DOCS/GPT_docs/14_멀티_에이전트_협업_아키텍처.md

# 멀티 에이전트 협업 아키텍처

## 컨테이너 오케스트레이션 구조

### Kubernetes 기반 에이전트 배포

```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: robeing-agent-cluster
spec:
  replicas: 5
  selector:
    matchLabels:
      app: robeing-agent
  template:
    metadata:
      labels:
        app: robeing-agent
    spec:
      containers:
      - name: agent-container
        image: robeing:latest
        ports:
        - containerPort: 8080
        env:
        - name: AGENT_ROLE
          value: "WORKER"
        - name: CLUSTER_ID
          value: "robeing-cluster-01"
        resources:
          requests:
            memory: "1Gi"
            cpu: "500m"
          limits:
            memory: "2Gi"
            cpu: "1000m"
```

### Docker Compose 멀티 에이전트 구성

```yaml
version: '3.8'
services:
  coordinator:
    image: robeing:coordinator
    container_name: agent_coordinator
    environment:
      - ROLE=COORDINATOR
      - AGENT_COUNT=5
    ports:
      - "8080:8080"
    networks:
      - agent_network
      
  agent1:
    image: robeing:worker
    container_name: agent_worker_1
    environment:
      - ROLE=WORKER
      - SPECIALIZATION=data_processing
      - COORDINATOR_URL=http://coordinator:8080
    networks:
      - agent_network
    depends_on:
      - coordinator
      
  agent2:
    image: robeing:worker
    container_name: agent_worker_2
    environment:
      - ROLE=WORKER
      - SPECIALIZATION=communication
      - COORDINATOR_URL=http://coordinator:8080
    networks:
      - agent_network
    depends_on:
      - coordinator

networks:
  agent_network:
    driver: bridge
```

## 에이전트 역할 분담

### 역할 기반 아키텍처

```python
class MultiAgentRoles:
    ROLES = {
        'coordinator': {
            'responsibilities': ['task_distribution', 'conflict_resolution', 'resource_management'],
            'capabilities': ['overview', 'planning', 'monitoring'],
            'priority': 1
        },
        'specialist': {
            'responsibilities': ['domain_expertise', 'deep_analysis'],
            'capabilities': ['specialized_processing', 'expert_knowledge'],
            'priority': 2
        },
        'worker': {
            'responsibilities': ['task_execution', 'data_processing'],
            'capabilities': ['parallel_processing', 'batch_operations'],
            'priority': 3
        },
        'messenger': {
            'responsibilities': ['communication', 'data_transfer'],
            'capabilities': ['protocol_handling', 'format_conversion'],
            'priority': 4
        },
        'guardian': {
            'responsibilities': ['security', 'monitoring', 'validation'],
            'capabilities': ['threat_detection', 'access_control'],
            'priority': 2
        }
    }
```

## 에이전트 간 통신 프로토콜

### 메시지 패싱 시스템

```python
class AgentCommunicationProtocol:
    def __init__(self):
        self.message_queue = asyncio.Queue()
        self.protocol_version = "1.0"
        
    async def send_message(self, sender_id, receiver_id, message):
        """에이전트 간 메시지 전송"""
        
        packet = {
            'id': uuid.uuid4().hex,
            'timestamp': datetime.now().isoformat(),
            'sender': sender_id,
            'receiver': receiver_id,
            'type': message['type'],
            'payload': message['data'],
            'priority': message.get('priority', 5),
            'requires_ack': message.get('requires_ack', False)
        }
        
        # 메시지 암호화
        encrypted_packet = self.encrypt_message(packet)
        
        # 큐에 추가
        await self.message_queue.put(encrypted_packet)
        
        # ACK 대기
        if packet['requires_ack']:
            ack = await self.wait_for_ack(packet['id'])
            return ack
            
        return True
    
    async def broadcast(self, sender_id, message):
        """전체 에이전트에게 브로드캐스트"""
        
        broadcast_packet = {
            'type': 'broadcast',
            'sender': sender_id,
            'message': message,
            'timestamp': datetime.now().isoformat()
        }
        
        # 모든 에이전트에게 전송
        active_agents = await self.get_active_agents()
        
        tasks = []
        for agent_id in active_agents:
            if agent_id != sender_id:
                task = asyncio.create_task(
                    self.send_message(sender_id, agent_id, broadcast_packet)
                )
                tasks.append(task)
                
        await asyncio.gather(*tasks)
```

