Quality Investigation Digital Worker
Orchestrates 8 specialized AI agents that simultaneously analyze production data, detect statistical anomalies via SPC charts, correlate component lots, search historical knowledge bases, assess supplier risk, and auto-generate 8D reports, CAPA documents, and customer notifications with human-in-the-loop approval gates..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Quality Investigation Mission Control - Initial configuration interface displaying quick start templates for common quality scenarios, investigation parameter inputs including work order ID, product details, facility selection, defect type classification, and priority level assignment. The agent team panel shows 6 specialized AI agents in ready status with LLM configuration options and system health monitoring.
Real-Time Multi-Agent Orchestration - Live collaboration view showing the investigation workflow progression through six stages (Initialize, Detect, Analyze, Synthesize, Document, Complete). The communication bus displays inter-agent message exchanges with payload details, while the chain of thought panel reveals agent reasoning processes including observations and equipment insights for transparent decision-making.
Investigation Workspace - Comprehensive root cause analysis results displaying the primary root cause (Component C47 voltage regulator TPS62A01DRLR exhibiting voltage output at upper specification limit due to supplier manufacturing process drift) with 87% confidence. Alternative hypotheses are systematically ruled out with supporting evidence, impact assessment quantifies 247 affected units and $127,000 estimated cost, and the confidence analysis panel breaks down contributing factors from statistical correlation, historical patterns, supplier documentation, and sensor data validation.
Executive Briefing Summary - AI-generated investigation summary showing completion in 20 seconds with 87% confidence using 8 specialized agents. The scenario summary presents structured input (quality issue investigation for work order WO-46102, Industrial Controller Board v4, yield drop on Line 3), process methodology (multi-agent AI analysis of MES, SPC, supplier and IoT sensor data in 20 seconds vs 18.5h manual, with 15 API calls and 1 automated decision), and output results (root cause identified, 87% confidence score, 247 units contained, 3 compliance documents generated including 8D, CAPA, and NCR, plus 4 future issue predictions).
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Complex quality investigations require coordinated analysis across multiple specialized domains. Without central coordination, agents may duplicate work, miss dependencies, or produce conflicting conclusions.
Core Logic
Decomposes investigation tasks into specialized sub-tasks, broadcasts work assignments to appropriate agents, monitors progress across all agents, aggregates findings into coherent conclusions, and escalates to human reviewers when confidence thresholds are not met. Maintains investigation state and ensures all evidence is synthesized before generating final outputs.
Sentinel Agent (Anomaly Detection)
Identifying quality deviations in high-volume manufacturing requires continuous monitoring of hundreds of parameters against dynamic control limits, which is impossible to do manually in real-time.
Core Logic
Performs Statistical Process Control (SPC) analysis on production data, monitoring UCL/LCL control limits, detecting out-of-spec conditions, and identifying trending parameters before they breach thresholds. Correlates test failures with production timestamps, equipment IDs, and shift patterns to pinpoint anomaly origins.
Investigator Agent (Root Cause Analysis)
Determining root cause requires correlating failures across component lots, suppliers, equipment, operators, and environmental conditions—a combinatorial analysis that humans cannot perform exhaustively.
Core Logic
Applies statistical correlation analysis (Pearson, Spearman) to identify relationships between failure rates and potential causes. Tests hypotheses using fishbone analysis patterns, calculates correlation coefficients with p-values, and ranks root cause candidates by statistical significance. Achieves correlation confidence >0.97 for definitive root causes.
Knowledge Agent (Historical Case Retrieval)
Organizations accumulate years of quality investigation knowledge in siloed documents, making it difficult to leverage past solutions for current problems.
Core Logic
Performs vector similarity search against historical investigation database, retrieving cases with >90% similarity to current incident. Extracts proven resolutions, containment actions, and lessons learned from past investigations. Synthesizes applicable knowledge into actionable recommendations with confidence scores.
Supplier Liaison Agent
Component quality issues often trace to supplier manufacturing processes, but accessing and interpreting supplier data (Certificates of Analysis, lot genealogy) is time-consuming and requires specialized knowledge.
Core Logic
Queries supplier portals and internal procurement systems for component lot data, Certificates of Analysis (CoA), and historical supplier performance. Analyzes CoA trends to identify borderline-pass conditions indicating process drift. Calculates supplier risk scores incorporating quality history, delivery performance, and financial stability.
Documentation Agent
Regulated industries require extensive compliance documentation (8D reports, CAPA plans, customer notifications) that must follow strict templates and include specific evidence—a time-consuming manual task.
Core Logic
Auto-generates industry-compliant documentation using investigation findings. Produces 8D problem-solving reports with all required sections, CAPA (Corrective and Preventive Action) plans with verification steps, and customer notification drafts. Ensures all documents meet ISO, IATF, and FDA regulatory requirements.
Predictive Agent (ML Failure Prediction)
Reactive quality management catches problems after defects occur. Predicting failures before they happen requires analyzing complex patterns across hundreds of process variables.
Core Logic
Applies machine learning models to production data streams, identifying leading indicators of quality degradation. Performs trend analysis and risk modeling to forecast failure probability. Generates predictive alerts for quality, supply chain, and equipment maintenance issues with confidence intervals.
Digital Twin Agent (Equipment Simulation)
Equipment-related quality issues require understanding complex interactions between machine parameters, environmental conditions, and product characteristics that are difficult to isolate in production.
Core Logic
Monitors real-time equipment state including temperature, vibration, power consumption, and efficiency metrics. Correlates equipment behavior with quality outcomes using digital twin models. Performs what-if simulations to test hypotheses about equipment contribution to defects without disrupting production.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization