멀티에이전트 시스템에서 MCP, Action, Tool 활용 전략과 6가지 설계 사례

# Node별 독립적 MCP 연결
def research_node(state):
    # 이 노드만의 MCP 클라이언트
    client = MultiServerMCPClient({
        "web_search": {
            "transport": "streamable_http",
            "url": "https://search-mcp-server/mcp"
        }
    })
    tools = await client.get_tools()
    
    # 노드 전용 LLM with tools
    llm_with_tools = llm.bind_tools(tools)
    response = llm_with_tools.invoke(state["messages"])
    
    return {"messages": [response]}

def analysis_node(state):
    # 분석 전용 MCP 연결
    client = MultiServerMCPClient({
        "data_analysis": {
            "transport": "stdio",
            "command": "python",
            "args": ["analysis_server.py"]
        }
    })
    tools = await client.get_tools()
    
    llm_with_tools = llm.bind_tools(tools)
    response = llm_with_tools.invoke(state["messages"])
    
    return {"messages": [response]}

# 에이전트 레벨 MCP 설정
class MultiAgentSystem:
    def __init__(self):
        self.mcp_client = MultiServerMCPClient({
            "search": {"transport": "streamable_http", "url": "..."},
            "analysis": {"transport": "stdio", "command": "..."},
            "database": {"transport": "sse", "url": "..."}
        })
        self.shared_tools = None
    
    async def initialize(self):
        self.shared_tools = await self.mcp_client.get_tools()
    
    def create_agent_node(self, agent_name, specialized_tools=None):
        def agent_node(state):
            # 공유 도구 + 전용 도구 조합
            all_tools = self.shared_tools + (specialized_tools or [])
            llm_with_tools = llm.bind_tools(all_tools)
            
            # 에이전트별 프롬프트 적용
            prompt = self.get_agent_prompt(agent_name)
            response = llm_with_tools.invoke([prompt] + state["messages"])
            
            return {"messages": [response]}
        return agent_node

class CustomerServiceSystem:
    def __init__(self):
        # 공통 MCP 서버들
        self.common_mcp = {
            "customer_db": {
                "transport": "streamable_http",
                "url": "https://customer-api/mcp",
                "headers": {"Authorization": "Bearer token"}
            },
            "knowledge_base": {
                "transport": "stdio", 
                "command": "python",
                "args": ["kb_server.py"]
            }
        }
        
        # 전문화된 MCP 서버들
        self.specialized_mcp = {
            "order_management": {
                "transport": "sse",
                "url": "https://order-api/mcp"
            },
            "technical_support": {
                "transport": "streamable_http", 
                "url": "https://tech-support/mcp"
            }
        }

    def build_supervisor_agent(self):
        # 슈퍼바이저는 라우팅 도구만 필요
        routing_tools = [
            self.create_handoff_tool("order_agent"),
            self.create_handoff_tool("tech_agent"),
            self.create_handoff_tool("billing_agent")
        ]
        
        return create_react_agent(
            model="anthropic:claude-3-5-sonnet-latest",
            tools=routing_tools,
            prompt=self.get_supervisor_prompt()
        )
    
    def build_order_agent(self):
        # 주문 관련 MCP + 공통 MCP
        client = MultiServerMCPClient({
            **self.common_mcp,
            **{"order_management": self.specialized_mcp["order_management"]}
        })
        
        tools = await client.get_tools()
        
        return create_react_agent(
            model="anthropic:claude-3-5-sonnet-latest",
            tools=tools,
            prompt=self.get_order_agent_prompt()
        )

@tool
def lookup_customer_info(customer_id: str) -> str:
    """고객 정보를 조회합니다."""
    # MCP를 통한 고객 DB 쿼리
    result = customer_db_client.call_tool("get_customer", {
        "customer_id": customer_id
    })
    return result

@tool  
def update_order_status(order_id: str, status: str) -> Command:
    """주문 상태를 업데이트하고 그래프 상태를 갱신합니다."""
    # 외부 시스템 업데이트
    order_api.update_status(order_id, status)
    
