worker-integration

// 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"

{trigger}/{topic}/{phase}

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

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

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');

$ 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%

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