🤖 企业级 AI Agent 系统架构设计
与技术方案深度研究报告

🎯 核心价值主张

📊 关键技术指标（KPI）

🏆 技术选型对比

业务痛点深度剖析

功能需求矩阵（MoSCoW 优先级）

典型业务场景

场景 1：P0 级支付故障自动处理

# 场景描述
用户在 APP 提交："支付失败，提示'银行系统异常'"

# 系统处理流程
1. 区域识别: IP 显示中国大陆 → 路由到国内集群
2. 智能分类: LLM 识别为"支付类-P0 紧急"
3. 日志检索: 自动查询支付服务最近 10 分钟错误日志
4. 根因分析: 发现某银行接口超时率 95%
5. 方案生成: 建议切换到备用支付通道
6. 任务分发: 自动指派给支付团队值班工程师
7. 自动修复: 执行预设的切换脚本（需人工确认）
8. 验证闭环: 监控支付成功率恢复正常，自动关闭工单

# 结果
• MTTR: 从 2 小时降至 8 分钟
• 影响用户：从 10 万 + 降至 5000

场景 2：跨国用户反馈合规处理

# 场景描述
德国用户通过邮件提交："GDPR 数据删除请求未处理"

# 系统处理流程
1. 区域识别: 邮箱域名 + IP → 欧盟区域
2. 合规标记: 自动添加"GDPR"标签
3. 数据隔离: 数据存储在欧洲节点，禁止出境
4. 专业分派: 指派给熟悉 GDPR 的法务团队
5. 模板回复: 生成符合 GDPR 要求的回复模板
6. 时限监控: GDPR 要求 72 小时内响应，系统倒计时提醒

# 合规保障
• 数据本地化存储
• 访问审计日志
• 自动脱敏处理

技术栈全景图

项目目录结构

enterprise-ai-agent/
├── backend/                          # 后端服务
│   ├── app/
│   │   ├── main.py                  # FastAPI 应用入口
│   │   ├── config.py                # 配置管理
│   │   ├── api/
│   │   │   ├── v1/
│   │   │   │   ├── bug_reports.py   # Bug 反馈接口
│   │   │   │   ├── suggestions.py   # 建议提交接口
│   │   │   │   ├── tickets.py       # 工单管理接口
│   │   │   │   └── users.py         # 用户管理接口
│   │   │   └── websocket.py         # WebSocket 处理器
│   │   ├── agents/
│   │   │   ├── __init__.py
│   │   │   ├── classifier.py        # Bug 分类 Agent
│   │   │   ├── rca.py               # 根因分析 Agent
│   │   │   ├── solution.py          # 方案生成 Agent
│   │   │   ├── dispatcher.py        # 任务分发 Agent
│   │   │   └── verifier.py          # 验证闭环 Agent
│   │   ├── graphs/
│   │   │   ├── ticket_workflow.py   # 工单状态图
│   │   │   └── rca_workflow.py      # RCA 流程图
│   │   ├── models/
│   │   │   ├── ticket.py            # 工单模型
│   │   │   ├── user.py              # 用户模型
│   │   │   ├── log.py               # 日志模型
│   │   │   └── message.py           # 消息模型
│   │   ├── schemas/
│   │   │   ├── ticket.py            # Pydantic Schema
│   │   │   └── websocket.py         # WS 消息格式
│   │   ├── services/
│   │   │   ├── notification.py      # 通知服务
│   │   │   ├── analytics.py         # 分析服务
│   │   │   └── export.py            # 导出服务
│   │   ├── utils/
│   │   │   ├── geoip.py             # 地理位置
│   │   │   ├── security.py          # 安全工具
│   │   │   └── logger.py            # 日志工具
│   │   └── middleware/
│   │       ├── auth.py              # 认证中间件
│   │       └── rate_limit.py        # 限流中间件
│   ├── tests/
│   ├── Dockerfile
│   └── requirements.txt
│
├── frontend/                         # 前端应用
│   ├── src/
│   │   ├── components/
│   │   ├── pages/
│   │   ├── hooks/
│   │   ├── stores/
│   │   └── utils/
│   ├── package.json
│   └── Dockerfile
│
├── deploy/                           # 部署配置
│   ├── kubernetes/
│   ├── docker-compose.yml
│   └── helm/
│
└── docs/                             # 文档
    ├── api.md
    └── architecture.md

