Manufacturing
Quality Control Automation
4 autonomous agents inspect, classify, diagnose, and correct quality issues in real-time. 90% defect reduction.
Agentic AI Workflow
4 autonomous agents ensure zero-defect production at scale
The Challenge
Manual inspection was missing defects that reached customers and damaging the brand
An electronics manufacturer had a 2.8% defect rate on its flagship product line, with customer returns costing $4.5M annually. Manual visual inspection caught only 65% of defects, and inspectors suffered from fatigue-related accuracy decline after 2 hours of continuous inspection.
When defects were found, root cause analysis took 3-5 days because process parameters from MES had to be manually correlated with defect patterns. By then, thousands of potentially defective units had already shipped. Corrective actions were implemented through manual machine adjustments that varied by operator skill level.
The manufacturer needed real-time defect detection with automated root cause analysis and corrective action.
The Solution
Agents that inspect every unit, classify defects, find root causes, and correct processes
Vijan.AI deployed 4 agents on the production line. The Vision Inspector analyzes high-resolution images from 12 production line cameras, detecting defects at 99.5% accuracy. The Defect Classifier categorizes each defect by type, severity, and location. The Root Cause agent correlates defect patterns with process parameters from MES (temperature, pressure, speed, material batch) in real-time, identifying causal relationships within minutes. The Corrective Action agent automatically adjusts machine settings for parameter-driven defects or alerts operators for mechanical issues.
Autonomous Agents
How each agent reasons, decides, and acts
Step 1 · Inspection
Quality Inspection Agent
Automated Visual Inspection
Performs high-speed visual inspection using computer vision models to detect surface defects, dimensional variances, and assembly errors, autonomously routing defective units for rework and conforming units to statistical process control.
Input
Product images and sensor measurements from inline inspection stations
Output
Pass/fail decisions with defect classifications and coordinates
- Invokes vision API with multi-angle product images for defect detection using trained CNN models
- Queries defect database to classify anomalies by type, severity, and root cause category
- Autonomous decision: accept, rework, or scrap based on defect severity and repairability
- Routes accepted units to SPC Controller and rejected units to Bottleneck Analyzer for process insights
Step 2 · Control
Quality Control Agent
Statistical Process Control
Monitors process variation using real-time SPC charts, autonomously detecting out-of-control conditions and triggering process adjustments to prevent defect propagation before reaching compliance review.
Input
Measurement data from accepted units with timestamps and batch IDs
Output
Process control status with corrective action recommendations
- Calls SPC engine to update X-bar and R charts with rolling window of measurement data
- Computes control limits dynamically based on process capability indices (Cp, Cpk)
- Autonomous decision: continue production, halt line for adjustment, or flag for engineering review
- Sends stable process confirmations to Compliance Tracker for final certification
Step 3 · Analysis
Bottleneck Detector
Defect Root Cause Analysis
Analyzes rejected units to identify process bottlenecks and systematic failure modes, autonomously recommending process improvements and merging findings with compliance data for holistic quality assessment.
Input
Defect data with station IDs, timestamps, and failure modes
Output
Root cause reports with process improvement recommendations
- Queries process map tool to identify upstream stations contributing to defect clusters
- Analyzes cycle time data to detect correlation between throughput pressure and quality degradation
- Autonomous decision: recommend process parameter tuning, maintenance, or design changes
- Merges root cause insights with compliance tracking for comprehensive quality gate decision
Step 4 · Certification
Compliance Tracker
Regulatory Compliance Certification
Validates product batches against ISO 9001, industry-specific standards, and customer specifications, autonomously issuing certificates of conformance and maintaining audit-ready traceability records.
Input
SPC confirmations and root cause analysis merged at quality gate
Output
Certificates of conformance with full traceability documentation
- Calls ISO standards API to verify all inspection points meet required tolerances and procedures
- Queries certification database to generate CoC documents with batch genealogy and test results
- Autonomous decision: release batch, hold for additional testing, or reject with disposition codes
- Stores complete audit trail including inspection images, SPC data, and corrective actions taken
Results
Measurable impact within 90 days of deployment
Defect Reduction
Outbound defect rate reduced from 2.8% to 0.28%. Customer returns dropped 85%, saving $3.8M annually.
Detection Accuracy
AI vision inspection catches 99.5% of defects vs. 65% for manual inspection. Inspects 100% of units, not samples.
Root Cause Time
Root cause identification reduced from 3-5 days to under 5 minutes through automated parameter correlation.
Batch Escapes
No defective batches have reached customers since deployment. Real-time correction prevents defect propagation.
Implementation
From pilot to production in 12 weeks
Agent Design & Tool Integration
Defined agent capabilities, connected ML model, rules engine, graph DB, and chargeback API tools. Configured orchestrator routing logic.
Shadow Mode & Autonomous Tuning
Agents ran in shadow mode on 10% of transactions. Tuned decision thresholds, tool call parameters, and feedback loop retraining frequency.
Full Autonomous Deployment
Production rollout across all channels. Agents operating fully autonomously with human-in-the-loop for critical escalations only.
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