## 작업 분산과 조율

### 태스크 스케줄러

```python
class DistributedTaskScheduler:
    def __init__(self):
        self.task_queue = []
        self.agent_pool = {}
        self.task_assignments = {}
        
    def distribute_task(self, task):
        """작업을 적절한 에이전트에게 분배"""
        
        # 작업 분석
        task_requirements = self.analyze_task_requirements(task)
        
        # 가용 에이전트 확인
        available_agents = self.get_available_agents()
        
        # 최적 에이전트 선택
        selected_agents = self.select_optimal_agents(
            task_requirements,
            available_agents
        )
        
        # 작업 분할
        if len(selected_agents) > 1:
            subtasks = self.split_task(task, len(selected_agents))
            
            assignments = []
            for agent, subtask in zip(selected_agents, subtasks):
                assignment = {
                    'agent_id': agent['id'],
                    'subtask': subtask,
                    'deadline': self.calculate_deadline(subtask),
                    'dependencies': self.identify_dependencies(subtask)
                }
                assignments.append(assignment)
                
            return assignments
        else:
            # 단일 에이전트 할당
            return [{
                'agent_id': selected_agents[0]['id'],
                'task': task,
                'deadline': self.calculate_deadline(task)
            }]
    
    def select_optimal_agents(self, requirements, available):
        """요구사항에 최적인 에이전트 선택"""
        
        scores = {}
        
        for agent in available:
            score = 0
            
            # 전문성 매칭
            if agent['specialization'] in requirements['skills']:
                score += 50
                
            # 현재 부하 고려
            load = self.get_agent_load(agent['id'])
            score -= load * 10
            
            # 성능 이력 고려
            performance = self.get_agent_performance(agent['id'])
            score += performance * 20
            
            scores[agent['id']] = score
            
        # 상위 에이전트 선택
        sorted_agents = sorted(
            available,
            key=lambda a: scores[a['id']],
            reverse=True
        )
        
        return sorted_agents[:requirements.get('agent_count', 1)]
```

## 합의 메커니즘

### 분산 의사결정

```python
class ConsensusProtocol:
    def __init__(self):
        self.voting_threshold = 0.66  # 2/3 majority
        
    async def reach_consensus(self, proposal, participating_agents):
        """에이전트 간 합의 도달"""
        
        # 제안 브로드캐스트
        voting_request = {
            'proposal': proposal,
            'voting_deadline': datetime.now() + timedelta(seconds=30)
        }
        
        # 투표 수집
        votes = await self.collect_votes(voting_request, participating_agents)
        
        # 투표 집계
        yes_votes = sum(1 for v in votes.values() if v == 'approve')
        no_votes = sum(1 for v in votes.values() if v == 'reject')
        abstain = sum(1 for v in votes.values() if v == 'abstain')
        
        total_votes = len(participating_agents)
        approval_rate = yes_votes / total_votes if total_votes > 0 else 0
        
        # 합의 판정
        if approval_rate >= self.voting_threshold:
            return {
                'consensus': 'approved',
                'approval_rate': approval_rate,
                'votes': votes
            }
        else:
            return {
                'consensus': 'rejected',
                'approval_rate': approval_rate,
                'votes': votes
            }
    
    async def byzantine_fault_tolerance(self, agents):
        """비잔틴 장애 허용"""
        
        # PBFT (Practical Byzantine Fault Tolerance) 구현
        f = (len(agents) - 1) // 3  # 최대 f개의 악의적 노드 허용
        
        # 3단계 합의 프로토콜
        # 1. Pre-prepare
        # 2. Prepare  
        # 3. Commit
        
        return True  # 합의 성공
```