SQL DDL 定义

-- 工单表
CREATE TABLE tickets (
    id BIGSERIAL PRIMARY KEY,
    ticket_number VARCHAR(50) UNIQUE NOT NULL,
    region VARCHAR(10) NOT NULL,  # CN / GLOBAL
    type VARCHAR(20) NOT NULL,     # BUG / FEATURE / SUGGESTION
    priority VARCHAR(5) NOT NULL DEFAULT 'P2',  # P0/P1/P2/P3
    status VARCHAR(30) NOT NULL DEFAULT 'RECEIVED',
    title VARCHAR(500) NOT NULL,
    description TEXT,
    environment JSONB,
    reporter_id BIGINT REFERENCES users(id),
    assignee_id BIGINT REFERENCES users(id),
    rca_report_id BIGINT REFERENCES rca_reports(id),
    tags VARCHAR(100)[],
    metadata JSONB,
    created_at TIMESTAMP NOT NULL DEFAULT NOW(),
    updated_at TIMESTAMP,
    resolved_at TIMESTAMP,
    closed_at TIMESTAMP,
    
    INDEX idx_region_status (region, status),
    INDEX idx_priority (priority),
    INDEX idx_assignee (assignee_id),
    INDEX idx_created_at (created_at)
);

-- RCA 报告表
CREATE TABLE rca_reports (
    id BIGSERIAL PRIMARY KEY,
    ticket_id BIGINT UNIQUE REFERENCES tickets(id),
    root_causes JSONB NOT NULL,  # [{cause, evidence, confidence}]
    log_analysis JSONB,
    similar_cases BIGINT[],
    recommendations JSONB,
    generated_by VARCHAR(100),
    confidence FLOAT CHECK (confidence >= 0 AND confidence <= 1),
    created_at TIMESTAMP NOT NULL DEFAULT NOW()
);

-- 工单状态变更日志
CREATE TABLE ticket_logs (
    id BIGSERIAL PRIMARY KEY,
    ticket_id BIGINT NOT NULL REFERENCES tickets(id),
    action VARCHAR(50) NOT NULL,  # CREATED/ASSIGNED/IN_PROGRESS/RESOLVED/CLOSED
    actor_id BIGINT REFERENCES users(id),
    actor_type VARCHAR(20),       # HUMAN / AGENT
    details JSONB,
    created_at TIMESTAMP NOT NULL DEFAULT NOW(),
    
    INDEX idx_ticket_id (ticket_id),
    INDEX idx_created_at (created_at)
);

RESTful API 设计规范

# Bug 反馈相关 API

# 1. 创建 Bug 反馈
POST /api/v1/bug-reports
Content-Type: application/json
Authorization: Bearer {token}

Request Body:
{
    "title": "支付接口超时",
    "description": "用户在支付时遇到'银行系统异常'提示",
    "environment": {
        "platform": "iOS",
        "version": "17.3",
        "app_version": "5.2.1"
    },
    "attachments": ["screenshot_001.png"],
    "contact": {
        "email": "user@example.com",
        "phone": "+86-138****1234"
    }
}

Response 201 Created:
{
    "id": 12345,
    "ticket_number": "TKT-20260312-00123",
    "region": "CN",
    "status": "RECEIVED",
    "created_at": "2026-03-12T10:30:00Z"
}

# 2. 查询工单详情
GET /api/v1/tickets/{ticket_id}
Authorization: Bearer {token}

Response 200 OK:
{
    "id": 12345,
    "ticket_number": "TKT-20260312-00123",
    "title": "支付接口超时",
    "status": "IN_PROGRESS",
    "priority": "P0",
    "assignee": {
        "id": 456,
        "name": "张三",
        "team": "支付团队"
    },
    "rca_report": {
        "root_causes": [
            {
                "cause": "某银行接口超时",
                "confidence": 0.92,
                "evidence": "错误日志显示超时率 95%"
            }
        ]
    },
    "timeline": [...]
}

# 3. 更新工单状态
PATCH /api/v1/tickets/{ticket_id}/status
Authorization: Bearer {token}

Request Body:
{
    "status": "RESOLVED",
    "resolution": "已切换到备用支付通道",
    "code_changes": "https://gitlab.example.com/merge_requests/1234"
}

