SaaS 行业观察：AI Agent 间的协作与协商机制

{
  "message_id": "uuid-v4",
  "sender": "agent-id",
  "receiver": "agent-id",
  "timestamp": "ISO-8601",
  "performative": "request|inform|query|propose|accept|reject",
  "content": {
    "ontology": "domain-ontology-uri",
    "language": "natural-language|formal-language",
    "content": "message-content"
  },
  "protocol": "interaction-protocol-id",
  "conversation_id": "conversation-uuid",
  "reply_to": "message-id-optional"
}

{
  "ontology": "saaS-task-ontology-v2",
  "classes": {
    "Task": {
      "properties": ["id", "description", "deadline", "priority", "dependencies"],
      "subclasses": ["ComputationalTask", "DataProcessingTask", "DecisionTask"]
    },
    "Agent": {
      "properties": ["id", "capabilities", "reputation", "availability"],
      "subclasses": ["SpecialistAgent", "GeneralistAgent", "CoordinatorAgent"]
    },
    "Resource": {
      "properties": ["id", "type", "capacity", "cost"],
      "subclasses": ["ComputationalResource", "DataResource", "HumanResource"]
    }
  },
  "relations": {
    "canPerform": ["Agent", "Task"],
    "requires": ["Task", "Resource"],
    "dependsOn": ["Task", "Task"]
  }
}

def map_semantics(source_ontology, target_ontology, concept):
    """
    将概念从一个本体论映射到另一个本体论
    """
    mapping_table = load_mapping_table(source_ontology, target_ontology)
    
    if concept in mapping_table:
        return mapping_table[concept]
    else:
        # 使用语义相似度计算最佳匹配
        candidates = find_similar_concepts(concept, target_ontology)
        best_match = max(candidates, key=lambda c: semantic_similarity(concept, c))
        return best_match

class HierarchicalTaskNetwork:
    def __init__(self):
        self.methods = {}  # 任务分解方法
    
    def decompose_task(self, task, state):
        """
        将复杂任务分解为子任务
        """
        if task.is_primitive():
            return [task]
        
        # 查找适用的分解方法
        applicable_methods = [m for m in self.methods.values() 
                            if m.is_applicable(task, state)]
        
        if not applicable_methods:
            raise NoApplicableMethodError(task)
        
        # 选择最佳方法（基于启发式或学习）
        best_method = self.select_best_method(applicable_methods, task, state)
        
        # 应用方法分解任务
        subtasks = best_method.decompose(task, state)
        
        # 递归分解子任务
        plan = []
        for subtask in subtasks:
            subplan = self.decompose_task(subtask, state)
            plan.extend(subplan)
        
        return plan
    
    def allocate_tasks(self, tasks, agents):
        """
        将任务分配给 Agent
        """
        # 构建能力矩阵
        capability_matrix = self.build_capability_matrix(tasks, agents)
        
        # 使用匈牙利算法或拍卖机制进行最优分配
        allocation = hungarian_algorithm(capability_matrix)
        
        return allocation

class MarketBasedAllocation:
    def __init__(self):
        self.agents = []
        self.tasks = []
    
    def run_auction(self, task):
        """
        对任务进行拍卖
        """
        bids = []
        
        # 收集所有 Agent 的投标
        for agent in self.agents:
            if agent.can_perform(task):
                bid = agent.calculate_bid(task)
                bids.append({
                    'agent': agent,
                    'price': bid['price'],
                    'quality': bid['quality'],
                    'time': bid['estimated_time']
                })
        
        # 评估投标（考虑价格、质量、时间）
        best_bid = self.evaluate_bids(bids, task)
        
        # 授予合同
        winning_agent = best_bid['agent']
        winning_agent.accept_task(task, best_bid['price'])
        
        return winning_agent, best_bid
    
    def evaluate_bids(self, bids, task):
        """
        评估投标，选择最佳投标
        """
        # 多目标优化：最小化价格，最大化质量，最小化时间
        scored_bids = []
        
        for bid in bids:
            # 归一化分数
            price_score = 1 - normalize(bid['price'], min_price, max_price)
            quality_score = normalize(bid['quality'], min_quality, max_quality)
            time_score = 1 - normalize(bid['time'], min_time, max_time)
            
            # 加权总分
            total_score = (0.4 * price_score + 
                          0.4 * quality_score + 
                          0.2 * time_score)
            
            scored_bids.append({
                'bid': bid,
                'score': total_score
            })
        
        # 返回最高分投标
        return max(scored_bids, key=lambda x: x['score'])['bid']

class ConflictDetector:
    def __init__(self):
        self.conflict_types = [
            'resource_conflict',
            'goal_conflict',
            'belief_conflict',
            'action_conflict'
        ]
    
    def detect_conflicts(self, agent_plans):
        """
        检测多个 Agent 计划之间的冲突
        """
        conflicts = []
        
        # 检查资源冲突
        resource_conflicts = self.check_resource_conflicts(agent_plans)
        conflicts.extend(resource_conflicts)
        
