2026 年 AI Agent 自主决策系统实战指南：从 Claude Fable 5 到生产级部署

# MCP Server for PostgreSQL
from mcp.server import Server
from mcp.types import Tool, TextContent
import asyncpg
import json

app = Server("sales-data-mcp")

@app.tool("query_sales")
async def query_sales(
    start_date: str,
    end_date: str,
    product_category: str = None
) -> TextContent:
    """查询销售数据
    
    Args:
        start_date: 开始日期 (YYYY-MM-DD)
        end_date: 结束日期 (YYYY-MM-DD)
        product_category: 产品类别（可选）
    """
    conn = await asyncpg.connect(
        host="localhost",
        database="sales_db",
        user="agent_user",
        password="secure_password"
    )
    
    query = """
    SELECT 
        product_id,
        product_name,
        category,
        sale_date,
        quantity,
        revenue
    FROM sales
    WHERE sale_date BETWEEN $1 AND $2
    """
    
    params = [start_date, end_date]
    
    if product_category:
        query += " AND category = $3"
        params.append(product_category)
    
    rows = await conn.fetch(query, *params)
    await conn.close()
    
    # 转换为 JSON
    result = [dict(row) for row in rows]
    return TextContent(text=json.dumps(result, ensure_ascii=False))

@app.tool("get_product_summary")
async def get_product_summary(
    start_date: str,
    end_date: str
) -> TextContent:
    """获取产品汇总统计"""
    conn = await asyncpg.connect(
        host="localhost",
        database="sales_db",
        user="agent_user",
        password="secure_password"
    )
    
    query = """
    SELECT 
        product_name,
        category,
        SUM(quantity) as total_quantity,
        SUM(revenue) as total_revenue,
        AVG(revenue) as avg_revenue,
        COUNT(*) as sale_count
    FROM sales
    WHERE sale_date BETWEEN $1 AND $2
    GROUP BY product_name, category
    ORDER BY total_revenue DESC
    """
    
    rows = await conn.fetch(query, start_date, end_date)
    await conn.close()
    
    result = [dict(row) for row in rows]
    return TextContent(text=json.dumps(result, ensure_ascii=False))

if __name__ == "__main__":
    app.run()

// 使用扩展思考的数据分析 Agent
import Anthropic from '@anthropic-ai/sdk';

const client = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY,
});

async function analyzeSalesData() {
  // 第一步：获取原始数据
  const rawData = await fetchSalesData('2026-01-01', '2026-03-31');
  
  // 第二步：使用扩展思考进行深度分析
  const response = await client.messages.create({
    model: 'claude-fable-5',
    max_tokens: 16000,
    thinking: {
      type: 'enabled',
      budget_tokens: 8000  // 分配 8K token 用于思考
    },
    messages: [{
      role: 'user',
      content: `分析以下销售数据，重点关注：
1. 整体趋势（环比、同比）
2. 表现最好和最差的产品
3. 异常值识别（突然的销量变化）
4. 潜在的业务洞察

数据：
${JSON.stringify(rawData)}`
    }]
  });
  
  // 提取思考过程和最终答案
  const thinkingBlocks = response.content.filter(
    block => block.type === 'thinking'
  );
  const answerBlocks = response.content.filter(
    block => block.type === 'text'
  );
  
  console.log('=== 思考过程 ===');
  console.log(thinkingBlocks.map(b => b.thinking).join('\n'));
  
  console.log('\n=== 分析结果 ===');
  console.log(answerBlocks.map(b => b.text).join('\n'));
  
  return {
    thinking: thinkingBlocks.map(b => b.thinking),
    analysis: answerBlocks.map(b => b.text)
  };
}

// 多步推理示例：自动诊断问题
async function diagnoseSalesIssue(productName: string) {
  const response = await client.messages.create({
    model: 'claude-fable-5',
    max_tokens: 16000,
    thinking: {
      type: 'enabled',
      budget_tokens: 10000
    },
    tools: [
      {
        type: 'mcp_tool',
        name: 'query_sales',
        description: '查询销售数据'
      },
      {
        type: 'mcp_tool',
        name: 'get_customer_feedback',
        description: '获取客户反馈'
      },
      {
        type: 'mcp_tool',
        name: 'check_inventory',
        description: '检查库存状态'
      }
    ],
    messages: [{
      role: 'user',
      content: `${productName} 的销量最近下降了 30%，请诊断原因。`
    }]
  });
  
  // Agent 会自动进行多步推理：
  // 1. 思考：可能的原因有哪些？
  // 2. 行动：查询销售数据，确认下降趋势
  // 3. 观察：发现下降从 2 周前开始
  // 4. 思考：2 周前发生了什么？
  // 5. 行动：检查库存，发现缺货
  // 6. 观察：确实在 2 周前库存耗尽
  // 7. 思考：为什么缺货？
  // 8. 行动：查询供应商数据
  // 9. 观察：供应商延迟交货
  // 10. 结论：销量下降是因为缺货，根因是供应链问题
  
  return response;
}

# Mem0 + Zep 组合记忆系统
from mem0 import Memory
from zep import ZepClient
import asyncio

class AgentMemory:
    """组合记忆系统"""
    
    def __init__(self):
        # Mem0 用于短期记忆
        self.mem0 = Memory()
        