    # 그래프 상태 업데이트와 함께 Command 반환
    return Command(
        update={
            "current_order": {"id": order_id, "status": status},
            "messages": [f"주문 {order_id}의 상태가 {status}로 변경되었습니다."]
        }
    )

def create_handoff_tool(target_agent: str):
    @tool
    def handoff_to_agent(reason: str) -> Command:
        f"""다음 에이전트로 작업을 전달: {target_agent}"""
        return Command(
            goto=target_agent,
            update={
                "handoff_reason": reason,
                "previous_agent": "supervisor"
            }
        )
    return handoff_to_agent

class ResearchAssistantSystem:
    def __init__(self):
        # 상위 레벨 MCP - 범용 연구 도구
        self.global_mcp = MultiServerMCPClient({
            "web_search": {
                "transport": "streamable_http",
                "url": "https://search-api/mcp"
            },
            "paper_db": {
                "transport": "sse", 
                "url": "https://arxiv-api/mcp"
            },
            "vector_store": {
                "transport": "stdio",
                "command": "python", 
                "args": ["vector_server.py"]
            }
        })
        
        # 하위 레벨 MCP - 전문화된 도구들
        self.specialized_clients = {}
    
    def create_literature_review_subgraph(self):
        # 문헌 조사 전용 MCP
        lit_client = MultiServerMCPClient({
            "semantic_scholar": {
                "transport": "streamable_http",
                "url": "https://semantic-scholar-api/mcp"
            },
            "citation_analyzer": {
                "transport": "stdio",
                "command": "python",
                "args": ["citation_server.py"]
            }
        })
        
        class LitReviewState(TypedDict):
            query: str
            papers: List[Dict]
            analysis: str
            recommendations: List[str]
        
        def search_papers_node(state: LitReviewState):
            tools = await lit_client.get_tools()
            llm_with_tools = llm.bind_tools(tools)
            
            response = llm_with_tools.invoke([
                {"role": "system", "content": "논문 검색 전문가입니다."},
                {"role": "user", "content": f"다음 주제로 논문을 검색하세요: {state['query']}"}
            ])
            
            return {"papers": response.tool_calls[0]["args"]["papers"]}
        
        def analyze_papers_node(state: LitReviewState):
            # 논문 분석 및 인용 관계 파악
            analysis_tools = await lit_client.get_tools()
            # ... 구현
            
        # 서브그래프 구성
        subgraph = StateGraph(LitReviewState)
        subgraph.add_node("search", search_papers_node)
        subgraph.add_node("analyze", analyze_papers_node)
        subgraph.add_edge("search", "analyze")
        
        return subgraph.compile()

class DynamicToolManager:
    def __init__(self):
        self.tool_registry = {}
        self.mcp_servers = {}
    
    async def register_mcp_server(self, name: str, config: dict):
        """런타임에 새로운 MCP 서버 등록"""
        client = MultiServerMCPClient({name: config})
        tools = await client.get_tools()
        
        self.mcp_servers[name] = client
        self.tool_registry.update({
            f"{name}_{tool.name}": tool for tool in tools
        })
    
    def create_adaptive_agent_node(self, agent_role: str):
        def agent_node(state, config):
            # 현재 태스크에 따른 동적 도구 선택
            task_type = state.get("current_task_type")
            required_tools = self.get_tools_for_task(task_type)
            
            # 필요한 MCP 서버가 없으면 동적 로딩
            for tool_name in required_tools:
                if tool_name not in self.tool_registry:
                    await self.load_tool_on_demand(tool_name)
            
            # 선택된 도구들로 LLM 구성
            selected_tools = [
                self.tool_registry[name] for name in required_tools
                if name in self.tool_registry
            ]
            
            llm_with_tools = llm.bind_tools(selected_tools)
            response = llm_with_tools.invoke(state["messages"])
            
            return {"messages": [response]}
        
        return agent_node

class ParallelProcessingSystem:
    def __init__(self):
        self.connection_pool = {}
        self.load_balancer = MCPLoadBalancer()
    
    def create_parallel_workers(self, num_workers: int):
        """병렬 워커 노드들 생성"""
        workers = []
        
        for i in range(num_workers):
            # 각 워커별 독립적인 MCP 연결
            worker_mcp = MultiServerMCPClient({
                f"data_processor_{i}": {
                    "transport": "streamable_http",
                    "url": f"https://processor-{i}.internal/mcp",
                    "headers": {"Worker-ID": str(i)}
                }
            })
            
            workers.append(self.create_worker_node(worker_mcp, i))
        
        return workers
    
    def create_worker_node(self, mcp_client, worker_id):
        async def worker_node(state):
            # 할당받은 데이터 청크 처리
            chunk = state.get("data_chunk")
            tools = await mcp_client.get_tools()
            