## 에이전트 클러스터링

### 동적 클러스터 형성

```python
class AgentClustering:
    def __init__(self):
        self.clusters = {}
        self.cluster_metrics = {}
        
    def form_cluster(self, agents, purpose):
        """목적별 에이전트 클러스터 형성"""
        
        cluster = {
            'id': uuid.uuid4().hex,
            'purpose': purpose,
            'agents': agents,
            'created_at': datetime.now(),
            'leader': None,
            'status': 'forming'
        }
        
        # 리더 선출
        cluster['leader'] = self.elect_leader(agents)
        
        # 클러스터 네트워크 설정
        self.setup_cluster_network(cluster)
        
        # 클러스터 등록
        self.clusters[cluster['id']] = cluster
        
        return cluster
    
    def elect_leader(self, agents):
        """리더 에이전트 선출"""
        
        # Raft 알고리즘 사용
        election_results = {}
        
        for agent in agents:
            # 각 에이전트의 리더십 점수 계산
            score = (
                agent['experience'] * 0.3 +
                agent['reliability'] * 0.3 +
                agent['resources'] * 0.2 +
                agent['availability'] * 0.2
            )
            election_results[agent['id']] = score
            
        # 최고 점수 에이전트를 리더로
        leader_id = max(election_results, key=election_results.get)
        
        return leader_id
```

## 리소스 공유

### 컴퓨팅 리소스 풀링

```python
class ResourcePooling:
    def __init__(self):
        self.resource_pool = {
            'cpu': 0,
            'memory': 0,
            'storage': 0,
            'bandwidth': 0
        }
        self.allocations = {}
        
    def contribute_resources(self, agent_id, resources):
        """에이전트가 리소스 풀에 기여"""
        
        for resource_type, amount in resources.items():
            self.resource_pool[resource_type] += amount
            
        # 기여도 기록
        if agent_id not in self.allocations:
            self.allocations[agent_id] = {'contributed': {}, 'used': {}}
            
        self.allocations[agent_id]['contributed'] = resources
        
    def request_resources(self, agent_id, requirements):
        """리소스 요청 및 할당"""
        
        # 가용 리소스 확인
        if self.check_availability(requirements):
            # 리소스 할당
            for resource_type, amount in requirements.items():
                self.resource_pool[resource_type] -= amount
                
                if agent_id not in self.allocations:
                    self.allocations[agent_id] = {'contributed': {}, 'used': {}}
                    
                if resource_type not in self.allocations[agent_id]['used']:
                    self.allocations[agent_id]['used'][resource_type] = 0
                    
                self.allocations[agent_id]['used'][resource_type] += amount
                
            return True, "Resources allocated"
        else:
            return False, "Insufficient resources"
```

## 장애 복구

### 에이전트 장애 처리

```python
class FailureRecovery:
    def __init__(self):
        self.health_checks = {}
        self.backup_agents = {}
        
    async def monitor_agent_health(self):
        """에이전트 상태 모니터링"""
        
        while True:
            for agent_id in self.get_all_agents():
                try:
                    # 헬스체크
                    response = await self.ping_agent(agent_id)
                    
                    if response['status'] == 'healthy':
                        self.health_checks[agent_id] = {
                            'status': 'healthy',
                            'last_check': datetime.now()
                        }
                    else:
                        await self.handle_unhealthy_agent(agent_id)
                        
                except Exception as e:
                    await self.handle_failed_agent(agent_id)
                    
            await asyncio.sleep(10)  # 10초마다 체크
            
    async def handle_failed_agent(self, agent_id):
        """실패한 에이전트 처리"""
        
        # 1. 작업 재할당
        pending_tasks = self.get_agent_tasks(agent_id)
        await self.redistribute_tasks(pending_tasks)
        
        # 2. 백업 에이전트 활성화
        if agent_id in self.backup_agents:
            backup_id = self.backup_agents[agent_id]
            await self.activate_backup(backup_id)
            
        # 3. 클러스터에서 제거
        await self.remove_from_cluster(agent_id)
        
        # 4. 복구 시도
        await self.attempt_recovery(agent_id)
```