worker-integration

Worker-Agent 集成技能

后台 Worker 与专用 Agent 之间的智能协作。

快速入门

# View agent recommendations for a trigger
npx agentic-flow workers agents ultralearn
npx agentic-flow workers agents optimize

# View performance metrics
npx agentic-flow workers metrics

# View integration stats
npx agentic-flow workers stats --integration

Agent 映射

Worker 会根据触发类型自动分派给最合适的 Agent：

触发类型	主要 Agent	后备 Agent	流水线阶段
ultralearn	researcher, coder	planner	发现 → 模式 → 向量化 → 摘要
optimize	performance-analyzer, coder	researcher	静态分析 → 性能 → 模式
audit	security-analyst, tester	reviewer	安全 → 密钥 → 漏洞扫描
benchmark	performance-analyzer	coder, tester	性能 → 指标 → 报告
testgaps	tester	coder	发现 → 覆盖率 → 缺口
document	documenter, researcher	coder	API 发现 → 模式 → 索引
deepdive	researcher, security-analyst	coder	调用图 → 依赖项 → 跟踪
refactor	coder, reviewer	researcher	复杂度 → 异味 → 模式

基于性能的选择

系统会根据执行历史学习，以改进 Agent 的选择：

// Agent selection considers:
// 1. Quality score (0-1)
// 2. Success rate
// 3. Average latency
// 4. Execution count

const { agent, confidence, reasoning } = selectBestAgent('optimize');
// agent: "performance-analyzer"
// confidence: 0.87
// reasoning: "Selected based on 45 executions with 94.2% success"

记忆键模式

Worker 会使用一致的模式存储结果：

{trigger}/{topic}/{phase}

Examples:
- ultralearn$auth-module$analysis
- optimize$database$performance
- audit$payment$vulnerabilities
- benchmark$api$metrics

基准阈值

系统会根据性能阈值监控 Agent：

{
  "researcher": {
    "p95_latency": "<500ms",
    "memory_mb": "<256MB"
  },
  "coder": {
    "p95_latency": "<300ms",
    "quality_score": ">0.85"
  },
  "security-analyst": {
    "scan_coverage": ">95%",
    "p95_latency": "<1000ms"
  }
}

反馈循环

Worker 会提供反馈，以实现持续改进：

import { workerAgentIntegration } from 'agentic-flow$workers$worker-agent-integration';

// Record execution feedback
workerAgentIntegration.recordFeedback(
  'optimize',           // trigger
  'coder',              // agent
  true,                 // success
  245,                  // latency ms
  0.92                  // quality score
);

// Check compliance
const { compliant, violations } = workerAgentIntegration.checkBenchmarkCompliance('coder');

集成统计信息

$ npx agentic-flow workers stats --integration

Worker-Agent Integration Stats
══════════════════════════════
Total Agents:       6
Tracked Agents:     4
Total Feedback:     156
Avg Quality Score:  0.89

Model Cache Stats
─────────────────
Hits:     1,234
Misses:   45
Hit Rate: 96.5%

配置

在 .claude$settings.json 中启用集成功能：

{
  "workers": {
    "enabled": true,
    "parallel": true,
    "memoryDepositEnabled": true,
    "agentMappings": {
      "ultralearn": ["researcher", "coder"],
      "optimize": ["performance-analyzer", "coder"]
    }
  }
}