask_name.lower():
for executor_id in available_executors:
if "code_generator" in self.message_broker.agents[executor_id].name.lower():
return executor_id
if "database" in subtask_name.lower() or "schema" in subtask_name.lower():
for executor_id in available_executors:
if "data_engineer" in self.message_broker.agents[executor_id].name.lower():
return executor_id
# Fallback to simple round-robin if no specific match or no specialized agent available
if available_executors:
executor_id = available_executors[self.next_executor_idx % len(available_executors)]
self.next_executor_idx = (self.next_executor_idx + 1) % len(available_executors)
return executor_id
return None
def _aggregate_project_results(self, project_id: str) -> Dict[str, Any]:
aggregated_results = {
"project_name": self.managed_projects[project_id]["name"],
"final_status": self.managed_projects[project_id]["status"].value,
"subtask_results": {}
}
for subtask_id, subtask_state in self.managed_projects[project_id]["subtasks"].items():
aggregated_results["subtask_results"][subtask_state["task_name"]] = {
"status": subtask_state["status"].value,
"result": subtask_state["result"] if subtask_state["result"] else subtask_state.get("error")
}
return aggregated_results5. 执行层：原子操作与专业技能
执行层Agent是集群的“劳动力”，它们接收来自协调层的具体指令，执行原子性的任务，并将其结果返回。每个执行Agent通常专注于一项或一组紧密相关的技能，例如代码生成、数据分析、API调用、文件操作等。它们是与底层工具、外部API或计算资源直接交互的Agent。
5.1 核心职责
- 任务执行：根据接收到的指令，执行特定的操作。
- 工具调用：封装和调用外部工具（如编译器、数据库客户端、REST API）。
- 结果报告：将任务执行的结果（成功、失败、输出数据）准确地汇报给协调层。
- 错误处理：处理执行过程中遇到的局部错误，并报告给协调层。
5.2 典型Agent：CodeGeneratorAgent, DataEngineerAgent
我们以一个CodeGeneratorAgent和一个DataEngineerAgent为例。
import time
class CodeGeneratorAgent(BaseAgent):
def __init__(self, agent_id: str, name: str, message_broker: MessageBroker):
super().__init__(agent_id, name, "Execution", message_broker)
self.supported_tasks = ["CodeGeneration", "Develop User Registration API", "Develop User Login API", "Implement Password Reset Flow", "Develop Product Listing API", "Develop Product Detail API"]
logger.info(f"CodeGeneratorAgent {self.name} initialized. Supports: {', '.join(self.supported_tasks)}")
async def handle_message(self, message: AgentMessage):
if message.message_type == MessageType.COMMAND and message.content.get("type") == "ExecuteSubtask":
await self.execute_code_generation_task(message)
else:
await super().handle_message(message)
async def execute_code_generation_task(self, message: AgentMessage):
project_id = message.task_id
subtask_id = message.content["subtask_id"]
task_name = message.content["task_name"]
description = message.content["description"]
if task_name not in self.supported_tasks:
error_msg = f"CodeGeneratorAgent does not support task: {task_name}"
logger.error(error_msg)
await self.send_message(message.sender_id, MessageType.ERROR,
{"type": "SubtaskFailure", "subtask_id": subtask_id, "error_message": error_msg},
project_id)
return
self.update_status(AgentStatus.BUSY)
logger.info(f"CodeGeneratorAgent {self.name} executing subtask '{task_name}' ({subtask_id}) for project {project_id}")
try:
# Simulate code generation with an LLM call or complex logic
await asyncio.sleep(2) # Simulate work
generated_code = self._simulate_code_generation(task_name, description)
result_content = {
"type": "SubtaskCompletion",
"subtask_id": subtask_id,
"result": {"code": generated_code, "language": "Python"}
}
await self.send_message(message.sender_id, MessageType.RESULT, result_content, project_id)
logger.info(f"CodeGeneratorAgent {self.name} completed subtask '{task_name}' ({subtask_id})")
except Exception as e:
error_msg = f"Error generating code for {task_name}: {e}"
logger.error(error_msg)
await self.send_message(message.sender_id, MessageType.ERROR,
{"type": "SubtaskFailure", "subtask_id": subtask_id, "error_message": error_msg},
project_id)
finally:
self.update_status(AgentStatus.IDLE)
def _simulate_code_generation(self, task_name: str, description: str) -> str:
