Worker-Agent Integration Skill Intelligent coordination between background workers and specialized agents. Quick Start

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 Mappings Workers automatically dispatch to optimal agents based on trigger type: Trigger Primary Agents Fallback Pipeline Phases ultralearn researcher, coder planner discovery → patterns → vectorization → summary optimize performance-analyzer, coder researcher static-analysis → performance → patterns audit security-analyst, tester reviewer security → secrets → vulnerability-scan benchmark performance-analyzer coder, tester performance → metrics → report testgaps tester coder discovery → coverage → gaps document documenter, researcher coder api-discovery → patterns → indexing deepdive researcher, security-analyst coder call-graph → deps → trace refactor coder, reviewer researcher complexity → smells → patterns Performance-Based Selection The system learns from execution history to improve agent selection: // 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" Memory Key Patterns Workers store results using consistent patterns: {trigger}/{topic}/{phase} Examples: - ultralearn$auth-module$analysis - optimize$database$performance - audit$payment$vulnerabilities - benchmark$api$metrics Benchmark Thresholds Agents are monitored against performance thresholds: { "researcher" : { "p95_latency" : "<500ms" , "memory_mb" : "<256MB" } , "coder" : { "p95_latency" : "<300ms" , "quality_score" : ">0.85" } , "security-analyst" : { "scan_coverage" : ">95%" , "p95_latency" : "<1000ms" } } Feedback Loop Workers provide feedback for continuous improvement: 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' ) ; Integration Statistics $ 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 % Configuration Enable integration features in .claude$settings.json : { "workers" : { "enabled" : true , "parallel" : true , "memoryDepositEnabled" : true , "agentMappings" : { "ultralearn" : [ "researcher" , "coder" ] , "optimize" : [ "performance-analyzer" , "coder" ] } } }