            # 워커별 특화된 처리
            processor_tool = tools[0]  # 데이터 처리 도구
            result = await processor_tool.invoke({
                "data": chunk,
                "worker_id": worker_id,
                "processing_params": state.get("params", {})
            })
            
            return {
                "processed_results": result,
                "worker_id": worker_id,
                "status": "completed"
            }
        
        return worker_node
    
    def create_coordinator_node(self):
        """작업 분산 및 결과 수집 노드"""
        def coordinator(state):
            # 작업 분할
            data = state["input_data"]
            chunks = self.split_data(data, num_chunks=len(self.workers))
            
            # Send API를 사용한 병렬 처리
            return [
                Send("worker_node", {"data_chunk": chunk, "chunk_id": i})
                for i, chunk in enumerate(chunks)
            ]
        
        return coordinator

class AggregationState(TypedDict):
    input_data: Any
    worker_results: Annotated[List[Dict], operator.add]
    final_result: Optional[Dict]
    processing_status: Dict[str, str]

def create_aggregator_node():
    """워커 결과들을 집계하는 노드"""
    def aggregator(state: AggregationState):
        results = state["worker_results"]
        
        # 모든 워커가 완료되었는지 확인
        if len(results) < expected_worker_count:
            return {"processing_status": {"overall": "waiting"}}
        
        # 결과 집계 및 후처리
        combined_result = combine_worker_results(results)
        
        # 최종 품질 검증 MCP 호출
        quality_client = MultiServerMCPClient({
            "quality_checker": {
                "transport": "streamable_http",
                "url": "https://quality-api/mcp"
            }
        })
        
        quality_tools = await quality_client.get_tools()
        validation_result = await quality_tools[0].invoke({
            "data": combined_result,
            "validation_rules": state.get("validation_rules", [])
        })
        
        return {
            "final_result": combined_result,
            "quality_score": validation_result["score"],
            "processing_status": {"overall": "completed"}
        }
    
    return aggregator

class AdaptiveAgentSystem:
    def __init__(self):
        self.context_analyzer = ContextAnalyzer()
        self.mcp_registry = MCPRegistry()
        self.tool_cache = {}
    
    def create_adaptive_supervisor(self):
        def supervisor_node(state, config):
            # 현재 컨텍스트 분석
            context = self.context_analyzer.analyze(state["messages"])
            
            # 컨텍스트에 따른 MCP 서버 선택
            required_capabilities = context["required_capabilities"]
            selected_servers = self.mcp_registry.get_servers_for_capabilities(
                required_capabilities
            )
            
            # 동적 MCP 클라이언트 구성
            dynamic_config = {
                name: server_config 
                for name, server_config in selected_servers.items()
            }
            
            client = MultiServerMCPClient(dynamic_config)
            tools = await client.get_tools()
            
            # 상황별 프롬프트 생성
            situational_prompt = self.generate_contextual_prompt(
                context, tools
            )
            
            llm_with_tools = llm.bind_tools(tools)
            response = llm_with_tools.invoke([
                {"role": "system", "content": situational_prompt},
                *state["messages"]
            ])
            
            # 다음 에이전트 결정
            next_agent = self.determine_next_agent(response, context)
            
            return Command(
                goto=next_agent,
                update={
                    "context": context,
                    "available_tools": [tool.name for tool in tools],
                    "messages": [response]
                }
            )
        
        return supervisor_node

class ResilientToolSystem:
    def __init__(self):
        self.primary_mcp = {}
        self.fallback_mcp = {}
        self.health_checker = MCPHealthChecker()
    
    async def create_resilient_tool_node(self, node_name: str):
        def tool_node_with_fallback(state):
            try:
                # 기본 MCP 서버 시도
                primary_client = MultiServerMCPClient(self.primary_mcp)
                tools = await primary_client.get_tools()
                
                result = await self.execute_with_tools(tools, state)
                return result
                
            except MCPConnectionError as e:
                # 폴백 시스템으로 전환
                logging.warning(f"Primary MCP failed: {e}, switching to fallback")
                
                fallback_client = MultiServerMCPClient(self.fallback_mcp)
                fallback_tools = await fallback_client.get_tools()
                