        # Zep 用于长期记忆
        self.zep = ZepClient(
            api_url="http://localhost:8000",
            api_key="your-api-key"
        )
    
    async def remember(self, user_id: str, conversation: list):
        """记住当前对话"""
        # 存入 Mem0（短期记忆）
        self.mem0.add(
            user_id=user_id,
            messages=conversation
        )
        
        # 存入 Zep（长期记忆）
        await self.zep.memory.add(
            session_id=user_id,
            messages=conversation
        )
    
    async def recall(self, user_id: str, query: str):
        """检索相关记忆"""
        # 从 Mem0 检索短期记忆
        short_term = self.mem0.search(
            user_id=user_id,
            query=query,
            limit=5
        )
        
        # 从 Zep 检索长期记忆
        long_term = await self.zep.memory.search(
            session_id=user_id,
            text=query,
            limit=5
        )
        
        return {
            "short_term": short_term,
            "long_term": long_term
        }

# 使用示例
async def main():
    memory = AgentMemory()
    user_id = "user_123"
    
    # 记住对话
    conversation = [
        {"role": "user", "content": "我喜欢用 Python 写数据分析"},
        {"role": "assistant", "content": "好的，我记住了。你偏好 Python 和数据分析相关的任务。"}
    ]
    await memory.remember(user_id, conversation)
    
    # 检索记忆
    query = "用户喜欢什么编程语言？"
    memories = await memory.recall(user_id, query)
    print(memories)

asyncio.run(main())

# Kubernetes 部署配置
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-agent
spec:
  replicas: 3
  selector:
    matchLabels:
      app: ai-agent
  template:
    metadata:
      labels:
        app: ai-agent
    spec:
      containers:
      - name: agent
        image: your-registry/ai-agent:latest
        env:
        - name: ANTHROPIC_API_KEY
          valueFrom:
            secretKeyRef:
              name: anthropic-secret
              key: api-key
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: db-secret
              key: url
        resources:
          requests:
            memory: "2Gi"
            cpu: "1"
          limits:
            memory: "4Gi"
            cpu: "2"
        livenessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: 8080
          initialDelaySeconds: 5
          periodSeconds: 5
---
apiVersion: v1
kind: Service
metadata:
  name: ai-agent-service
spec:
  selector:
    app: ai-agent
  ports:
  - port: 80
    targetPort: 8080
  type: LoadBalancer

# 成本监控和优化工具
import anthropic
from datetime import datetime, timedelta

class CostTracker:
    """跟踪和优化 Agent 成本"""
    
    def __init__(self):
        self.daily_budget = 100  # 每日预算 $100
        self.costs = []
    
    def track_usage(self, model: str, input_tokens: int, output_tokens: int):
        """记录每次 API 调用的成本"""
        pricing = {
            'claude-fable-5': {'input': 15, 'output': 75},
            'claude-opus-4': {'input': 3, 'output': 15},
            'claude-haiku-4-5': {'input': 0.25, 'output': 1.25}
        }
        
        price = pricing[model]
        cost = (input_tokens / 1_000_000) * price['input'] + \
               (output_tokens / 1_000_000) * price['output']
        
        self.costs.append({
            'timestamp': datetime.now(),
            'model': model,
            'cost': cost
        })
        
        return cost
    
    def get_daily_cost(self):
        """获取今日成本"""
        today = datetime.now().date()
        today_costs = [
            c['cost'] for c in self.costs
            if c['timestamp'].date() == today
        ]
        return sum(today_costs)
    
    def should_downgrade(self) -> bool:
        """判断是否应该降级到更便宜的模型"""
        daily_cost = self.get_daily_cost()
        return daily_cost > self.daily_budget * 0.8  # 达到 80% 预算时降级

# 使用示例
tracker = CostTracker()

async def call_model_with_cost_control(user_message: str):
    client = anthropic.Anthropic()
    
    # 判断是否应该降级
    if tracker.should_downgrade():
        model = 'claude-opus-4'  # 降级到 Opus
    else:
        model = 'claude-fable-5'  # 使用 Fable 5
    
    response = client.messages.create(
        model=model,
        max_tokens=8192,
        messages=[{'role': 'user', 'content': user_message}]
    )
    
    # 跟踪成本
    cost = tracker.track_usage(
        model=model,
        input_tokens=response.usage.input_tokens,
        output_tokens=response.usage.output_tokens
    )
    
    cost_str = "{:.4f}".format(cost)
    total_str = "{:.2f}".format(tracker.get_daily_cost())
    print("本次调用成本: $" + cost_str)
    print("今日总成本: $" + total_str)
    
    return response