# In a real scenario, this would involve calling an LLM API (e.g., OpenAI, Anthropic)
# or using code generation tools based on the task description.
if "user registration" in task_name.lower():
return f"def register_user(username, password):n # Code for user registration based on: {description}n return {{'status': 'success', 'user_id': 'gen_user_123'}}n"
elif "user login" in task_name.lower():
return f"def login_user(username, password):n # Code for user login based on: {description}n return {{'status': 'success', 'token': 'jwt_token_xyz'}}n"
elif "password reset" in task_name.lower():
return f"def reset_password(email):n # Code for password reset based on: {description}n return {{'status': 'success', 'message': 'Reset email sent'}}n"
elif "product listing" in task_name.lower():
return f"def get_product_list(category=None):n # Code for product listing API based on: {description}n return {{'products': ['item1', 'item2']}}n"
return f"# Placeholder code for '{task_name}': {description}n"
class DataEngineerAgent(BaseAgent):
def __init__(self, agent_id: str, name: str, message_broker: MessageBroker):
super().__init__(agent_id, name, "Execution", message_broker)
self.supported_tasks = ["DatabaseDesign", "Design User Database Schema", "Design Product Database Schema"]
logger.info(f"DataEngineerAgent {self.name} initialized. Supports: {', '.join(self.supported_tasks)}")
async def handle_message(self, message: AgentMessage):
if message.message_type == MessageType.COMMAND and message.content.get("type") == "ExecuteSubtask":
await self.execute_database_design_task(message)
else:
await super().handle_message(message)
async def execute_database_design_task(self, message: AgentMessage):
project_id = message.task_id
subtask_id = message.content["subtask_id"]
task_name = message.content["task_name"]
description = message.content["description"]
if task_name not in self.supported_tasks:
error_msg = f"DataEngineerAgent does not support task: {task_name}"
logger.error(error_msg)
await self.send_message(message.sender_id, MessageType.ERROR,
{"type": "SubtaskFailure", "subtask_id": subtask_id, "error_message": error_msg},
project_id)
return
self.update_status(AgentStatus.BUSY)
logger.info(f"DataEngineerAgent {self.name} executing subtask '{task_name}' ({subtask_id}) for project {project_id}")
try:
# Simulate database schema design
await asyncio.sleep(1.5) # Simulate work
schema_sql = self._simulate_schema_design(task_name, description)
result_content = {
"type": "SubtaskCompletion",
"subtask_id": subtask_id,
"result": {"schema_type": "SQL", "sql_script": schema_sql}
}
await self.send_message(message.sender_id, MessageType.RESULT, result_content, project_id)
logger.info(f"DataEngineerAgent {self.name} completed subtask '{task_name}' ({subtask_id})")
except Exception as e:
error_msg = f"Error designing database for {task_name}: {e}"
logger.error(error_msg)
await self.send_message(message.sender_id, MessageType.ERROR,
{"type": "SubtaskFailure", "subtask_id": subtask_id, "error_message": error_msg},
project_id)
finally:
self.update_status(AgentStatus.IDLE)
def _simulate_schema_design(self, task_name: str, description: str) -> str:
if "user database schema" in task_name.lower():
return "CREATE TABLE users (id INT PRIMARY KEY, username VARCHAR(255), password_hash VARCHAR(255), email VARCHAR(255));nCREATE TABLE roles (id INT PRIMARY KEY, role_name VARCHAR(255));nCREATE TABLE user_roles (user_id INT, role_id INT, PRIMARY KEY (user_id, role_id));"
elif "product database schema" in task_name.lower():
return "CREATE TABLE products (id INT PRIMARY KEY, name VARCHAR(255), description TEXT, price DECIMAL(10,2));nCREATE TABLE categories (id INT PRIMARY KEY, name VARCHAR(255));nCREATE TABLE product_categories (product_id INT, category_id INT, PRIMARY KEY (product_id, category_id));"
return f"-- Generic SQL schema for '{task_name}': {description}n"6. 集群编排与生命周期管理
构建了各个层级的Agent之后，我们还需要一个机制来启动、管理和监控整个集群。
6.1 Agent注册与发现
所有Agent在启动时都应该向消息代理注册，以便其他Agent能够找到并与之通信。在分布式环境中，这可能涉及服务发现机制（如Consul, etcd）。
6.2 异步运行与调度
Python的asyncio是实现Agent并发运行的关键。每个Agent的run方法都在一个独立的协程中运行。
async def main():
# 1. Instantiate Message Broker
broker = global_message_broker
# 2. Instantiate Agents
# Execution Layer Agents
executor_code_gen_1 = CodeGeneratorAgent("exe_cg_1", "CodeGenius_1", broker)
executor_data_eng_1 = DataEngineerAgent("exe_de_1", "DataArchitect_1", broker)
executor_code_gen_2 = CodeGeneratorAgent("exe_cg_2", "CodeGenius_2", broker)
# Coordination Layer Agents
coordinator_1 = TaskCoordinator("coord_1", "ProjectLead_A", broker,
[executor_code_gen_1.agent_id, executor_data_eng_1.agent_id, executor_code_gen_2.agent_id])
coordinator_2 = TaskCoordinator("coord_2", "ProjectLead_B", broker,
[executor_code_gen_1.agent_id, executor_data_eng_1.agent_id, executor_code_gen_2.agent_id])
# Management Layer Agent
manager_agent = StrategicManager("mgmt_1", "CEO_Strategist", broker,
[coordinator_1.agent_id, coordinator_2.agent_id])
# 3. Register Agents with Message Broker
broker.register_agent(executor_code_gen_1)
broker.register_agent(executor_data_eng_1)
broker.register_agent(executor_code_gen_2)
broker.register_agent(coordinator_1)
broker.register_agent(coordinator_2)
broker.register_agent(manager_agent)
# 4. Start all agents in the background
agent_tasks = [
asyncio.create_task(manager_agent.run()),
asyncio.create_task(coordinator_1.run()),
asyncio.create_task(coordinator_2.run()),
asyncio.create_task(executor_code_gen_1.run()),
asyncio.create_task(executor_data_eng_1.run()),
asyncio.create_task(executor_code_gen_2.run()),
]
# 5. Simulate an external request to the StrategicManager
external_request_task = asyncio.create_task(
manager_agent.send_message(
manager_agent.agent_id, # Self-send for simplicity, or from a dedicated "User" agent
MessageType.REQUEST,
{
"type": "NewProject",
"project_name": "E-commerce Platform Development",
"description": "Develop a full-stack e-commerce platform with user management, product catalog, and payment integration."
