Cloudflare Workers AI平台全面升级：从边缘计算到智能体基础设施

// Dynamic Workers 示例：AI生成并执行数据处理代码
export default {
  async fetch(request, env) {
    // AI根据用户需求生成代码
    const userRequest = await request.json();
    const generatedCode = await generateCodeWithAI(userRequest.prompt, env);
    
    // 在安全沙箱中执行生成的代码
    const result = await env.DYNAMIC_WORKER.run(generatedCode, {
      input: userRequest.data,
      timeout: 30000,  // 30秒超时
      memory: 128,     // 128MB内存限制
    });
    
    return Response.json({ result });
  }
};

async function generateCodeWithAI(prompt, env) {
  const response = await env.AI.run('@cf/deepseek-ai/deepseek-v4-flash', {
    messages: [
      { role: 'system', content: '生成纯函数代码，接收input参数返回处理结果' },
      { role: 'user', content: prompt }
    ],
    thinking: { type: 'enabled', level: 'high' }
  });
  return response.result;
}

// 使用Cloudflare Agent SDK创建智能体
import { Agent, AgentNamespace } from 'cloudflare:agents';

export class DataAnalysisAgent extends Agent {
  // Agent初始化
  async onStart() {
    this.state = {
      conversations: [],
      tools: ['search', 'calculator', 'code_executor'],
      model: '@cf/deepseek-ai/deepseek-v4-pro'
    };
  }
  
  // 处理用户消息
  async onMessage(message: string) {
    // 1. 理解意图
    const intent = await this.analyzeIntent(message);
    
    // 2. 规划执行步骤
    const plan = await this.createPlan(intent);
    
    // 3. 执行工具调用
    for (const step of plan.steps) {
      const result = await this.executeTool(step.tool, step.params);
      step.result = result;
    }
    
    // 4. 生成最终回复
    const response = await this.generateResponse(plan);
    return response;
  }
  
  // 工具执行器
  async executeTool(toolName: string, params: any) {
    switch (toolName) {
      case 'search':
        return await this.env.SEARCH_API.query(params.query);
      case 'calculator':
        return eval(params.expression);  // 简化示例
      case 'code_executor':
        return await this.env.DYNAMIC_WORKER.run(params.code, {
          input: params.input
        });
      default:
        throw new Error(`Unknown tool: ${toolName}`);
    }
  }
}

export default {
  async fetch(request, env) {
    return AgentNamespace.handle(request, env, DataAnalysisAgent);
  }
};

# .github/workflows/agent-ci.yml
name: Agent CI/CD
on:
  push:
    paths: ['agents/**']

jobs:
  test-agent:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Install Wrangler
        run: npm install -g wrangler
      - name: Run Agent Tests
        run: |
          # 单元测试Agent工具
          wrangler agents test --unit
          # 集成测试Agent行为
          wrangler agents test --integration
          # 安全扫描Agent工具
          wrangler agents test --security
      - name: Deploy to Staging
        if: github.ref == 'refs/heads/main'
        run: wrangler agents deploy --env staging
      - name: Production Deploy
        if: github.ref == 'refs/heads/main'
        run: wrangler agents deploy --env production

// Agent可观测性配置
export class ObservableAgent extends Agent {
  async onMessage(message: string) {
    // 启用执行追踪
    const trace = this.startTrace('message_processing');
    
    try {
      trace.log('intent_analysis_start', { message });
      const intent = await this.analyzeIntent(message);
      trace.log('intent_analysis_complete', { intent });
      
      trace.log('tool_execution_start', { tools: intent.requiredTools });
      const results = await this.executeTools(intent.requiredTools);
      trace.log('tool_execution_complete', { results });
      
      trace.log('response_generation_start');
      const response = await this.generateResponse(results);
      trace.log('response_generation_complete', { responseLength: response.length });
      
      trace.end('success');
      return response;
    } catch (error) {
      trace.end('error', { error: error.message });
      throw error;
    }
  }
}

// 安全沙箱配置示例
export class SecureAgentSandbox {
  async executeUntrustedCode(code: string, userInput: any) {
    // 创建安全沙箱环境
    const sandbox = await this.env.SANDBOX.create({
      // 资源限制
      limits: {
        cpu: 1,           // 1 vCPU
        memory: 256,      // 256MB
        timeout: 60000,   // 60秒
        network: 'restricted'  // 限制网络访问
      },
      // Zero Trust出站代理配置
      outbound: {
        proxy: 'zero-trust',
        // 仅允许访问白名单域
        allowedDomains: [
          'api.internal-service.com',
          'cdn.trusted-assets.com'
        ],
        // 动态注入凭证（不暴露给沙箱内代码）
        credentialInjection: {
          type: 'header',
          name: 'Authorization',
          value: this.env.SECRET_API_TOKEN
        }
      }
    });
    
    // 在安全环境中执行AI生成的代码
    const result = await sandbox.run(code, { input: userInput });
    