# 4. 批量查询工单
GET /api/v1/tickets?region=CN&status=IN_PROGRESS&priority=P0&page=1&size=20
Authorization: Bearer {token}

Response 200 OK:
{
    "items": [...],
    "total": 156,
    "page": 1,
    "size": 20,
    "total_pages": 8
}

WebSocket 通信协议

# WebSocket 连接建立
wss://api.example.com/ws/{user_id}?token={jwt_token}

# 客户端 → 服务端消息格式
{
    "type": "subscribe_ticket",
    "ticket_id": 12345,
    "request_id": "req_001"
}

{
    "type": "send_message",
    "ticket_id": 12345,
    "content": "请问进展如何？"
}

# 服务端 → 客户端消息格式

# 1. 工单状态更新
{
    "type": "ticket_status_update",
    "ticket_id": 12345,
    "data": {
        "old_status": "ASSIGNED",
        "new_status": "IN_PROGRESS",
        "assignee": "张三",
        "updated_at": "2026-03-12T10:35:00Z"
    }
}

# 2. RCA 分析进度
{
    "type": "rca_progress",
    "ticket_id": 12345,
    "data": {
        "step": "正在检索日志...",
        "progress": 45,
        "details": "已分析 10,000 条日志，发现 3 个异常"
    }
}

# 3. 新消息通知
{
    "type": "new_message",
    "ticket_id": 12345,
    "data": {
        "id": 789,
        "sender": "张三",
        "content": "已定位问题，正在修复",
        "created_at": "2026-03-12T10:40:00Z"
    }
}

# 4. 心跳保活
Client → Server: {"type": "ping", "timestamp": 1710234567}
Server → Client: {"type": "pong", "timestamp": 1710234567}

# 5. 错误响应
{
    "type": "error",
    "request_id": "req_001",
    "error": {
        "code": "INVALID_TICKET_ID",
        "message": "工单不存在"
    }
}

StateGraph 完整定义

from typing import TypedDict, List, Annotated, Optional
from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import MemorySaver
import operator

# 定义共享状态
class TicketState(TypedDict):
    """工单处理完整状态"""
    # 基础信息
    ticket_id: str
    ticket_number: str
    region: str
    type: str
    priority: str
    
    # 分类结果
    bug_category: str
    tags: List[str]
    
    # RCA 相关
    logs: List[dict]
    anomalies: List[dict]
    rca_report: Optional[dict]
    
    # 解决方案
    solution: Optional[dict]
    code_changes: Optional[str]
    
    # 分发相关
    assignee: Optional[str]
    team: Optional[str]
    
    # 消息历史
    messages: Annotated[List[str], operator.add]
    
    # 状态追踪
    status: str
    retry_count: int
    error_message: Optional[str]

# 创建状态图
workflow = StateGraph(TicketState)

# 添加所有节点
workflow.add_node("classify_bug", classify_bug_node)
workflow.add_node("fetch_logs", fetch_logs_node)
workflow.add_node("detect_anomalies", detect_anomalies_node)
workflow.add_node("analyze_root_cause", analyze_root_cause_node)
workflow.add_node("generate_solution", generate_solution_node)
workflow.add_node("assign_ticket", assign_ticket_node)
workflow.add_node("notify_engineer", notify_engineer_node)
workflow.add_node("human_approval", human_approval_node)

# 定义边（状态转换）
workflow.add_edge(START, "classify_bug")
workflow.add_edge("classify_bug", "fetch_logs")
workflow.add_edge("fetch_logs", "detect_anomalies")
workflow.add_edge("detect_anomalies", "analyze_root_cause")
workflow.add_edge("analyze_root_cause", "generate_solution")
workflow.add_edge("generate_solution", "assign_ticket")
workflow.add_edge("assign_ticket", "human_approval")
workflow.add_edge("human_approval", "notify_engineer")
workflow.add_edge("notify_engineer", END)

# 添加条件分支（重试机制）
def should_retry(state: TicketState) -> str:
    if state["retry_count"] < 3:
        return "retry"
    else:
        return "fail"

workflow.add_conditional_edges(
    "analyze_root_cause",
    should_retry,
    {
        "retry": "fetch_logs",
        "fail": END
    }
)

