From sensor data to predictive insights in milliseconds.
Monday showed 3 core prompts for anomaly detection, predictive maintenance, and quality control. Tuesday automated data ingestion and agent orchestration. Wednesday mapped team workflows across plant managers, data engineers, and ML ops. Today: complete production architecture. Edge computing, multi-agent coordination, real-time analytics, and ML pipelines that scale from pilot line to enterprise manufacturing.
Key Assumptions
System Requirements
Functional
- Ingest sensor data from 1,000+ data points per machine at 1-100Hz
- Detect anomalies in real-time with <100ms latency for critical events
- Predict machine failures 24-72 hours in advance with 85%+ accuracy
- Generate quality control insights from vision + sensor fusion
- Orchestrate maintenance workflows across plant teams
- Provide digital twin visualization with real-time state sync
- Support batch analytics for production optimization
Non-Functional (SLOs)
💰 Cost Targets: {"per_machine_per_month_usd":15,"per_prediction_usd":0.02,"storage_per_gb_per_month_usd":0.1}
Agent Layer
planner
L4Decomposes incoming sensor data streams, routes to specialized agents, coordinates multi-step workflows
🔧 anomaly_detector, predictive_maintenance, quality_control, work_order_creator
⚡ Recovery: Retry with exponential backoff (3 attempts), Fallback to rule-based routing if ML planner fails, Queue for human review if all agents fail
executor
L3Runs primary data processing workflows: feature extraction, anomaly detection, prediction generation
🔧 feature_store.read, ml_model.infer, time_series_analyzer, feature_store.write
⚡ Recovery: Switch to cached features if feature store unavailable, Use last known good model version if inference fails, Degrade to statistical methods if ML unavailable
evaluator
L3Validates output quality, checks prediction confidence, triggers retraining when drift detected
🔧 statistical_validator, confidence_scorer, drift_detector, human_review_trigger
⚡ Recovery: Flag low-confidence predictions for human review, Trigger model retraining if accuracy drops below 80%, Escalate to domain expert if drift is severe
guardrail
L2Enforces safety policies, prevents false positives, validates before triggering critical actions
🔧 policy_checker, risk_assessor, false_positive_filter, audit_logger
⚡ Recovery: Default to conservative (reject) if policy engine fails, Require human approval for high-risk actions, Log all decisions for post-incident review
anomaly_detector
L3Real-time anomaly detection using statistical methods + ML models
🔧 isolation_forest, autoencoder_model, statistical_outlier_detector, feature_store.read
⚡ Recovery: Fallback to z-score method if ML fails, Use ensemble of 3 methods, majority vote, Alert if detection latency exceeds 100ms
predictive_maintenance
L4Predict machine failures 24-72 hours in advance using survival analysis + deep learning
🔧 survival_model, lstm_predictor, feature_store.read, maintenance_recommender
⚡ Recovery: Use rule-based heuristics if ML unavailable, Require 2/3 model agreement for high-confidence predictions, Flag low-confidence predictions for expert review
ML Layer
Feature Store
Update: Real-time (streaming) + hourly batch aggregations
- • vibration_rms_1h
- • temperature_mean_1h
- • pressure_std_1h
- • cycle_count_1d
- • power_consumption_1h
- • quality_defect_rate_1d
- • maintenance_days_since_last
- • operating_hours_total
Model Registry
Strategy: Semantic versioning with A/B testing for major versions
- • anomaly_detector_v3
- • failure_predictor_v2
- • quality_classifier_v1
Observability Stack
Real-time monitoring, tracing & alerting
0 activeDeployment Variants
Startup Architecture
Fast to deploy, cost-efficient, scales to 100 competitors
Infrastructure
Risks & Mitigations
⚠️ Edge gateway hardware failure in harsh factory environment
Medium✓ Mitigation: Redundant gateways per line, industrial-grade hardware (IP65 rated), local buffering (24h), automated failover, quarterly hardware audits
⚠️ Model drift due to changing production processes
High✓ Mitigation: Continuous monitoring (PSI, KL divergence), automated retraining pipeline (weekly), A/B testing before deployment, human-in-the-loop validation
⚠️ False positives causing alert fatigue
Medium✓ Mitigation: Guardrail agent with adaptive thresholds, human feedback loop, precision-focused tuning (target <5% FP rate), escalation for repeated FPs
⚠️ Integration failure with legacy SCADA systems
Medium✓ Mitigation: Protocol abstraction layer, extensive testing in staging, fallback to manual data entry, vendor support contracts, phased rollout
⚠️ Data privacy violation (sensor data contains trade secrets)
Low✓ Mitigation: Data classification policy, encryption at rest/in transit, access controls (RBAC), audit logging, data residency compliance, regular security audits
⚠️ Cloud connectivity loss during critical production
Low✓ Mitigation: Edge autonomy mode (local ML inference), critical alerts still work, 24h local buffering, automatic sync when reconnected, multi-path networking (LTE backup)
⚠️ Insufficient training data for rare failure modes
High✓ Mitigation: Synthetic data generation (physics-based simulation), transfer learning from similar machines, active learning (prioritize labeling of uncertain predictions), partnerships with OEMs for historical data
Evolution Roadmap
Progressive transformation from MVP to scale
Phase 1: MVP (0-3 months)
Phase 2: Scale (3-6 months)
Phase 3: Enterprise (6-12 months)
Complete Systems Architecture
9-layer edge-to-cloud architecture
Edge Layer
5 components
Presentation Layer
4 components
API Gateway
4 components
Agent Layer
6 components
ML Layer
5 components
Integration Layer
4 components
Data Layer
4 components
External Services
4 components
Observability
5 components
Security
5 components
Request Flow - Anomaly Detection
Automated data flow every hour
Data Flow - Sensor to Action
From sensor reading to maintenance work order in <200ms
Key Integrations
OPC-UA (Industrial Protocol)
ERP Integration (SAP)
CMMS (Maximo)
MES (Manufacturing Execution System)
Security & Compliance
Failure Modes & Recovery
| Failure | Fallback | Impact | SLA |
|---|---|---|---|
| Edge gateway offline | Local buffering (24h), auto-reconnect, alert plant manager | Degraded real-time visibility, no data loss | 99.5% gateway uptime |
| ML model inference timeout | Use cached predictions, fallback to rule-based heuristics | Reduced accuracy, no critical alerts missed | 99.9% inference availability |
| Cloud connectivity loss | Edge autonomy mode (local ML inference, local alerts) | No cloud analytics, critical alerts still work | 99.0% cloud connectivity |
| Feature store unavailable | Use cached features (1h TTL), degrade to raw sensor data | Reduced prediction accuracy, increased latency | 99.9% feature store uptime |
| False positive spike | Guardrail agent increases threshold, human review queue | Reduced alert volume, potential missed anomalies | <5% false positive rate |
| Database write failure | Buffer writes in Kafka (7 days retention), retry with backoff | Delayed analytics, no real-time impact | 99.9% write availability |
| Model drift detected | Trigger retraining pipeline, flag predictions as low-confidence | Degraded accuracy, increased human review | Detect drift within 24h, retrain within 48h |
RAG vs Fine-Tuning
Hallucination Detection
Evaluation Framework
Dataset Curation
Agentic RAG
Federated Learning
Tech Stack Summary
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