                # 폴백 도구로 재시도
                result = await self.execute_with_tools(fallback_tools, state)
                
                # 상태에 폴백 사용 기록
                result["system_status"] = "using_fallback"
                return result
                
            except Exception as e:
                # 완전 실패 시 안전한 응답
                return {
                    "messages": [f"시스템 오류가 발생했습니다: {str(e)}"],
                    "system_status": "error",
                    "requires_human_intervention": True
                }
        
        return tool_node_with_fallback

class OptimizedMCPManager:
    def __init__(self):
        self.connection_pools = {}
        self.tool_cache = TTLCache(maxsize=1000, ttl=300)  # 5분 TTL
        self.result_cache = LRUCache(maxsize=500)
    
    async def get_cached_tools(self, server_name: str):
        """도구 스키마 캐싱"""
        cache_key = f"tools_{server_name}"
        
        if cache_key in self.tool_cache:
            return self.tool_cache[cache_key]
        
        client = await self.get_pooled_client(server_name)
        tools = await client.get_tools()
        
        self.tool_cache[cache_key] = tools
        return tools
    
    async def get_pooled_client(self, server_name: str):
        """연결 풀 관리"""
        if server_name not in self.connection_pools:
            self.connection_pools[server_name] = MCPConnectionPool(
                config=self.server_configs[server_name],
                min_connections=2,
                max_connections=10
            )
        
        return await self.connection_pools[server_name].get_client()
    
    async def cached_tool_execution(self, tool_name: str, args: dict):
        """결과 캐싱을 통한 중복 호출 방지"""
        cache_key = f"{tool_name}_{hash(json.dumps(args, sort_keys=True))}"
        
        if cache_key in self.result_cache:
            return self.result_cache[cache_key]
        
        result = await self.execute_tool(tool_name, args)
        self.result_cache[cache_key] = result
        
        return result

class BatchProcessor:
    def __init__(self):
        self.batch_queue = asyncio.Queue()
        self.batch_size = 10
        self.processing_task = None
    
    async def queue_tool_call(self, tool_call: ToolCall):
        """도구 호출을 배치 큐에 추가"""
        await self.batch_queue.put(tool_call)
        
        # 배치 처리기가 실행 중이 아니면 시작
        if not self.processing_task or self.processing_task.done():
            self.processing_task = asyncio.create_task(
                self.process_batch()
            )
    
    async def process_batch(self):
        """배치 단위로 도구 호출 처리"""
        while True:
            batch = []
            
            # 배치 크기만큼 수집
            for _ in range(self.batch_size):
                try:
                    call = await asyncio.wait_for(
                        self.batch_queue.get(), timeout=1.0
                    )
                    batch.append(call)
                except asyncio.TimeoutError:
                    break
            
            if not batch:
                break
            
            # 배치 실행
            await self.execute_batch(batch)
    
    async def execute_batch(self, batch: List[ToolCall]):
        """배치된 도구 호출들을 병렬 실행"""
        tasks = [
            self.execute_single_tool_call(call) 
            for call in batch
        ]
        
        results = await asyncio.gather(*tasks, return_exceptions=True)
        
        # 결과를 각 호출자에게 반환
        for call, result in zip(batch, results):
            call.set_result(result)

class AgentSystemMonitor:
    def __init__(self):
        self.logger = structlog.get_logger()
        self.metrics_collector = MetricsCollector()
        self.trace_manager = TraceManager()
    
    def create_monitored_node(self, node_func, node_name: str):
        """모니터링이 포함된 노드 래퍼"""
        async def monitored_node(state, config):
            trace_id = self.trace_manager.start_trace(node_name)
            start_time = time.time()
            
            try:
                # 입력 상태 로깅
                self.logger.info(
                    "node_execution_start",
                    node_name=node_name,
                    trace_id=trace_id,
                    input_state_keys=list(state.keys())
                )
                
                # MCP 호출 모니터링
                with self.trace_manager.span(f"{node_name}_mcp_calls"):
                    result = await node_func(state, config)
                
                # 성공 메트릭 수집
                execution_time = time.time() - start_time
                self.metrics_collector.record_node_execution(
                    node_name, execution_time, "success"
                )
                
                return result
                
            except Exception as e:
                # 실패 메트릭 및 로깅
                execution_time = time.time() - start_time
                self.metrics_collector.record_node_execution(
                    node_name, execution_time, "error"
                )
                
                self.logger.error(
                    "node_execution_error",
                    node_name=node_name,
                    trace_id=trace_id,
                    error=str(e),
                    execution_time=execution_time
                )
                
                raise
            
            finally:
                self.trace_manager.end_trace(trace_id)
        
        return monitored_node

class SecureAgentSystem:
    def __init__(self):
        self.permission_manager = PermissionManager()
        self.audit_logger = AuditLogger()
    
    def create_secure_agent_node(self, agent_id: str, permissions: List[str]):
        def secure_agent_node(state, config):
            # 현재 사용자 컨텍스트 확인
            user_context = config.get("configurable", {}).get("user_context")
            
            if not self.permission_manager.check_permissions(
                user_context, agent_id, permissions
            ):
                raise PermissionError(f"Agent {agent_id} access denied")
            