# 编译为可执行图（启用检查点）
memory = MemorySaver()
app = workflow.compile(checkpointer=memory)

Agent 节点实现示例

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import JsonOutputParser

async def classify_bug_node(state: TicketState) -> dict:
    """Bug 分类节点"""
    llm = ChatOpenAI(model="gpt-4-turbo-preview", temperature=0)
    
    prompt = ChatPromptTemplate.from_messages([
        ("system", """你是一位经验丰富的技术支持专家。请分析 Bug 报告并分类。
        
分类标准：
- category: frontend/backend/database/network/security/other
- priority: P0(紧急)/P1(高)/P2(中)/P3(低)
- tags: 最多 5 个相关标签

以 JSON 格式返回。"""),
        ("human", """标题：{title}
描述：{description}
环境：{environment}

{format_instructions}""")
    ])
    
    parser = JsonOutputParser()
    chain = prompt | llm | parser
    
    result = await chain.ainvoke({
        "title": state["title"],
        "description": state["description"],
        "environment": state.get("environment", "unknown"),
        "format_instructions": parser.get_format_instructions()
    })
    
    return {
        "bug_category": result["category"],
        "priority": result["priority"],
        "tags": result["tags"]
    }

async def analyze_root_cause_node(state: TicketState) -> dict:
    """根因分析节点"""
    llm = ChatOpenAI(model="gpt-4-turbo-preview", temperature=0.3)
    
    prompt = ChatPromptTemplate.from_messages([
        ("system", """你是一位资深 SRE 专家。请分析异常日志并找出根因。

请提供：
1. Top-3 可能的根因（按可能性排序）
2. 每个根因的证据链
3. 置信度评分 (0-1)
4. 推荐的修复方案

以 JSON 格式返回。"""),
        ("human", """异常日志：
{anomalies}

相关错误日志：
{error_logs}

系统上下文：
{context}

{format_instructions}""")
    ])
    
    parser = JsonOutputParser()
    chain = prompt | llm | parser
    
    result = await chain.ainvoke({
        "anomalies": json.dumps(state["anomalies"]),
        "error_logs": json.dumps(state["logs"][:10]),
        "context": state.get("context", ""),
        "format_instructions": parser.get_format_instructions()
    })
    
    return {
        "rca_report": result,
        "status": "ANALYZED"
    }

连接管理器实现

from fastapi import WebSocket, WebSocketDisconnect
from typing import Dict, Set
import json
import asyncio

class ConnectionManager:
    """WebSocket 连接管理器"""
    
    def __init__(self):
        # user_id -> WebSocket
        self.active_connections: Dict[str, WebSocket] = {}
        # ticket_id -> set of user_ids
        self.ticket_subscribers: Dict[str, Set[str]] = {}
        # 心跳超时时间（秒）
        self.timeout = 60
    
    async def connect(self, websocket: WebSocket, user_id: str) -> bool:
        """建立连接"""
        await websocket.accept()
        
        # 发送欢迎消息
        await self.send_personal_message({
            "type": "connected",
            "user_id": user_id,
            "timestamp": asyncio.get_event_loop().time()
        }, user_id)
        
        self.active_connections[user_id] = websocket
        
        # 启动心跳检测
        asyncio.create_task(self.heartbeat(user_id))
        
        return True
    
    def disconnect(self, user_id: str):
        """断开连接"""
        if user_id in self.active_connections:
            del self.active_connections[user_id]
        
        # 清理订阅关系
        for ticket_id in list(self.ticket_subscribers.keys()):
            if user_id in self.ticket_subscribers[ticket_id]:
                self.ticket_subscribers[ticket_id].remove(user_id)
    
    async def send_personal_message(self, message: dict, user_id: str):
        """发送个人消息"""
        if user_id in self.active_connections:
            websocket = self.active_connections[user_id]
            try:
                await websocket.send_text(json.dumps(message))
            except:
                self.disconnect(user_id)
    
    async def subscribe_ticket(self, user_id: str, ticket_id: str):
        """订阅工单更新"""
        if ticket_id not in self.ticket_subscribers:
            self.ticket_subscribers[ticket_id] = set()
        self.ticket_subscribers[ticket_id].add(user_id)
    