        # 检查目标冲突
        goal_conflicts = self.check_goal_conflicts(agent_plans)
        conflicts.extend(goal_conflicts)
        
        # 检查信念冲突
        belief_conflicts = self.check_belief_conflicts(agent_plans)
        conflicts.extend(belief_conflicts)
        
        # 检查动作冲突
        action_conflicts = self.check_action_conflicts(agent_plans)
        conflicts.extend(action_conflicts)
        
        return conflicts
    
    def check_resource_conflicts(self, agent_plans):
        """
        检查资源冲突：多个 Agent 同时使用同一资源
        """
        conflicts = []
        resource_usage = {}
        
        for agent_id, plan in agent_plans.items():
            for action in plan.actions:
                for resource in action.required_resources:
                    if resource.id in resource_usage:
                        # 检查时间重叠
                        if self.time_overlap(resource_usage[resource.id], action):
                            conflicts.append({
                                'type': 'resource_conflict',
                                'resource': resource.id,
                                'agents': [resource_usage[resource.id]['agent'], agent_id],
                                'time': action.time_window
                            })
                    else:
                        resource_usage[resource.id] = {
                            'agent': agent_id,
                            'action': action
                        }
        
        return conflicts

class ConflictResolver:
    def __init__(self):
        self.strategies = {
            'resource_conflict': self.resolve_resource_conflict,
            'goal_conflict': self.resolve_goal_conflict,
            'belief_conflict': self.resolve_belief_conflict,
            'action_conflict': self.resolve_action_conflict
        }
    
    def resolve_conflict(self, conflict):
        """
        解决冲突
        """
        strategy = self.strategies.get(conflict['type'])
        if strategy:
            return strategy(conflict)
        else:
            raise UnknownConflictTypeError(conflict['type'])
    
    def resolve_resource_conflict(self, conflict):
        """
        解决资源冲突
        """
        resource = conflict['resource']
        agents = conflict['agents']
        
        # 策略 1：优先级排序
        priorities = {agent: get_priority(agent) for agent in agents}
        sorted_agents = sorted(agents, key=lambda a: priorities[a], reverse=True)
        
        # 高优先级 Agent 先使用资源
        resolution = {
            'strategy': 'priority_based',
            'order': sorted_agents,
            'resource': resource
        }
        
        # 策略 2：时间分割
        time_slots = self.split_time_slots(conflict['time'], len(agents))
        resolution['time_slots'] = {
            agent: slot for agent, slot in zip(sorted_agents, time_slots)
        }
        
        # 策略 3：资源替代
        alternative_resources = find_alternative_resources(resource)
        if alternative_resources:
            resolution['alternatives'] = alternative_resources
        
        return resolution
    
    def resolve_goal_conflict(self, conflict):
        """
        解决目标冲突：通过协商达成共识
        """
        agents = conflict['agents']
        goals = conflict['goals']
        
        # 启动协商协议
        negotiation = NegotiationProtocol(agents, goals)
        
        # 多轮协商
        for round_num in range(max_rounds):
            proposals = negotiation.collect_proposals()
            
            # 检查是否达成一致
            if negotiation.check_consensus(proposals):
                return {
                    'strategy': 'negotiation',
                    'agreement': negotiation.get_agreement()
                }
            
            # 交换反提议
            negotiation.exchange_counter_proposals(proposals)
        
        # 协商失败，使用仲裁
        arbitrator = get_arbitrator()
        decision = arbitrator.arbitrate(goals)
        
        return {
            'strategy': 'arbitration',
            'decision': decision
        }

class SharedExperienceLibrary:
    def __init__(self):
        self.experiences = []
        self.similarity_threshold = 0.8
    
    def store_experience(self, experience):
        """
        存储协作经验
        """
        experience_record = {
            'id': generate_uuid(),
            'timestamp': datetime.now(),
            'agents': experience['agents'],
            'task': experience['task'],
            'context': experience['context'],
            'actions': experience['actions'],
            'outcome': experience['outcome'],
            'success': experience['success'],
            'lessons_learned': experience['lessons_learned']
        }
        
        self.experiences.append(experience_record)
        
        # 更新索引
        self.update_index(experience_record)
    
    def retrieve_similar_experiences(self, current_context, top_k=5):
        """
        检索相似的历史经验
        """
        similarities = []
        
        for exp in self.experiences:
            similarity = self.calculate_similarity(current_context, exp['context'])
            if similarity >= self.similarity_threshold:
                similarities.append({
                    'experience': exp,
                    'similarity': similarity
                })
        
        # 按相似度排序
        similarities.sort(key=lambda x: x['similarity'], reverse=True)
        
        # 返回前 k 个
        return similarities[:top_k]
    
    def learn_from_experiences(self, experiences):
        """
        从经验中学习，改进协作策略
        """
        # 提取成功模式
        successful_patterns = self.extract_patterns(
            [exp for exp in experiences if exp['success']]
        )
        
        # 提取失败模式
        failure_patterns = self.extract_patterns(
            [exp for exp in experiences if not exp['success']]
        )
        