},
task_id=str(uuid.uuid4()) # Unique project ID from external
)
)
# Keep the main loop running to allow agents to process messages
# In a real application, you'd have more sophisticated termination logic.
await asyncio.sleep(60) # Let the agents run for a minute
for task in agent_tasks:
task.cancel() # Gracefully stop agents
await asyncio.gather(*agent_tasks, return_exceptions=True) # Await their termination
logger.info("Simulation completed.")
if __name__ == "__main__":
asyncio.run(main())运行上述main函数，您将看到日志中清晰地展现出任务如何从StrategicManager下发，经过TaskCoordinator分解调度，最终由CodeGeneratorAgent和DataEngineerAgent完成，再层层上报结果的全过程。
7. 健壮性与可扩展性考量
构建超大型Agent集群，必须深入思考其健壮性和应对未来挑战的能力。
7.1 状态管理与持久化
Agent的内部状态是其行为的基础。在Agent重启或系统故障时，状态必须能够恢复。
- 数据库：将Agent的关键状态（如
active_tasks,managed_projects）持久化到PostgreSQL、MongoDB等数据库中。 - 事件溯源（Event Sourcing）：记录所有状态变更事件，通过重放事件来重建Agent状态，便于审计和调试。
7.2 故障恢复与容错机制
- 重试机制：执行层Agent失败时，协调层应具备重试能力，或将任务重新分配给其他Agent。
- 心跳与监控：Agent应定期发送心跳信号，监控系统可据此检测Agent是否存活。
- 隔离性：单个Agent的崩溃不应影响整个集群。通过进程隔离、容器化（Docker, Kubernetes）实现。
- 管理层降级：当协调层Agent持续失败时，管理层应能介入，重新规划或报警。
7.3 负载均衡与弹性伸缩
- 动态调度：协调层在分配任务时，应考虑执行层Agent的当前负载和可用资源。
- 水平扩展：当任务量增加时，可以动态启动更多的协调层或执行层Agent实例。容器编排工具（如Kubernetes）在此发挥关键作用，可以根据CPU/内存使用率或队列长度自动伸缩Agent Pod。
7.4 安全性
- 认证与授权：Agent间的通信应进行身份验证，确保只有授权的Agent才能发送或接收特定消息。
- 数据隔离：确保不同任务或不同租户的数据不会混淆或泄露。
- 输入/输出校验：Agent处理外部输入或产生输出时，应进行严格的校验，防止恶意注入或数据污染。
8. 未来展望与进阶话题
今天我们构建了一个分层Agent集群的蓝图，并提供了核心代码示例。但这仅仅是开始。未来，我们可以探索更多高级特性：
- 自适应能力：Agent根据环境变化或任务特性动态调整其行为和策略。
- 学习与优化：通过强化学习或元学习，Agent能够从过去的经验中学习，优化任务分解、调度和执行策略。
- 人类在环（Human-in-the-Loop）：在关键决策点或遇到复杂问题时，引入人类专家进行干预和指导。
- 更智能的Agent选择：基于Agent的技能图谱、历史表现、实时负载等进行高级匹配。
- 分布式消息系统集成：用Kafka或RabbitMQ替代内存消息代理，实现真正的分布式、高吞吐、持久化通信。
构建一个具备三层架构的超大型Agent集群，是迈向通用人工智能和解决现实世界复杂问题的关键一步。它不仅仅是技术上的挑战，更是对我们如何设计、管理和与智能系统协作的深刻思考。通过清晰的职责划分、健壮的通信机制和完善的生命周期管理，我们能够解锁Agent集群的巨大潜力，让它们在更广阔的领域发挥价值。这个分层团队模型，将成为我们驾驭AI复杂性，实现大规模智能协作的强大工具。