    async def broadcast_ticket_update(self, ticket_id: str, update: dict):
        """广播工单更新给所有订阅者"""
        if ticket_id in self.ticket_subscribers:
            message = {
                "type": "ticket_update",
                "ticket_id": ticket_id,
                "data": update
            }
            
            for user_id in self.ticket_subscribers[ticket_id]:
                await self.send_personal_message(message, user_id)
    
    async def heartbeat(self, user_id: str):
        """心跳检测"""
        while True:
            await asyncio.sleep(self.timeout)
            
            if user_id not in self.active_connections:
                break
            
            try:
                # 发送 ping
                await self.send_personal_message({
                    "type": "ping",
                    "timestamp": asyncio.get_event_loop().time()
                }, user_id)
            except:
                self.disconnect(user_id)
                break

manager = ConnectionManager()

流式输出生成器

async def stream_rca_analysis(ticket_id: str, user_id: str):
    """流式输出 RCA 分析过程"""
    
    steps = [
        {"step": "初始化分析任务", "progress": 5},
        {"step": "连接日志系统", "progress": 15},
        {"step": "检索相关日志", "progress": 30},
        {"step": "执行异常检测", "progress": 50},
        {"step": "分析根因", "progress": 70},
        {"step": "生成修复建议", "progress": 85},
        {"step": "完成分析报告", "progress": 100}
    ]
    
    for step in steps:
        # 模拟处理延迟
        await asyncio.sleep(1.5)
        
        # 发送进度更新
        await manager.send_personal_message({
            "type": "rca_progress",
            "ticket_id": ticket_id,
            "data": step
        }, user_id)
    
    # 发送最终结果
    rca_result = await perform_rca_analysis(ticket_id)
    
    await manager.send_personal_message({
        "type": "rca_complete",
        "ticket_id": ticket_id,
        "data": rca_result
    }, user_id)

Elasticsearch 查询优化

from elasticsearch import AsyncElasticsearch
from datetime import datetime, timedelta

class LogSearchService:
    def __init__(self):
        self.es = AsyncElasticsearch(
            hosts=["http://es-cluster:9200"],
            retry_on_timeout=True,
            max_retries=3
        )
    
    async def search_related_logs(
        self,
        service: str,
        time_window: str,
        keywords: List[str],
        size: int = 1000
    ) -> List[dict]:
        """智能日志检索"""
        
        query = {
            "query": {
                "bool": {
                    "must": [
                        {"term": {"service.keyword": service}},
                        {"range": {"@timestamp": {"gte": f"now-{time_window}"}}},
                        {
                            "multi_match": {
                                "query": " ".join(keywords),
                                "fields": [
                                    "message^3",
                                    "error_type^2",
                                    "stack_trace",
                                    "trace_id"
                                ],
                                "fuzziness": "AUTO"
                            }
                        }
                    ],
                    "filter": [
                        {"terms": {"level.keyword": ["ERROR", "FATAL"]}}
                    ]
                }
            },
            "sort": [
                {"@timestamp": {"order": "desc"}}
            ],
            "size": size,
            "_source": {
                "includes": ["@timestamp", "message", "error_type", "stack_trace", "trace_id"]
            }
        }
        
        response = await self.es.search(index="logs-*", body=query)
        return [hit["_source"] for hit in response["hits"]["hits"]]
    
    async def aggregate_errors_by_time(self, service: str, interval: str = "5m"):
        """按时间聚合错误统计"""
        
        query = {
            "size": 0,
            "query": {
                "bool": {
                    "must": [
                        {"term": {"service.keyword": service}},
                        {"range": {"@timestamp": {"gte": "now-1h"}}}
                    ]
                }
            },
            "aggs": {
                "errors_over_time": {
                    "date_histogram": {
                        "field": "@timestamp",
                        "calendar_interval": interval,
                        "min_doc_count": 1
                    },
                    "aggs": {
                        "error_types": {
                            "terms": {
                                "field": "error_type.keyword",
                                "size": 10
                            }
                        }
                    }
                }
            }
        }
        
        response = await self.es.search(index="logs-*", body=query)
        return response["aggregations"]["errors_over_time"]["buckets"]