    // 审计执行日志
    const auditLog = sandbox.getAuditLog();
    await this.env.AUDIT_STORE.put(
      `audit-${Date.now()}`,
      JSON.stringify(auditLog)
    );
    
    return result;
  }
}

// 智能模型路由：根据任务复杂度选择模型
export class ModelRouter {
  private models = {
    lightweight: '@cf/deepseek-ai/deepseek-v4-flash',
    balanced: '@cf/zai-org/glm-5.3-flash',
    powerful: '@cf/deepseek-ai/deepseek-v4-pro'
  };
  
  async route(task: string, complexity: number) {
    let model;
    if (complexity < 3) {
      model = this.models.lightweight;   // 简单任务用轻量模型
    } else if (complexity < 7) {
      model = this.models.balanced;      // 中等任务用均衡模型
    } else {
      model = this.models.powerful;      // 复杂任务用强力模型
    }
    
    const result = await this.env.AI.run(model, {
      messages: [{ role: 'user', content: task }],
      thinking: { 
        type: 'enabled', 
        level: complexity > 7 ? 'max' : 'high'
      }
    });
    
    return { result, model, cost: this.estimateCost(model, result.usage) };
  }
  
  estimateCost(model: string, usage: any) {
    const pricing = {
      'deepseek-v4-flash': { input: 0.14, output: 0.29 },
      'glm-5.3-flash': { input: 0.20, output: 0.40 },
      'deepseek-v4-pro': { input: 0.43, output: 0.87 }
    };
    const p = pricing[model.split('/').pop()] || pricing['deepseek-v4-flash'];
    return (usage.input_tokens / 1e6 * p.input) + (usage.output_tokens / 1e6 * p.output);
  }
}

// 多级缓存策略降低推理成本
export class CachedAIInference {
  async infer(prompt: string, options: any = {}) {
    // 第一级：KV缓存（精确匹配）
    const cacheKey = this.hashKey(prompt, options);
    const cached = await this.env.AI_CACHE.get(cacheKey);
    if (cached) {
      return { ...JSON.parse(cached), source: 'kv_cache' };
    }
    
    // 第二级：语义缓存（相似匹配）
    const semanticMatch = await this.findSemanticMatch(prompt);
    if (semanticMatch && semanticMatch.similarity > 0.95) {
      return { ...semanticMatch.response, source: 'semantic_cache' };
    }
    
    // 第三级：实时推理
    const result = await this.env.AI.run(options.model || '@cf/deepseek-ai/deepseek-v4-flash', {
      messages: [{ role: 'user', content: prompt }],
      ...options
    });
    
    // 写入缓存
    await this.env.AI_CACHE.put(cacheKey, JSON.stringify(result), {
      expirationTtl: 3600  // 1小时过期
    });
    
    return { ...result, source: 'live_inference' };
  }
  
  async findSemanticMatch(prompt: string) {
    // 使用嵌入模型进行语义搜索
    const embedding = await this.env.AI.run('@cf/baai/bge-large-en', {
      text: [prompt]
    });
    return await this.env.VECTOR_INDEX.query(embedding.data[0], {
      topK: 1,
      threshold: 0.95
    });
  }
  
  hashKey(prompt: string, options: any): string {
    const crypto = require('crypto');
    return crypto
      .createHash('sha256')
      .update(JSON.stringify({ prompt, model: options.model }))
      .digest('hex');
  }
}

// 完整的AI Agent应用：智能数据分析助手
import { Agent } from 'cloudflare:agents';

interface AnalysisResult {
  summary: string;
  insights: string[];
  visualizations: any[];
  recommendations: string[];
}

export class DataAnalysisAssistant extends Agent {
  async onStart() {
    this.state = {
      model: '@cf/deepseek-ai/deepseek-v4-pro',
      flashModel: '@cf/deepseek-ai/deepseek-v4-flash',
      maxHistory: 10
    };
  }

  async analyze(data: any[], query: string): Promise<AnalysisResult> {
    const trace = this.startTrace('full_analysis');
    
    // 步骤1：使用Flash模型快速理解数据结构
    trace.log('data_understanding_start');
    const dataStructure = await this.understandData(data);
    trace.log('data_understanding_complete', { structure: dataStructure });
    
    // 步骤2：使用Pro模型进行深度分析
    trace.log('deep_analysis_start');
    const analysis = await this.env.AI.run(this.state.model, {
      messages: [
        {
          role: 'system',
          content: '你是数据分析专家。基于提供的数据和分析任务，给出深度洞察。'
        },
        {
          role: 'user',
          content: `数据概览: ${JSON.stringify(dataStructure)}
分析任务: ${query}`
        }
      ],
      thinking: { type: 'enabled', level: 'max' }
    });
    trace.log('deep_analysis_complete');
    
    // 步骤3：生成可视化代码并执行
    trace.log('visualization_start');
    const vizCode = await this.generateVisualization(dataStructure, analysis);
    const vizResult = await this.env.DYNAMIC_WORKER.run(vizCode, {
      input: { data, analysis }
    });
    trace.log('visualization_complete');
    
    // 步骤4：生成最终报告
    const report = this.formatReport(analysis, vizResult);
    trace.end('success');
    
    return report;
  }

  async understandData(data: any[]) {
    const sample = data.slice(0, 100);
    const result = await this.env.AI.run(this.state.flashModel, {
      messages: [
        { role: 'system', content: '分析数据结构，返回字段名、类型、统计摘要。JSON格式。' },
        { role: 'user', content: JSON.stringify(sample) }
      ]
    });
    return JSON.parse(result.result);
  }

  async generateVisualization(dataStructure: any, analysis: any) {
    const result = await this.env.AI.run(this.state.flashModel, {
      messages: [
        {
          role: 'system',
          content: '生成纯JavaScript代码，使用input.data和input.analysis创建文本图表。返回可执行的函数。'
        },
        {
          role: 'user',
          content: `数据结构: ${JSON.stringify(dataStructure)}
分析结果: ${analysis.result}`
        }
      ]
    });
    return result.result;
  }

  formatReport(analysis: any, vizResult: any): AnalysisResult {
    return {
      summary: analysis.result,
      insights: ['关键指标已识别', '趋势分析完成'],
      visualizations: [vizResult],
      recommendations: ['建议进一步分析异常值', '考虑增加时间维度对比']
    };
  }
}