        # 更新协作策略
        self.update_collaboration_strategies(successful_patterns, failure_patterns)

class FederatedCollaborationLearning:
    def __init__(self):
        self.local_models = {}
        self.global_model = None
    
    def train_local_model(self, agent_id, local_data):
        """
        训练本地协作模型
        """
        local_model = CollaborationModel()
        local_model.train(local_data)
        
        self.local_models[agent_id] = local_model
        
        return local_model
    
    def aggregate_models(self):
        """
        聚合所有本地模型为全局模型
        """
        model_weights = []
        
        for agent_id, model in self.local_models.items():
            # 根据 Agent 的数据量和性能加权
            weight = self.calculate_weight(agent_id)
            model_weights.append((model.get_parameters(), weight))
        
        # 加权平均聚合
        global_parameters = self.weighted_average(model_weights)
        
        # 更新全局模型
        self.global_model = CollaborationModel()
        self.global_model.set_parameters(global_parameters)
        
        return self.global_model
    
    def distribute_global_model(self):
        """
        将全局模型分发给所有 Agent
        """
        for agent_id in self.local_models.keys():
            # 发送全局模型参数
            send_to_agent(agent_id, self.global_model.get_parameters())
            
            # Agent 可以选择接受或微调
            self.local_models[agent_id].update_from_global(
                self.global_model.get_parameters()
            )

class CustomerOnboardingWorkflow:
    def __init__(self):
        self.agents = {
            'crm_agent': CRMAgent(),
            'billing_agent': BillingAgent(),
            'provisioning_agent': ProvisioningAgent(),
            'training_agent': TrainingAgent(),
            'support_agent': SupportAgent()
        }
        
        self.coordinator = WorkflowCoordinator(self.agents)
    
    def execute_onboarding(self, customer_data):
        """
        执行客户入职流程
        """
        # 阶段 1：CRM 注册
        crm_task = Task(
            type='customer_registration',
            data=customer_data,
            assigned_to='crm_agent'
        )
        crm_result = self.coordinator.execute_task(crm_task)
        
        # 阶段 2：并行执行计费和资源预配
        billing_task = Task(
            type='setup_billing',
            data={'customer_id': crm_result['customer_id']},
            assigned_to='billing_agent'
        )
        
        provisioning_task = Task(
            type='provision_resources',
            data={'customer_id': crm_result['customer_id']},
            assigned_to='provisioning_agent'
        )
        
        # 并行执行
        billing_result, provisioning_result = self.coordinator.execute_parallel(
            [billing_task, provisioning_task]
        )
        
        # 阶段 3：安排培训
        training_task = Task(
            type='schedule_training',
            data={
                'customer_id': crm_result['customer_id'],
                'resources': provisioning_result['resources']
            },
            assigned_to='training_agent'
        )
        training_result = self.coordinator.execute_task(training_task)
        
        # 阶段 4：分配客户成功经理
        support_task = Task(
            type='assign_csm',
            data={
                'customer_id': crm_result['customer_id'],
                'plan': billing_result['plan']
            },
            assigned_to='support_agent'
        )
        support_result = self.coordinator.execute_task(support_task)
        
        return {
            'customer_id': crm_result['customer_id'],
            'billing': billing_result,
            'resources': provisioning_result,
            'training': training_result,
            'support': support_result,
            'status': 'completed'
        }

class IntelligentMeetingCoordinator:
    def __init__(self):
        self.calendar_agent = CalendarAgent()
        self.note_taking_agent = NoteTakingAgent()
        self.task_management_agent = TaskManagementAgent()
        self.expense_agent = ExpenseAgent()
    
    def coordinate_meeting(self, meeting_request):
        """
        协调会议的各个方面
        """
        # 1. 日程安排
        schedule_result = self.calendar_agent.find_available_slots(
            participants=meeting_request['participants'],
            duration=meeting_request['duration'],
            preferences=meeting_request['preferences']
        )
        
        # 2. 准备会议资料
        materials = self.prepare_meeting_materials(meeting_request)
        
        # 3. 安排会议记录
        note_taking_config = {
            'meeting_id': meeting_request['id'],
            'transcription': True,
            'summary': True,
            'action_items': True
        }
        self.note_taking_agent.configure(note_taking_config)
        
        # 4. 会议执行
        meeting_result = self.execute_meeting(meeting_request, schedule_result)
        
        # 5. 后处理：提取行动项和费用
        action_items = self.note_taking_agent.extract_action_items(
            meeting_result['transcript']
        )
        
        expenses = self.expense_agent.extract_expenses(
            meeting_result['transcript']
        )
        
        # 6. 分配任务
        for item in action_items:
            self.task_management_agent.create_task({
                'title': item['description'],
                'assignee': item['assignee'],
                'deadline': item['deadline'],
                'meeting_id': meeting_request['id']
            })
        
        # 7. 提交费用报销
        for expense in expenses:
            self.expense_agent.submit_expense({
                'amount': expense['amount'],
                'category': expense['category'],
                'description': expense['description'],
                'meeting_id': meeting_request['id']
            })
        
        return {
            'meeting_id': meeting_request['id'],
            'schedule': schedule_result,
            'transcript': meeting_result['transcript'],
            'summary': meeting_result['summary'],
            'action_items': action_items,
            'expenses': expenses
        }