            # 권한에 따른 MCP 서버 필터링
            allowed_servers = self.permission_manager.get_allowed_mcp_servers(
                user_context, agent_id
            )
            
            # 보안이 적용된 MCP 클라이언트 생성
            secure_client = SecureMCPClient(
                server_configs=allowed_servers,
                user_token=user_context.get("token"),
                audit_logger=self.audit_logger
            )
            
            tools = await secure_client.get_tools()
            
            # 도구 호출 감사 로깅
            for tool in tools:
                tool = self.wrap_tool_with_audit(tool, agent_id, user_context)
            
            llm_with_tools = llm.bind_tools(tools)
            response = llm_with_tools.invoke(state["messages"])
            
            return {"messages": [response]}
        
        return secure_agent_node

class CollaborativePlatform:
    def __init__(self):
        self.session_manager = SessionManager()
        self.user_mcp_configs = {}
        self.shared_workspace = SharedWorkspace()
    
    def create_user_specific_agent(self, user_id: str, session_id: str):
        """사용자별 맞춤형 에이전트 생성"""
        def user_agent_node(state, config):
            # 사용자별 MCP 설정 로드
            user_config = self.user_mcp_configs.get(user_id, {})
            
            # 개인화된 도구 세트 구성
            personal_mcp = MultiServerMCPClient({
                "personal_files": {
                    "transport": "streamable_http",
                    "url": f"https://files-api/users/{user_id}/mcp",
                    "headers": {"User-ID": user_id, "Session-ID": session_id}
                },
                "user_preferences": {
                    "transport": "stdio",
                    "command": "python",
                    "args": ["user_prefs_server.py", user_id]
                }
            })
            
            # 공유 워크스페이스 도구
            shared_mcp = MultiServerMCPClient({
                "workspace": {
                    "transport": "sse",
                    "url": f"https://workspace-api/sessions/{session_id}/mcp"
                },
                "collaboration": {
                    "transport": "streamable_http",
                    "url": "https://collab-api/mcp"
                }
            })
            
            personal_tools = await personal_mcp.get_tools()
            shared_tools = await shared_mcp.get_tools()
            
            # 사용자 컨텍스트에 맞는 프롬프트
            user_prompt = self.generate_user_prompt(user_id, session_id)
            
            llm_with_tools = llm.bind_tools(personal_tools + shared_tools)
            response = llm_with_tools.invoke([
                {"role": "system", "content": user_prompt},
                *state["messages"]
            ])
            
            # 협업 상태 업데이트
            collaboration_update = self.extract_collaboration_info(response)
            
            return {
                "messages": [response],
                "user_actions": {user_id: collaboration_update},
                "workspace_updates": collaboration_update.get("workspace_changes", {})
            }
        
        return user_agent_node

    def create_workspace_coordinator(self):
        """워크스페이스 전체를 조율하는 코디네이터"""
        def coordinator_node(state):
            workspace_state = state.get("workspace_state", {})
            user_actions = state.get("user_actions", {})
            
            # 충돌 감지 및 해결
            conflicts = self.detect_conflicts(user_actions)
            
            if conflicts:
                resolution_mcp = MultiServerMCPClient({
                    "conflict_resolver": {
                        "transport": "streamable_http",
                        "url": "https://conflict-resolution/mcp"
                    }
                })
                
                resolution_tools = await resolution_mcp.get_tools()
                resolver_llm = llm.bind_tools(resolution_tools)
                
                resolution = await resolver_llm.invoke([
                    {"role": "system", "content": "협업 충돌을 해결하는 전문가입니다."},
                    {"role": "user", "content": f"다음 충돌을 해결해주세요: {conflicts}"}
                ])
                
                return {
                    "conflict_resolution": resolution,
                    "workspace_state": self.apply_resolution(workspace_state, resolution)
                }
            