Docker Compose 完整配置

version: '3.8'

services:
  # FastAPI 后端
  fastapi-app:
    build:
      context: ./backend
      dockerfile: Dockerfile
    ports:
      - "8000:8000"
    environment:
      - DATABASE_URL=postgresql://user:pass@postgres:5432/tickets
      - REDIS_URL=redis://redis:6379/0
      - ELASTICSEARCH_URL=http://elasticsearch:9200
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - LOG_LEVEL=INFO
    depends_on:
      postgres:
        condition: service_healthy
      redis:
        condition: service_started
      elasticsearch:
        condition: service_healthy
    deploy:
      replicas: 3
      resources:
        limits:
          cpus: '2'
          memory: 2G
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
  
  # React 前端
  frontend:
    build:
      context: ./frontend
      dockerfile: Dockerfile
    ports:
      - "3000:80"
    depends_on:
      - fastapi-app
  
  # PostgreSQL 数据库
  postgres:
    image: postgres:15-alpine
    volumes:
      - postgres_data:/var/lib/postgresql/data
    environment:
      - POSTGRES_DB=tickets
      - POSTGRES_USER=user
      - POSTGRES_PASSWORD=pass
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U user"]
      interval: 10s
      timeout: 5s
      retries: 5
  
  # Redis 缓存
  redis:
    image: redis:7-alpine
    command: redis-server --appendonly yes
    volumes:
      - redis_data:/data
    healthcheck:
      test: ["CMD", "redis-cli", "ping"]
      interval: 10s
      timeout: 5s
      retries: 5
  
  # Elasticsearch
  elasticsearch:
    image: docker.elastic.co/elasticsearch/elasticsearch:8.11.0
    environment:
      - discovery.type=single-node
      - "ES_JAVA_OPTS=-Xms2g -Xmx2g"
      - xpack.security.enabled=false
    volumes:
      - es_data:/usr/share/elasticsearch/data
    healthcheck:
      test: ["CMD-SHELL", "curl -f http://localhost:9200/_cluster/health"]
      interval: 30s
      timeout: 10s
      retries: 5
  
  # RabbitMQ 消息队列
  rabbitmq:
    image: rabbitmq:3.12-management
    ports:
      - "15672:15672"
    environment:
      - RABBITMQ_DEFAULT_USER=guest
      - RABBITMQ_DEFAULT_PASS=guest
    volumes:
      - rabbitmq_data:/var/lib/rabbitmq
  
  # Prometheus 监控
  prometheus:
    image: prom/prometheus:v2.50.0
    volumes:
      - ./deploy/prometheus.yml:/etc/prometheus/prometheus.yml
      - prometheus_data:/prometheus
    ports:
      - "9090:9090"
    command:
      - '--config.file=/etc/prometheus/prometheus.yml'
      - '--storage.tsdb.path=/prometheus'
  
  # Grafana 可视化
  grafana:
    image: grafana/grafana:10.3.0
    ports:
      - "3001:3000"
    volumes:
      - grafana_data:/var/lib/grafana
      - ./deploy/grafana/dashboards:/etc/grafana/provisioning/dashboards
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=admin123
    depends_on:
      - prometheus

volumes:
  postgres_data:
  redis_data:
  es_data:
  rabbitmq_data:
  prometheus_data:
  grafana_data:

Kubernetes 部署配置

# deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: fastapi-app
  namespace: ai-agent
spec:
  replicas: 3
  selector:
    matchLabels:
      app: fastapi
  template:
    metadata:
      labels:
        app: fastapi
    spec:
      containers:
      - name: fastapi
        image: registry.example.com/ai-agent:latest
        ports:
        - containerPort: 8000
        env:
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: db-secret
              key: url
        - name: REDIS_URL
          value: "redis://redis-master:6379/0"
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "2Gi"
            cpu: "2000m"
        livenessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: 8000
          initialDelaySeconds: 5
          periodSeconds: 5
---
apiVersion: v1
kind: Service
metadata:
  name: fastapi-service
  namespace: ai-agent
spec:
  selector:
    app: fastapi
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8000
  type: ClusterIP
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: fastapi-hpa
  namespace: ai-agent
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: fastapi-app
  minReplicas: 3
  maxReplicas: 20
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80

预期业务收益

实施路线图