构建企业级AI智能体（Spring AI Alibaba + JManus实战）

# application-prod.yml
spring:
  ai:
    alibaba:
      api-key: ${API_KEY}
      endpoint: https://api.modelscope.cn
    jmanus:
      max-retries: 3
      timeout: 5000ms
      memory-store: redis
      thread-pool:
        core-size: 20
        max-size: 100
        queue-capacity: 200

redis:
  host: redis-cluster.prod
  port: 6379
  password: ${REDIS_PWD}
  timeout: 2000ms

@Bean
@Description("电商客服工单智能体")
public Agent customerServiceAgent(
    ChatClient chatClient, 
    ToolExecutor toolExecutor) {
    
    return new Agent.Builder()
        .withSystemPrompt("""
            您是企业级电商客服智能体，需遵守：
            1. **工单分类规则**：一级分类[物流/质量/售后/支付]
            2. **优先级算法**：涉及金额>100元为紧急工单
            3. **输出规范**：{"category":"物流","urgency":3,"action":"refund"}
            4. **异常处理**：置信度<0.8时转人工
            """)
        .withMemory(new RedisChatMemoryRepository(redisTemplate))
        .withTools("refundTool", "logisticsQueryTool", "compensationTool")
        .withExecutor(toolExecutor)
        .build();
}

sequenceDiagram
    participant 用户
    participant Gateway
    participant PlanningAgent
    participant ClassifierAgent
    participant RefundAgent
    participant NotifierAgent
    
    用户->>Gateway： "订单123未收到货，要求退款"
    Gateway->>PlanningAgent： 原始请求
    PlanningAgent->>ClassifierAgent： classify(订单123)
    ClassifierAgent-->>PlanningAgent： {"category":"物流","urgency":2}
    PlanningAgent->>RefundAgent： refund(订单123)
    RefundAgent-->>PlanningAgent： {"status":"success","refund_id":"RF789"}
    PlanningAgent->>NotifierAgent： notify(用户,"退款已受理")
    NotifierAgent-->>用户： "您的退款RF789已处理"

@Bean
public StateGraph workflowGraph(
    AgentClassifier classifier,
    AgentRefund refundAgent,
    AgentNotifier notifier) {
    
    return new StateGraph("OrderWorkflow")
        .addNode("classify", asyncNode(classifier))
        .addNode("checkLogistics", asyncNode(logisticsChecker))
        .addNode("processRefund", asyncNode(refundAgent))
        .addNode("sendNotification", asyncNode(notifier))
        
        .addEdge(START, "classify")
        .addConditionalEdge("classify", 
            new ConditionRouter("urgency > 2"), 
            Map.of("urgent", "checkLogistics", "normal", "processRefund"))
        .addEdge("checkLogistics", "processRefund")
        .addEdge("processRefund", "sendNotification")
        .addEdge("sendNotification", END);
}

// 智能路由决策器
public class ConditionRouter implements Function<AgentState, String> {
    @Override
    public String apply(AgentState state) {
        int urgency = (int) state.get("urgency");
        return urgency > 2 ? "urgent" : "normal";
    }
}

@Bean
public CircuitBreakerConfig agentCircuitBreaker() {
    return CircuitBreakerConfig.custom()
        .failureRateThreshold(50) // 失败率阈值
        .waitDurationInOpenState(Duration.ofSeconds(30))
        .permittedNumberOfCallsInHalfOpenState(10)
        .slidingWindowType(SlidingWindowType.COUNT_BASED)
        .slidingWindowSize(100)
        .build();
}

@Tool(name = "RefundService", description = "调用支付系统退款")
public RefundResult refund(
    @P("订单ID") String orderId,
    @P("退款金额") BigDecimal amount) {
    
    McpRequest request = McpRequest.builder()
        .service("alipay/refund")
        .param("order_id", orderId)
        .param("amount", amount)
        .param("currency", "CNY")
        .timeout(Duration.ofSeconds(5))
        .build();
    
    McpResponse response = mcpClient.execute(request);
    
    if (response.getCode() == 200) {
        return new RefundResult("SUCCESS", response.getData());
    } else {
        throw new McpException("退款失败: " + response.getMessage());
    }
}

# MCP客户端配置
spring.ai.mcp.endpoint=https://mcp-gateway.prod
spring.ai.mcp.max-connections=200
spring.ai.mcp.connection-timeout=3000
spring.ai.mcp.circuit-breaker.enabled=true

C4Deployment
    title 智能体生产部署架构
    Deployment_Node(云环境, "阿里云 ACK", "Kubernetes 1.28") {
        Deployment_Node(命名空间, "jmanus-prod", "") {
            Container(网关, "API Gateway", "Nginx", "路由/限流")
            Container(智能体, "Spring Boot", "JDK21", "核心逻辑")
            Container(缓存, "Redis Cluster", "v7.0", "会话存储")
            Container(监控, "Prometheus", "", "指标收集")
        }
    }
    
    Rel(网关, 智能体, "HTTP/2")
    Rel(智能体, 缓存, "Redis协议")
    Rel(智能体, 监控, "Metrics")
    Rel(网关, 支付宝/钉钉/物流系统, "HTTPS")

# prometheus-rules.yml
groups:
- name: jmanus-alerts
  rules:
  - alert: HighErrorRate
    expr: sum(rate(http_server_errors_total[5m])) by (service) / sum(rate(http_requests_total[5m])) by (service) > 0.05
    for: 5m
    labels:
      severity: critical
    annotations:
      description: '服务 {{ $labels.service }} 错误率超过5%'
      
  - alert: HighLatency
    expr: histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le)) > 3
    for: 10m
    labels:
      severity: warning

// 智能裁剪对话历史
public List<Message> compressHistory(List<Message> history) {
    return new TokenCompressor(QwenTokenizer())
        .setMaxTokens(1024)
        .compress(history);
}

spring:
  ai:
    tools:
      timeout:
        default: 3000ms
        payment: 5000ms
        logistics: 10000ms