            # 정상 업데이트
            return {
                "workspace_state": self.merge_user_actions(workspace_state, user_actions)
            }
        
        return coordinator_node

class RealtimeSyncManager:
    def __init__(self):
        self.websocket_manager = WebSocketManager()
        self.change_detector = ChangeDetector()
        self.broadcast_queue = asyncio.Queue()
    
    def create_sync_aware_tool(self, base_tool, tool_category: str):
        """실시간 동기화가 포함된 도구 래퍼"""
        @tool
        async def synced_tool(*args, **kwargs):
            # 원본 도구 실행
            result = await base_tool.invoke(*args, **kwargs)
            
            # 변경사항 감지
            changes = self.change_detector.detect_changes(
                tool_category, args, kwargs, result
            )
            
            if changes:
                # 실시간 브로드캐스트
                await self.broadcast_queue.put({
                    "type": "tool_execution",
                    "tool": base_tool.name,
                    "changes": changes,
                    "timestamp": datetime.utcnow().isoformat(),
                    "affected_users": self.get_affected_users(changes)
                })
            
            return result
        
        return synced_tool
    
    async def broadcast_worker(self):
        """변경사항을 실시간으로 브로드캐스트"""
        while True:
            try:
                change_event = await self.broadcast_queue.get()
                affected_users = change_event["affected_users"]
                
                # 영향받는 사용자들에게만 전송
                for user_id in affected_users:
                    await self.websocket_manager.send_to_user(
                        user_id, change_event
                    )
                
                self.broadcast_queue.task_done()
                
            except Exception as e:
                logging.error(f"Broadcast error: {e}")

class EdgeAgentSystem:
    def __init__(self, resource_constraints: Dict[str, Any]):
        self.constraints = resource_constraints
        self.lightweight_mcp = LightweightMCPClient()
        self.tool_prioritizer = ToolPrioritizer(constraints)
    
    def create_resource_aware_agent(self, agent_role: str):
        """리소스 제약을 고려한 에이전트"""
        def edge_agent_node(state, config):
            # 현재 리소스 상태 확인
            current_resources = self.get_current_resource_usage()
            
            # 리소스 기반 도구 선택
            available_tools = self.tool_prioritizer.get_affordable_tools(
                current_resources, agent_role
            )
            
            if not available_tools:
                # 리소스 부족 시 최소 기능으로 대체
                return self.fallback_response(state)
            
            # 경량화된 MCP 연결
            edge_mcp_config = {
                "local_tools": {
                    "transport": "stdio",
                    "command": "python",
                    "args": ["edge_tools.py"],
                    "resource_limit": self.constraints["memory_limit"]
                }
            }
            
            if self.has_network_connectivity():
                # 네트워크가 있으면 클라우드 도구도 활용
                edge_mcp_config["cloud_backup"] = {
                    "transport": "streamable_http",
                    "url": "https://cloud-api/mcp",
                    "timeout": 5.0,  # 빠른 타임아웃
                    "retry_count": 1
                }
            
            client = LightweightMCPClient(edge_mcp_config)
            tools = await client.get_tools(priority_filter=available_tools)
            
            # 리소스 효율적인 추론
            lightweight_llm = self.get_optimized_llm(current_resources)
            llm_with_tools = lightweight_llm.bind_tools(tools)
            
            response = llm_with_tools.invoke(state["messages"][-3:])  # 컨텍스트 제한
            
            return {"messages": [response]}
        
        return edge_agent_node
    
    def create_offline_capable_node(self, essential_tools: List[str]):
        """오프라인에서도 동작 가능한 노드"""
        def offline_node(state):
            # 로컬 캐시된 도구들만 사용
            cached_tools = self.lightweight_mcp.get_cached_tools(essential_tools)
            
            if not cached_tools:
                # 캐시된 도구가 없으면 규칙 기반 대응
                return self.rule_based_response(state)
            
            # 오프라인 전용 경량 LLM
            offline_llm = self.get_offline_llm()
            response = offline_llm.invoke({
                "messages": state["messages"],
                "available_tools": [tool.name for tool in cached_tools]
            })
            
            return {"messages": [response], "mode": "offline"}
        
        return offline_node

class DistributedEdgeSystem:
    def __init__(self):
        self.edge_nodes = {}
        self.load_balancer = EdgeLoadBalancer()
        self.mesh_network = MeshNetworkManager()
    
    def create_distributed_coordinator(self):
        """분산 엣지 노드들을 조율하는 코디네이터"""
        def coordinator_node(state):
            task_complexity = self.analyze_task_complexity(state["messages"][-1])
            
            # 태스크 복잡도에 따른 노드 선택
            if task_complexity == "simple":
                # 로컬에서 처리
                return self.process_locally(state)
            
            elif task_complexity == "medium":
                # 인근 엣지 노드와 협업
                nearby_nodes = self.mesh_network.find_nearby_nodes()
                return self.distribute_to_nearby(state, nearby_nodes)
            
            else:
                # 클라우드 도움 필요
                return self.escalate_to_cloud(state)
        
        return coordinator_node
    
    def distribute_to_nearby(self, state, nearby_nodes):
        """인근 노드들과 작업 분산"""
        subtasks = self.split_task(state["messages"][-1])
        
        # 각 노드의 현재 부하 확인
        node_loads = {
            node_id: self.get_node_load(node_id) 
            for node_id in nearby_nodes
        }
        
        # 부하가 적은 노드부터 할당
        sorted_nodes = sorted(node_loads.items(), key=lambda x: x[1])
        
        assignments = []
        for i, subtask in enumerate(subtasks):
            node_id = sorted_nodes[i % len(sorted_nodes)][0]
            assignments.append(
                Send(f"edge_node_{node_id}", {
                    "subtask": subtask,
                    "original_state": state,
                    "coordinator_node": self.node_id
                })
            )
        
        return assignments

class SelfOptimizingSystem:
    def __init__(self):
        self.performance_tracker = PerformanceTracker()
        self.optimization_engine = OptimizationEngine()
        self.a_b_tester = ABTester()
    
    def create_adaptive_node(self, node_name: str, optimization_config: Dict):
        """성능을 모니터링하고 자동 최적화하는 노드"""
        def adaptive_node(state, config):
            # 현재 성능 메트릭 수집
            current_metrics = self.performance_tracker.get_current_metrics(node_name)
            
            # A/B 테스트 진행 중인지 확인
            if self.a_b_tester.is_testing(node_name):
                variant = self.a_b_tester.get_variant(node_name, state)
                mcp_config = self.get_variant_config(node_name, variant)
            else:
                # 최적화된 설정 사용
                mcp_config = self.optimization_engine.get_optimal_config(
                    node_name, current_metrics
                )
            
            # 동적 MCP 클라이언트 생성
            client = MultiServerMCPClient(mcp_config)
            tools = await client.get_tools()
            
            # 성능 측정 시작
            start_time = time.time()
            
            try:
                llm_with_tools = llm.bind_tools(tools)
                response = llm_with_tools.invoke(state["messages"])
                
                # 성공 메트릭 기록
                execution_time = time.time() - start_time
                self.performance_tracker.record_success(
                    node_name, execution_time, len(response.content)
                )
                
                # A/B 테스트 결과 기록
                if self.a_b_tester.is_testing(node_name):
                    self.a_b_tester.record_result(
                        node_name, variant, execution_time, "success"
                    )
                
                return {"messages": [response]}
                
            except Exception as e:
                # 실패 메트릭 기록
                execution_time = time.time() - start_time
                self.performance_tracker.record_failure(
                    node_name, execution_time, str(e)
                )
                
                if self.a_b_tester.is_testing(node_name):
                    self.a_b_tester.record_result(
                        node_name, variant, execution_time, "failure"
                    )
                
                # 자동 복구 시도
                return self.attempt_recovery(state, e)
        
        return adaptive_node
    
    async def optimization_background_task(self):
        """백그라운드에서 지속적으로 최적화 수행"""
        while True:
            await asyncio.sleep(300)  # 5분마다 실행
            
            # 성능 데이터 분석
            performance_data = self.performance_tracker.get_all_metrics()
            
            for node_name, metrics in performance_data.items():
                # 성능 저하 감지
                if self.detect_performance_degradation(metrics):
                    # 새로운 A/B 테스트 시작
                    new_configs = self.optimization_engine.generate_variants(
                        node_name, metrics
                    )
                    
                    self.a_b_tester.start_test(node_name, new_configs)
                
                # A/B 테스트 결과 확인
                elif self.a_b_tester.is_test_complete(node_name):
                    best_config = self.a_b_tester.get_winner(node_name)
                    self.optimization_engine.update_optimal_config(
                        node_name, best_config
                    )
                    self.a_b_tester.end_test(node_name)